title

Data Science Course | Data Science Full Course | Data Scientist For Beginners | Intellipaat

description

ðŸ”µ Intellipaat Data Science course: https://intellipaat.com/data-scientist-course-training/
In this video on the Data Science course, you will learn what is Data Science, Types of Data Analytics, Life Cycle of Data Science, Data Visualisation, How to Create and Test a Model, Statistics for Data Science, and Probability, R programming, Machine learning, Numpy, Hands-on demo on Book Recommendation System, Skills Required to Become a Data Scientist, Trends in Data Science Science, Data Science vs Data Analytics, Data Science Interview Questions.
This is a must-watch session for everyone who wishes to learn data science and make a career in it.
#DataScienceCourse #DataScienceFullCourse #DataScientistForBeginners #DataScienceTutorialForBeginners #DataScientistCourse #DataScience #Intellipaat
00:00 - Introduction
10:15 - How machine learning works?
16:40 - Data
21:15 - Need of data science
49:39 - What is data science?
55:08 - Types of data analytics
01:01:33 - Life cycle of data science
01:22:37 - Data science process
01:29:58 - Data Cleaning
01:30:40 - Data Visualization
01:34:33 - Creating a model
01:51:02 - Converting data into useful information
02:02:02 - Descriptive Statistics
02:25:51 - Basic definitions
02:40:31 - What is probability?
02:59:45 - Variables in R
03:29:01 - Data types in R
03:56:33 - Objects in R
04:35:03 - Quiz
04:46:03 - Hands-on
05:08:01 - Dream Job
05:37:20 - Machine learning types
05:53:50 - What is regression?
06:03:00 - Types of regression
06:18:36 - Understanding linear regression
06:31:24 - Linear regression(Recap)
06:35:04 - What is logistic regression?
06:57:58 - What is classification?
07:08:58 - What is scikit learn?
07:16:18 - Steps in building a classifier
07:27:18 - What is NumPy?
07:38:17 - Initializing Numpy Array
07:54:49 - Find the data type of the array
08:19:32 - Numpy indexing and scaling
08:35:06 - Characteristics of Scipy
09:04:21 - Tasks to be performed
ðŸ”µ Read complete Data Science tutorial here: https://intellipaat.com/blog/tutorial/data-science-tutorial/
ðŸ”µ Do subscribe to Intellipaat channel & get regular updates on videos: http://bit.ly/Intellipaat
ðŸ”µ Watch Data Science tutorials here:- https://bit.ly/30QlOmv
ðŸ”µ Read the insightful blog on what is Data Science: https://intellipaat.com/blog/what-is-data-science/
ðŸ”µ Interested to know about Data Science Certification? Read this blog: https://intellipaat.com/blog/data-science-certification/
----------------------------
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{'title': 'Data Science Course | Data Science Full Course | Data Scientist For Beginners | Intellipaat', 'heatmap': [{'end': 3400.789, 'start': 3057.402, 'weight': 0.724}, {'end': 4418.641, 'start': 4075.4, 'weight': 0.726}, {'end': 5436.915, 'start': 4753.65, 'weight': 0.729}], 'summary': 'This comprehensive data science course covers various aspects, including data science insights, roles, data types, applications, digital marketing strategies, analytics domains, statistical analysis, r programming fundamentals, data manipulation, machine learning applications, regression analysis, python ml project, classification problems, numpy, scipy functions, and book recommendations, providing practical examples and insights into each topic.', 'chapters': [{'end': 962.794, 'segs': [{'end': 56.09, 'src': 'embed', 'start': 30.603, 'weight': 1, 'content': [{'end': 37.877, 'text': 'So today, I have my colleague Satish who is an expert in data science and he will answer the question that people have in their mind.', 'start': 30.603, 'duration': 7.274}, {'end': 46.141, 'text': "Hi Satish! Hi! So Satish, what's the right path to become a data scientist? That's a very good question Laksh.", 'start': 38.378, 'duration': 7.763}, {'end': 55.269, 'text': "To become a data scientist first you must need to earn a bachelor's degree or you have to complete your undergrad in any disciplines.", 'start': 47.142, 'duration': 8.127}, {'end': 56.09, 'text': 'it can be either.', 'start': 55.269, 'duration': 0.821}], 'summary': "To become a data scientist, earn a bachelor's degree or complete undergrad in any discipline.", 'duration': 25.487, 'max_score': 30.603, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k30603.jpg'}, {'end': 129.949, 'src': 'embed', 'start': 99.344, 'weight': 0, 'content': [{'end': 104.547, 'text': "So what's the scope of data science in India? Yeah, I'm more glad to answer this, Laksh.", 'start': 99.344, 'duration': 5.203}, {'end': 106.428, 'text': 'In the recent times.', 'start': 105.087, 'duration': 1.341}, {'end': 114.275, 'text': 'if you could well notice, data science is one of the most trendiest and hottest job profile, not only in India, on a global scale too.', 'start': 106.428, 'duration': 7.847}, {'end': 122.562, 'text': 'And data science promises more demanding and meaty careers for all the aspirants that includes non-IT professions as well.', 'start': 114.816, 'duration': 7.746}, {'end': 129.949, 'text': 'And in the recent years, data science had expanded its professional horizons in IT as well as non-IT industry too.', 'start': 123.143, 'duration': 6.806}], 'summary': 'Data science is a trending and demanding job in india and globally, expanding into it and non-it industries.', 'duration': 30.605, 'max_score': 99.344, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k99344.jpg'}, {'end': 412.625, 'src': 'embed', 'start': 388.658, 'weight': 2, 'content': [{'end': 397.882, 'text': "so it's always advisable to keep yourself updated in the field of data science in order to be an aspiring data scientist or to excel well in the field of data science.", 'start': 388.658, 'duration': 9.224}, {'end': 400.801, 'text': 'thanks for your advice.', 'start': 398.78, 'duration': 2.021}, {'end': 404.282, 'text': 'so what are the prerequisites to learn data science?', 'start': 400.801, 'duration': 3.481}, {'end': 412.625, 'text': "uh yeah, i feel this is one of the most important question and it is one of the primary question which is haunting every professional's mind today.", 'start': 404.282, 'duration': 8.343}], 'summary': 'Stay updated in data science. prerequisites are crucial for professionals.', 'duration': 23.967, 'max_score': 388.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k388658.jpg'}, {'end': 587.54, 'src': 'embed', 'start': 534.813, 'weight': 3, 'content': [{'end': 542.236, 'text': 'If you want to make a career in data science, then Intellipaat has IIT Madras Advanced Data Science and AI Certification Program.', 'start': 534.813, 'duration': 7.423}, {'end': 550.159, 'text': 'This course is of very high quality and cost effective as it is taught by IIT professors and industry experts.', 'start': 543.056, 'duration': 7.103}, {'end': 561.463, 'text': "Now let's go into the nitty gritties of how data science functions by understanding what is machine learning exactly.", 'start': 555, 'duration': 6.463}, {'end': 562.789, 'text': 'alright, guys.', 'start': 562.449, 'duration': 0.34}, {'end': 570.252, 'text': 'so machine learning is basically training a machine how basically a certain task has to be done.', 'start': 562.789, 'duration': 7.463}, {'end': 574.314, 'text': 'right. in the most simplest explanation, this is what it is.', 'start': 570.252, 'duration': 4.062}, {'end': 575.835, 'text': 'I will repeat it to you.', 'start': 574.314, 'duration': 1.521}, {'end': 582.418, 'text': 'so it is the science of teaching a machine how a particular task is done.', 'start': 575.835, 'duration': 6.583}, {'end': 587.54, 'text': 'right now, in the further slides, I will tell you how this process is actually implemented.', 'start': 582.418, 'duration': 5.122}], 'summary': 'Intellipaat offers iit madras advanced data science and ai certification program, taught by iit professors and industry experts, focusing on machine learning and its implementation.', 'duration': 52.727, 'max_score': 534.813, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k534813.jpg'}, {'end': 830.627, 'src': 'embed', 'start': 782.294, 'weight': 5, 'content': [{'end': 784.235, 'text': 'So you have 100, 000 plus images of fruits.', 'start': 782.294, 'duration': 1.941}, {'end': 789.457, 'text': 'Now before you begin training your system, you will have to prepare your data.', 'start': 784.795, 'duration': 4.662}, {'end': 794.179, 'text': 'In order to do that, you will first have to divide the data into a 70-40 ratio.', 'start': 789.858, 'duration': 4.321}, {'end': 797.675, 'text': 'This should be clear to everyone.', 'start': 796.474, 'duration': 1.201}, {'end': 803.159, 'text': 'Next, what you do is you start implementing machine learning.', 'start': 798.756, 'duration': 4.403}, {'end': 809.364, 'text': 'Okay Now that you have your data prepared, you go ahead and now you implement machine learning.', 'start': 803.74, 'duration': 5.624}, {'end': 812.266, 'text': "So let's go ahead and understand how that is done.", 'start': 809.784, 'duration': 2.482}, {'end': 818.871, 'text': 'Alright So guys, in the process of machine learning is basically divided into three phases.', 'start': 812.867, 'duration': 6.004}, {'end': 821.533, 'text': 'The first phase is the training phase.', 'start': 819.512, 'duration': 2.021}, {'end': 823.835, 'text': 'The second phase is the testing phase.', 'start': 821.954, 'duration': 1.881}, {'end': 826.177, 'text': 'And the third phase is the predicting phase.', 'start': 824.175, 'duration': 2.002}, {'end': 830.627, 'text': 'In the training phase, you feed the machine with the training dataset.', 'start': 826.724, 'duration': 3.903}], 'summary': 'Dataset: 100,000+ fruit images. data preparation: 70-30 ratio. machine learning: 3 phases - training, testing, predicting.', 'duration': 48.333, 'max_score': 782.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k782294.jpg'}], 'start': 8.82, 'title': 'Data science insights and roles', 'summary': 'Discusses common data science faqs, emphasizing the demand and high remuneration in the field. it also highlights the role of a data scientist in the industry, stressing the importance of continuous learning and problem-solving skills. additionally, it covers the iit madras advanced data science and ai certification program, explaining machine learning and data preparation.', 'chapters': [{'end': 278.8, 'start': 8.82, 'title': 'Data science faqs and insights', 'summary': 'Discusses the common questions about data science, including the path to becoming a data scientist, the scope of data science in india, and the responsibilities of a data scientist, emphasizing the demand and high remuneration in the field as well as the exponential growth of data science technology.', 'duration': 269.98, 'highlights': ["The Path to Becoming a Data Scientist To become a data scientist, one must earn a bachelor's degree in disciplines like IT, mathematics, or computer science, consider specialized certification in data science technology, and pursue a master's degree in data engineering or related fields.", 'The Scope of Data Science in India Data science is a highly demanding and lucrative job profile in India and globally, with abundant career opportunities and exceptional remuneration, making it a recommended technology to pursue.', 'Responsibilities of a Data Scientist Data science involves analyzing raw and unstructured data using statistical tools and concepts like R, SAS, or Python to derive meaningful insights, as the world is increasingly data-driven with a rapidly growing volume of data.']}, {'end': 534.018, 'start': 278.8, 'title': 'Role of data scientist in industry', 'summary': 'Discusses the role of a data scientist in the industry, emphasizing the importance of continuous learning and problem-solving skills, with a recommendation for aspiring data scientists to prioritize passion and interest over specific prerequisites, while highlighting the broad applicability of data science across diverse industries and the necessity of staying updated with the field.', 'duration': 255.218, 'highlights': ['The importance of continuous learning and problem-solving skills is emphasized for aspiring data scientists, irrespective of their academic or technical background, with a recommendation to stay aligned with the latest industry trends and project requirements.', 'Passion and interest are identified as the key prerequisites for aspiring data scientists, with an emphasis on the attitude to solve real-time challenges and the passion towards data analyzing skills.', 'Data science is portrayed as a field welcoming individuals from diverse cultures and backgrounds, with a broad applicability across various industries including manufacturing, product, service, e-commerce, supply chain, healthcare, retail, and media/entertainment.', 'The session covers a 360-degree view of knowledge related to data science, encompassing interview preparation and hands-on demo for learners, emphasizing a comprehensive understanding of the technology.']}, {'end': 962.794, 'start': 534.813, 'title': 'Iit madras data science program', 'summary': 'Covers the iit madras advanced data science and ai certification program, explaining machine learning, data preparation, and the three phases of machine learning, using the example of identifying fruits, and dividing data into a 70-30 ratio for training and testing.', 'duration': 427.981, 'highlights': ['The IIT Madras Advanced Data Science and AI Certification Program is a high-quality and cost-effective course taught by IIT professors and industry experts. The program is highlighted for its quality and cost-effectiveness due to being taught by IIT professors and industry experts.', 'Machine learning involves training a machine to perform specific tasks and is the science of teaching a machine how to complete a particular task. The definition and purpose of machine learning are explained, emphasizing the training aspect and its significance in task completion.', 'Data preparation for machine learning involves dividing a dataset into a 70-30 ratio for training and testing, using the example of images of fruits. The process of preparing data for machine learning is outlined, including the specific 70-30 ratio division for training and testing, illustrated with the example of images of fruits.', 'The process of machine learning is comprised of three phases: training, testing, and predicting, with the training phase involving feeding the machine with the training dataset. The three phases of machine learning are detailed, with specific emphasis on the training phase and its role in feeding the machine with the training dataset.']}], 'duration': 953.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8820.jpg', 'highlights': ['Data science is a highly demanding and lucrative job profile in India and globally, with abundant career opportunities and exceptional remuneration, making it a recommended technology to pursue.', "The Path to Becoming a Data Scientist involves earning a bachelor's degree in disciplines like IT, mathematics, or computer science, considering specialized certification in data science technology, and pursuing a master's degree in data engineering or related fields.", 'The importance of continuous learning and problem-solving skills is emphasized for aspiring data scientists, irrespective of their academic or technical background, with a recommendation to stay aligned with the latest industry trends and project requirements.', 'The IIT Madras Advanced Data Science and AI Certification Program is a high-quality and cost-effective course taught by IIT professors and industry experts, highlighted for its quality and cost-effectiveness due to being taught by IIT professors and industry experts.', 'Machine learning involves training a machine to perform specific tasks and is the science of teaching a machine how to complete a particular task, emphasizing the training aspect and its significance in task completion.', 'Data preparation for machine learning involves dividing a dataset into a 70-30 ratio for training and testing, using the example of images of fruits, outlining the specific 70-30 ratio division for training and testing, illustrated with the example of images of fruits.', 'The process of machine learning is comprised of three phases: training, testing, and predicting, with the training phase involving feeding the machine with the training dataset, detailing the three phases of machine learning, with specific emphasis on the training phase and its role in feeding the machine with the training dataset.']}, {'end': 1836.148, 'segs': [{'end': 1068.296, 'src': 'embed', 'start': 1037.52, 'weight': 6, 'content': [{'end': 1039.921, 'text': "So when we speak, that's also data.", 'start': 1037.52, 'duration': 2.401}, {'end': 1047.608, 'text': "When we consume information, let's say, when we are watching a video on youtube, that's also.", 'start': 1040.021, 'duration': 7.587}, {'end': 1049.47, 'text': "you're also generating data there.", 'start': 1047.608, 'duration': 1.862}, {'end': 1057.713, 'text': "right whenever you're interacting with, let's say, that button that helps you play that video, you're essentially doing nothing, but generating data.", 'start': 1049.47, 'duration': 8.243}, {'end': 1064.155, 'text': "right when you scroll on facebook, right when you like a post on facebook, you're not doing anything, but you're generating data.", 'start': 1057.713, 'duration': 6.442}, {'end': 1068.296, 'text': 'okay. so everything today right is essentially data.', 'start': 1064.155, 'duration': 4.141}], 'summary': 'Every action generates data, from watching videos to social media interactions.', 'duration': 30.776, 'max_score': 1037.52, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1037520.jpg'}, {'end': 1236.268, 'src': 'embed', 'start': 1212.576, 'weight': 0, 'content': [{'end': 1220.403, 'text': "it's a slight cliche, but um, the amount of data that we have generated in the last two years, okay,", 'start': 1212.576, 'duration': 7.827}, {'end': 1228.206, 'text': 'is equivalent to all the data that was being stored and generated before the last two years right in the history of mankind.', 'start': 1220.403, 'duration': 7.803}, {'end': 1236.268, 'text': "all the data, if you put together all that data that was saved, that was stored before, let's say, 2019,", 'start': 1228.206, 'duration': 8.062}], 'summary': 'Data generated in 2 years = all data before 2019', 'duration': 23.692, 'max_score': 1212.576, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1212576.jpg'}, {'end': 1337.036, 'src': 'embed', 'start': 1287.692, 'weight': 1, 'content': [{'end': 1307.336, 'text': 'and businesses are realizing that they could potentially use this data to identify insights which could help them grow their business by several times as compared to what they would have done before be without using this data.', 'start': 1287.692, 'duration': 19.644}, {'end': 1314.819, 'text': 'okay, all the businesses are realizing the power of using data for bringing about data driven decisions.', 'start': 1307.336, 'duration': 7.483}, {'end': 1323.423, 'text': 'right. data driven decision making is is is something which everyone is realizing is so important because if you, as a business,', 'start': 1314.819, 'duration': 8.604}, {'end': 1330.371, 'text': "are not using data and your competitor is, then you're essentially losing out on a competitive edge from there Right.", 'start': 1323.423, 'duration': 6.948}, {'end': 1333.633, 'text': "So that's one of the reasons why data science is so popular today.", 'start': 1330.391, 'duration': 3.242}, {'end': 1337.036, 'text': 'Right It helps you come up with a competitive edge.', 'start': 1333.814, 'duration': 3.222}], 'summary': 'Businesses can grow several times with data-driven decisions, gaining a competitive edge.', 'duration': 49.344, 'max_score': 1287.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1287692.jpg'}, {'end': 1441.238, 'src': 'embed', 'start': 1409.457, 'weight': 3, 'content': [{'end': 1416.221, 'text': "Data science today is being used and there's so so so many popular use cases of data science across so many different industries.", 'start': 1409.457, 'duration': 6.764}, {'end': 1425.806, 'text': 'Just to give you a small glimpse, let me show you a very, very interesting infographic.', 'start': 1417.221, 'duration': 8.585}, {'end': 1441.238, 'text': 'So this is an infographic which captures some of the most widely used business use cases applications of data science across different industries.', 'start': 1426.866, 'duration': 14.372}], 'summary': 'Data science is widely used across industries for various business applications.', 'duration': 31.781, 'max_score': 1409.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1409457.jpg'}, {'end': 1582.303, 'src': 'embed', 'start': 1556.241, 'weight': 4, 'content': [{'end': 1560.945, 'text': "okay, so that's what customer churn analytics right or churn prediction enables you to do right.", 'start': 1556.241, 'duration': 4.704}, {'end': 1569.432, 'text': 'it enables you to understand in advance who are the customers that are most likely to churn right, and it also enables, with their toolkit,', 'start': 1560.945, 'duration': 8.487}, {'end': 1572.214, 'text': 'to know what is the reason why this customer is leaving right.', 'start': 1569.432, 'duration': 2.782}, {'end': 1577.259, 'text': "and if you know that this is the reason why this customer is going to leave, you have, let's say,", 'start': 1572.214, 'duration': 5.045}, {'end': 1582.303, 'text': '30 days or 60 days to improve upon that parameter so that you can prevent that customer from churning.', 'start': 1577.259, 'duration': 5.044}], 'summary': 'Customer churn analytics predicts likely churn and provides time to prevent it.', 'duration': 26.062, 'max_score': 1556.241, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1556241.jpg'}, {'end': 1686.817, 'src': 'embed', 'start': 1657.469, 'weight': 5, 'content': [{'end': 1661.873, 'text': "uh, there's several other applications as well, for example cross selling and upselling.", 'start': 1657.469, 'duration': 4.404}, {'end': 1673.333, 'text': 'you can use machine learning to understand If a customer buys X, what is that next best product that you can push to the customer?', 'start': 1661.873, 'duration': 11.46}, {'end': 1677.974, 'text': "And there's a good likelihood that that customer will end up buying that product.", 'start': 1673.913, 'duration': 4.061}, {'end': 1686.817, 'text': 'That customer, when it came to you, he came to you with the intention of buying X, which would have led to $100 sale for you.', 'start': 1678.654, 'duration': 8.163}], 'summary': 'Machine learning can drive cross-selling, upselling, and increase sales by predicting next best product, leading to $100 sale.', 'duration': 29.348, 'max_score': 1657.469, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1657469.jpg'}], 'start': 962.794, 'title': 'Data types and data science applications', 'summary': 'Explores data types, emphasizing their significance for business purposes, and delves into data science, highlighting the exponential growth of unstructured data and its diverse applications across industries, including customer churn analytics, cross-selling, and upselling in marketing.', 'chapters': [{'end': 1072.718, 'start': 962.794, 'title': 'Understanding data types', 'summary': 'Explains the concept of data as a collection of facts and information, highlighting its various forms and its generation through everyday activities, emphasizing its significance for business purposes.', 'duration': 109.924, 'highlights': ['Data is essentially a collection of facts and information, which could be used for meaningful output, including various formats such as numbers, names, and others.', 'Everyday activities such as speaking, consuming information, and interacting with digital platforms generate data, emphasizing its pervasive nature in contemporary society.', 'The chapter emphasizes the significance of data for business purposes, underlining its potential for aiding better and improved learning and its role in various business activities.']}, {'end': 1836.148, 'start': 1074.54, 'title': 'Understanding data science and its applications', 'summary': 'Explains the evolution of data from structured to unstructured, emphasizing the exponential growth in unstructured data and its implications on businesses. it also highlights the importance of data science in making data-driven decisions and its diverse applications across various industries, focusing on use cases like customer churn analytics, cross-selling, and upselling in marketing.', 'duration': 761.608, 'highlights': ['The amount of data generated in the last two years is equivalent to all the data stored and generated before 2019 in the history of mankind. The exponential growth of unstructured data is emphasized by the comparison of data generated in the last two years to all data stored and generated before 2019, showcasing the immense volume and pace of data generation.', 'Data science helps businesses identify insights that could help them grow by several times compared to what they would have done without using data. Data science is crucial for businesses as it enables the identification of insights that can significantly enhance business growth, demonstrating the tangible benefits of leveraging data science for decision-making.', 'Data-driven decision making is essential for gaining a competitive edge in business, emphasizing the significance of using data for strategic advantage. The importance of data-driven decision making is underscored, highlighting its role in creating a competitive edge for businesses and the potential consequences of not utilizing data for decision-making.', 'Data science has diverse applications across various industries, including marketing, manufacturing, insurance, banking, and healthcare, providing a wide range of use cases. The versatility of data science is highlighted, showcasing its applicability across diverse industries and the multitude of use cases, emphasizing its broad impact and relevance beyond specific domains.', 'Customer churn analytics in marketing enables businesses to predict and prevent customer churn, leading to improved retention and enhanced customer satisfaction. The use case of customer churn analytics in marketing is elucidated, demonstrating its ability to predict and prevent customer churn, ultimately contributing to improved customer retention and satisfaction, with potential quantifiable impact on customer retention rates.', 'Cross-selling and upselling in marketing leverage machine learning to predict customer behavior, enabling businesses to increase sales and revenue through targeted recommendations. The applications of cross-selling and upselling in marketing are explained, emphasizing their utilization of machine learning to predict customer behavior and drive targeted recommendations, showcasing the potential for increased sales and revenue through personalized offerings.']}], 'duration': 873.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k962794.jpg', 'highlights': ['The exponential growth of unstructured data is emphasized by the comparison of data generated in the last two years to all data stored and generated before 2019, showcasing the immense volume and pace of data generation.', 'Data science helps businesses identify insights that could help them grow by several times compared to what they would have done without using data.', 'Data-driven decision making is essential for gaining a competitive edge in business, emphasizing the significance of using data for strategic advantage.', 'Data science has diverse applications across various industries, including marketing, manufacturing, insurance, banking, and healthcare, providing a wide range of use cases.', 'Customer churn analytics in marketing enables businesses to predict and prevent customer churn, leading to improved retention and enhanced customer satisfaction.', 'Cross-selling and upselling in marketing leverage machine learning to predict customer behavior, enabling businesses to increase sales and revenue through targeted recommendations.', 'Everyday activities such as speaking, consuming information, and interacting with digital platforms generate data, emphasizing its pervasive nature in contemporary society.', 'The chapter emphasizes the significance of data for business purposes, underlining its potential for aiding better and improved learning and its role in various business activities.', 'The amount of data generated in the last two years is equivalent to all the data stored and generated before 2019 in the history of mankind.']}, {'end': 3355.62, 'segs': [{'end': 1885.332, 'src': 'embed', 'start': 1836.148, 'weight': 1, 'content': [{'end': 1844.583, 'text': "Now let's continue with the session, OK, so basically providing me an upgraded service at some additional cost, which is, of course,", 'start': 1836.148, 'duration': 8.435}, {'end': 1848.404, 'text': 'a profit generating source for the organization.', 'start': 1844.583, 'duration': 3.821}, {'end': 1850.945, 'text': 'Right Revenue generating source for the organization.', 'start': 1848.504, 'duration': 2.441}, {'end': 1852.906, 'text': "Right So that's cross selling.", 'start': 1851.965, 'duration': 0.941}, {'end': 1853.506, 'text': "That's upselling.", 'start': 1852.926, 'duration': 0.58}, {'end': 1857.487, 'text': 'Digital marketing is very, very popular in the market right now.', 'start': 1854.246, 'duration': 3.241}, {'end': 1861.968, 'text': 'Right You use ads that you use because everyone is on social media.', 'start': 1857.547, 'duration': 4.421}, {'end': 1872.869, 'text': "Right. Everyone, let's say, everyone uses Instagram, everyone uses Facebook, everyone's uses YouTube as well.", 'start': 1862.928, 'duration': 9.941}, {'end': 1879.451, 'text': 'And I do push ads on this platform so that and you do this in a very, very targeted manner, right?', 'start': 1872.869, 'duration': 6.582}, {'end': 1885.332, 'text': "You understand what are the demographics that you need to target to, let's say, promote your business, right?", 'start': 1879.991, 'duration': 5.341}], 'summary': 'Cross-selling and upselling through targeted digital marketing on popular platforms is a profitable strategy for revenue generation.', 'duration': 49.184, 'max_score': 1836.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1836148.jpg'}, {'end': 1937.501, 'src': 'embed', 'start': 1906.398, 'weight': 2, 'content': [{'end': 1921.382, 'text': 'Digital marketing is very popular today because of the absolute scale, absolute volume of audience which is available on these social media platforms.', 'start': 1906.398, 'duration': 14.984}, {'end': 1925.704, 'text': 'Digital marketing sentiment analysis.', 'start': 1922.903, 'duration': 2.801}, {'end': 1933.377, 'text': "again, it's a very, very popular advanced analytics use case where you use social media reviews, social media comments, etc.", 'start': 1925.704, 'duration': 7.673}, {'end': 1937.501, 'text': 'to understand the perception of customers about a particular brand.', 'start': 1934.118, 'duration': 3.383}], 'summary': 'Digital marketing leverages social media for sentiment analysis and customer perception.', 'duration': 31.103, 'max_score': 1906.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1906398.jpg'}, {'end': 2067.043, 'src': 'embed', 'start': 2035.174, 'weight': 3, 'content': [{'end': 2042.698, 'text': 'Clinical trials, by the way, is one very popular use case of data science in the healthcare industry.', 'start': 2035.174, 'duration': 7.524}, {'end': 2051.943, 'text': "You can use statistics, you can use data science to predict the effectiveness of, let's say, a vaccine.", 'start': 2044.279, 'duration': 7.664}, {'end': 2054.685, 'text': 'Because of the recency of it.', 'start': 2052.884, 'duration': 1.801}, {'end': 2064.73, 'text': "I can say that let's say when India was coming or when any healthcare firm was to generate, was basically building a vaccine.", 'start': 2054.685, 'duration': 10.045}, {'end': 2067.043, 'text': 'They have done clinical trials.', 'start': 2065.543, 'duration': 1.5}], 'summary': 'Clinical trials utilize data science to predict vaccine effectiveness, aiding in healthcare industry advancements.', 'duration': 31.869, 'max_score': 2035.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k2035174.jpg'}, {'end': 2321.832, 'src': 'embed', 'start': 2294.999, 'weight': 4, 'content': [{'end': 2302.564, 'text': "then it's a huge loss for the credit card organization because it has to repay its customer on any fraud that has happened to them.", 'start': 2294.999, 'duration': 7.565}, {'end': 2307.728, 'text': 'So for that reason, what credit card companies do is they basically make use of historical data.', 'start': 2303.185, 'duration': 4.543}, {'end': 2314.894, 'text': 'They make use of machine learning to identify trades of fraudulent transactions and trades of non-fraudulent transactions.', 'start': 2308.909, 'duration': 5.985}, {'end': 2321.832, 'text': "conceptualize that in a, in a, in an algorithm such as, let's say, linear regression, logistic regression, etc.", 'start': 2315.851, 'duration': 5.981}], 'summary': 'Credit card companies use machine learning to detect fraudulent transactions and minimize losses.', 'duration': 26.833, 'max_score': 2294.999, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k2294999.jpg'}, {'end': 2402.98, 'src': 'embed', 'start': 2354.989, 'weight': 0, 'content': [{'end': 2363.395, 'text': "Okay So a lot of different use cases across healthcare, across sales, across travel, right? Everywhere there's data science.", 'start': 2354.989, 'duration': 8.406}, {'end': 2365.316, 'text': 'And as I said, this is not exhaustive.', 'start': 2363.435, 'duration': 1.881}, {'end': 2371.26, 'text': "There's so many other applications which you've not even touched upon in this infographic, right?", 'start': 2366.237, 'duration': 5.023}, {'end': 2377.368, 'text': "So, because there's so much that you could do, potentially using data, using data science.", 'start': 2371.28, 'duration': 6.088}, {'end': 2379.068, 'text': "That's why data science is so popular.", 'start': 2377.648, 'duration': 1.42}, {'end': 2381.87, 'text': "That's why data science is almost indispensable in today's world.", 'start': 2379.088, 'duration': 2.782}, {'end': 2388.233, 'text': 'OK, so why data science is important? Because it helps you in understanding your data.', 'start': 2382.83, 'duration': 5.403}, {'end': 2392.355, 'text': 'Right It helps you in identifying interesting insights.', 'start': 2388.913, 'duration': 3.442}, {'end': 2399.238, 'text': 'Insight is nothing but an information, a finding which is actionable for the business.', 'start': 2393.255, 'duration': 5.983}, {'end': 2402.98, 'text': "And it's basically it basically can be used.", 'start': 2399.698, 'duration': 3.282}], 'summary': 'Data science is indispensable, with applications in healthcare, sales, and travel, providing actionable insights for businesses.', 'duration': 47.991, 'max_score': 2354.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k2354989.jpg'}, {'end': 2474.898, 'src': 'embed', 'start': 2447.22, 'weight': 7, 'content': [{'end': 2452.903, 'text': 'so, to start with, in the most easiest manner, if i have to define data science, data science is nothing.', 'start': 2447.22, 'duration': 5.683}, {'end': 2460.247, 'text': "okay, data science is nothing, but it's the science, okay, of applying mathematics to data.", 'start': 2452.903, 'duration': 7.344}, {'end': 2466.735, 'text': "okay, it's the science of applying mathematics to data so that you can make the data talk to you.", 'start': 2460.247, 'duration': 6.488}, {'end': 2474.898, 'text': 'okay, when i say you can make the data talk to you, it means that you enable interpretability of raw data.', 'start': 2466.735, 'duration': 8.163}], 'summary': 'Data science is the application of mathematics to enable interpretability of raw data.', 'duration': 27.678, 'max_score': 2447.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k2447220.jpg'}, {'end': 2649.686, 'src': 'embed', 'start': 2613.696, 'weight': 8, 'content': [{'end': 2620.638, 'text': 'one sentence definition which could encompass everything that data science is comprising of.', 'start': 2613.696, 'duration': 6.942}, {'end': 2627.419, 'text': "So data science is nothing, but it's essentially a blend of various tools,", 'start': 2622.078, 'duration': 5.341}, {'end': 2636.061, 'text': 'algorithms and different machine learning principles with the goal to discover hidden patterns from raw data.', 'start': 2627.419, 'duration': 8.642}, {'end': 2649.686, 'text': 'The ultimate goal here is that you generate insights to generate patterns which were otherwise, uh, not visible to our hidden our naked eyes.', 'start': 2637.161, 'duration': 12.525}], 'summary': 'Data science is the blend of tools and algorithms with the goal to discover hidden patterns from raw data.', 'duration': 35.99, 'max_score': 2613.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k2613696.jpg'}, {'end': 2951.984, 'src': 'embed', 'start': 2924.766, 'weight': 9, 'content': [{'end': 2933.534, 'text': 'right, and using that equation they will try and try and predict the sales of this product in the future.', 'start': 2924.766, 'duration': 8.768}, {'end': 2935.559, 'text': "okay, So that's what a data scientist does.", 'start': 2933.534, 'duration': 2.025}, {'end': 2939.14, 'text': "A data scientist doesn't just explain what has happened in the past.", 'start': 2935.639, 'duration': 3.501}, {'end': 2941.141, 'text': 'rather they build algorithms.', 'start': 2939.14, 'duration': 2.001}, {'end': 2946.663, 'text': 'they use machine learning algorithms to make predictions in the future as well.', 'start': 2941.141, 'duration': 5.522}, {'end': 2951.984, 'text': 'And we will learn this better in the next slide.', 'start': 2949.143, 'duration': 2.841}], 'summary': 'Data scientists use equations and machine learning to predict future product sales.', 'duration': 27.218, 'max_score': 2924.766, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k2924766.jpg'}], 'start': 1836.148, 'title': 'Data science and digital marketing strategies', 'summary': 'Covers digital marketing strategies such as cross-selling, upselling, targeted advertising, sentiment analysis, and the application of data science in healthcare, fraud detection, and various industries, emphasizing predictive capabilities and practical applications.', 'chapters': [{'end': 2003.863, 'start': 1836.148, 'title': 'Digital marketing strategies and sentiment analysis', 'summary': 'Discusses cross-selling, upselling, and digital marketing strategies including targeted advertising on social media platforms, and sentiment analysis to understand customer perception and improve brand reputation.', 'duration': 167.715, 'highlights': ['Digital marketing strategies involve targeted advertising on social media platforms like Instagram, Facebook, and YouTube to reach a wide audience. This highlights the use of popular social media platforms for targeted advertising, reaching a wide audience.', 'Sentiment analysis in digital marketing uses social media reviews to understand customer perception, identify positive and negative sentiments, and improve brand reputation. This highlights the use of sentiment analysis to understand customer perceptions, identify positive and negative sentiments, and improve brand reputation.', 'Cross-selling and upselling are profit-generating sources for organizations by providing upgraded services at additional costs. This highlights the profit-generating sources for organizations through cross-selling and upselling upgraded services.']}, {'end': 2353.668, 'start': 2003.863, 'title': 'Data science in healthcare and fraud detection', 'summary': 'Discusses the application of data science in healthcare, specifically in clinical trials for vaccine effectiveness, and the use of machine learning in fraud detection for credit card transactions to prevent potential losses.', 'duration': 349.805, 'highlights': ['Clinical trials for vaccine effectiveness involve using statistics and data science to ensure the sample is representative of the entire population, reducing the risk of ineffectiveness or adverse effects. Clinical trials for vaccine effectiveness involve using statistics and data science to ensure the sample is representative of the entire population, reducing the risk of ineffectiveness or adverse effects.', 'Fraud detection model for credit card transactions uses historical data and machine learning to identify fraudulent transactions, thereby preventing potential losses for credit card companies. Fraud detection model for credit card transactions uses historical data and machine learning to identify fraudulent transactions, thereby preventing potential losses for credit card companies.', 'Machine learning is utilized in fraud detection for credit card transactions to predict potential fraudulent activities, minimizing the impact of fraudulent transactions on credit card organizations and customers. Machine learning is utilized in fraud detection for credit card transactions to predict potential fraudulent activities, minimizing the impact of fraudulent transactions on credit card organizations and customers.']}, {'end': 2756.286, 'start': 2354.989, 'title': 'Data science: importance and applications', 'summary': 'Discusses the importance of data science in various industries, its definition, and how it differs from other analytics roles, emphasizing its role in uncovering hidden patterns from raw data and its predictive capabilities.', 'duration': 401.297, 'highlights': ["Data science is popular and indispensable due to its versatility across industries, including healthcare, sales, and travel. Data science has various use cases across healthcare, sales, and travel, making it indispensable in today's world.", 'Data science is important for understanding data, identifying actionable insights, and making informed decisions for business improvement. Data science helps in understanding data, identifying actionable insights, and making informed decisions for business improvement.', 'Data science is the science of applying mathematics to data, enabling interpretability of raw data and generating hidden patterns and insights. Data science is the application of mathematics to data, enabling interpretability of raw data and generating hidden patterns and insights.', 'Data science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from raw data. Data science is a blend of tools, algorithms, and machine learning principles to discover hidden patterns from raw data.', 'Data scientists differ from other analytics roles in their focus on predicting future events using advanced machine learning algorithms. Data scientists differ from other analytics roles by using advanced machine learning algorithms to predict future events.']}, {'end': 3355.62, 'start': 2756.886, 'title': 'Role of data scientist', 'summary': 'Explains the role of a data scientist in analyzing past data to make future predictions, distinguishing between the tasks of data analyst and data scientist, and outlining the domains of data analytics.', 'duration': 598.734, 'highlights': ['Data scientist uses past data to predict future events by correlating with independent variables and building machine learning algorithms. Data scientists analyze past data to predict future events by correlating with independent variables and building machine learning algorithms, enabling informed decision-making.', 'Data analyst and business intelligence officers focus on analyzing and describing past data to find hindsight and insight. Data analyst and business intelligence officers analyze and describe past data to find hindsight and insight, enabling understanding of historical trends and events.', 'Descriptive analytics focuses on describing historical events, while diagnostic analytics involves deep diving into specific past events to understand their root causes. Descriptive analytics focuses on describing historical events, while diagnostic analytics involves deep diving into specific past events to understand their root causes, enabling validation of hypotheses and understanding of trends.']}], 'duration': 1519.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k1836148.jpg', 'highlights': ["Data science has various use cases across healthcare, sales, and travel, making it indispensable in today's world.", 'Digital marketing strategies involve targeted advertising on social media platforms like Instagram, Facebook, and YouTube to reach a wide audience.', 'Sentiment analysis in digital marketing uses social media reviews to understand customer perception, identify positive and negative sentiments, and improve brand reputation.', 'Clinical trials for vaccine effectiveness involve using statistics and data science to ensure the sample is representative of the entire population, reducing the risk of ineffectiveness or adverse effects.', 'Fraud detection model for credit card transactions uses historical data and machine learning to identify fraudulent transactions, thereby preventing potential losses for credit card companies.', 'Cross-selling and upselling are profit-generating sources for organizations by providing upgraded services at additional costs.', 'Data science is important for understanding data, identifying actionable insights, and making informed decisions for business improvement.', 'Data science is the science of applying mathematics to data, enabling interpretability of raw data and generating hidden patterns and insights.', 'Data science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from raw data.', 'Data scientists differ from other analytics roles by using advanced machine learning algorithms to predict future events.']}, {'end': 4726.65, 'segs': [{'end': 3378.704, 'src': 'embed', 'start': 3355.64, 'weight': 2, 'content': [{'end': 3364.199, 'text': 'So a lot of different hypothesis and then you decide to do an analysis to either prove or disprove each of this hypothesis.', 'start': 3355.64, 'duration': 8.559}, {'end': 3373.923, 'text': "that is something that you do when we talk about this, when we talk about diagnostic analytics, okay, so that's diagnostic analytics.", 'start': 3364.199, 'duration': 9.724}, {'end': 3378.704, 'text': 'diagnostic is analytics is something where business analysts and data analysts are concentrated like.', 'start': 3373.923, 'duration': 4.781}], 'summary': 'Diagnostic analytics involves analyzing hypotheses to prove or disprove them.', 'duration': 23.064, 'max_score': 3355.64, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3355640.jpg'}, {'end': 3428.321, 'src': 'embed', 'start': 3401.209, 'weight': 1, 'content': [{'end': 3407.974, 'text': "Predictive analytics as you would know and I think I've been speaking about it over the last few minutes as well.", 'start': 3401.209, 'duration': 6.765}, {'end': 3417.395, 'text': "It's basically a technique where you use historical data You use historical data, feed it to a machine.", 'start': 3408.654, 'duration': 8.741}, {'end': 3428.321, 'text': 'You let the machine use its intelligence to derive relationships between the dependent variable and various independent variables.', 'start': 3419.196, 'duration': 9.125}], 'summary': 'Predictive analytics uses historical data to derive relationships between variables.', 'duration': 27.112, 'max_score': 3401.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3401209.jpg'}, {'end': 3538.43, 'src': 'embed', 'start': 3510.809, 'weight': 0, 'content': [{'end': 3521.935, 'text': 'okay, so prescriptive analytics is the fourth domain of analytics where, instead of predicting outcomes right, you use concepts of a diagnostic,', 'start': 3510.809, 'duration': 11.126}, {'end': 3526.978, 'text': 'descriptive and productive analytics to prescribe outcomes, to come up with recommendations.', 'start': 3521.935, 'duration': 5.043}, {'end': 3537.209, 'text': 'okay, a very good example of this is recommendation engine right, wherein you use machine learning not to, uh, influence a business,', 'start': 3528.422, 'duration': 8.787}, {'end': 3538.43, 'text': 'influence an insight.', 'start': 3537.209, 'duration': 1.221}], 'summary': 'Prescriptive analytics uses diagnostic, descriptive, and predictive analytics to prescribe outcomes and make recommendations.', 'duration': 27.621, 'max_score': 3510.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3510809.jpg'}, {'end': 3605.046, 'src': 'embed', 'start': 3579.805, 'weight': 3, 'content': [{'end': 3585.948, 'text': 'Okay?. So our data science primarily works on predictive and prescriptive analytics, right?', 'start': 3579.805, 'duration': 6.143}, {'end': 3592.58, 'text': "And let's say, 20 to 25 percent of their time also goes in performing descriptive analytics and diagnostic analytics.", 'start': 3586.228, 'duration': 6.352}, {'end': 3594.481, 'text': 'right in the same manner.', 'start': 3592.58, 'duration': 1.901}, {'end': 3598.443, 'text': 'in the same manner, a data analyst would be primarily focused on diagnostic analytics.', 'start': 3594.481, 'duration': 3.962}, {'end': 3605.046, 'text': 'right, this is where a data analyst 80 percent time, 80 percent time of data analysts would be concentrated.', 'start': 3598.443, 'duration': 6.603}], 'summary': 'Data science focuses on predictive and prescriptive analytics, with 20-25% time on descriptive and diagnostic analytics. data analysts primarily focus on diagnostic analytics, with 80% of their time concentrated there.', 'duration': 25.241, 'max_score': 3579.805, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3579805.jpg'}, {'end': 3870.594, 'src': 'embed', 'start': 3839.537, 'weight': 4, 'content': [{'end': 3851.846, 'text': 'okay, so see, uh, every data science engagement, every data science project basically goes through these five stages And, as I said, when I say every,', 'start': 3839.537, 'duration': 12.309}, {'end': 3855.107, 'text': "I'm talking about, let's say, 85 to 90% engagement.", 'start': 3851.846, 'duration': 3.261}, {'end': 3862.19, 'text': 'The first stage of data science is called data acquisition.', 'start': 3856.348, 'duration': 5.842}, {'end': 3863.491, 'text': "It's called data acquisition.", 'start': 3862.41, 'duration': 1.081}, {'end': 3870.594, 'text': 'Now as about extracting data from various data sources.', 'start': 3864.391, 'duration': 6.203}], 'summary': 'Data science projects go through 5 stages, with 85-90% engagement, starting with data acquisition.', 'duration': 31.057, 'max_score': 3839.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3839537.jpg'}, {'end': 3975.343, 'src': 'embed', 'start': 3923.864, 'weight': 5, 'content': [{'end': 3934.132, 'text': "you're basically trying to scope your business problem in the sense that you're trying to identify all the possible parameters that could have an impact on the problem that you're trying to solve.", 'start': 3923.864, 'duration': 10.268}, {'end': 3937.675, 'text': "right, let's say, the problem that you're trying to solve is customer churn.", 'start': 3934.132, 'duration': 3.543}, {'end': 3946.342, 'text': "okay, you're trying to solve, uh, you're trying to build a model right which could predict in advance whether a customer is likely to churn or not.", 'start': 3937.675, 'duration': 8.667}, {'end': 3956.3, 'text': 'okay, now, even before you start collecting data, it is important for you to do this step right, because only if you know what are the pat,', 'start': 3946.342, 'duration': 9.958}, {'end': 3962.781, 'text': 'what are the parameters that could have an impact on your, on your final output, which is data which is customer churn,', 'start': 3956.3, 'duration': 6.481}, {'end': 3975.343, 'text': "unless you know that you can't really start collecting data right i mean unless you know what variables will will lead to a data chain or could lead to data could lead to customer churn what data would you collect right?", 'start': 3962.781, 'duration': 12.562}], 'summary': 'Identify parameters impacting customer churn before collecting data for predictive modeling.', 'duration': 51.479, 'max_score': 3923.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3923864.jpg'}, {'end': 4418.641, 'src': 'heatmap', 'start': 4075.4, 'weight': 0.726, 'content': [{'end': 4079.923, 'text': "you're facing problems and hence you decide to move out right.", 'start': 4075.4, 'duration': 4.523}, {'end': 4084.326, 'text': "so the kind of internet service that you're using, okay.", 'start': 4079.923, 'duration': 4.403}, {'end': 4089.33, 'text': 'then there could be many other reasons as well, right, there could be reasons like um, uh,', 'start': 4084.326, 'duration': 5.004}, {'end': 4095.87, 'text': 'like the customer has been trying to reach out to the customer service department but he did not get a satisfactory response.', 'start': 4089.33, 'duration': 6.54}, {'end': 4101.273, 'text': "right. so customer service, a poor customer experience, let's say okay.", 'start': 4095.87, 'duration': 5.403}, {'end': 4105.776, 'text': 'and similarly there could be like, when you build a model right, you would do a detailed problem solving,', 'start': 4101.273, 'duration': 4.503}, {'end': 4110.238, 'text': 'to understand what are the various parameters that could lead to customer churn right.', 'start': 4105.776, 'duration': 4.462}, {'end': 4112.019, 'text': "and now let's say these are the four.", 'start': 4110.238, 'duration': 1.781}, {'end': 4119.283, 'text': 'and similarly, you will build a map that will, when try and cap it will be, you will try to build a very, very exhaustive sort,', 'start': 4112.019, 'duration': 7.264}, {'end': 4129.584, 'text': 'of a sort of a uh like factor map right, which will capture most or all of the factors that could lead to customer chat right.', 'start': 4119.283, 'duration': 10.301}, {'end': 4135.066, 'text': 'and now that you know that you basically these are all the parameters that could lead to customer,', 'start': 4129.584, 'duration': 5.482}, {'end': 4137.586, 'text': 'now you could go about doing your data acquisition right now.', 'start': 4135.066, 'duration': 2.52}, {'end': 4143.188, 'text': 'you know that i need to get data which is which is about the transactional behavior of customers.', 'start': 4137.586, 'duration': 5.602}, {'end': 4147.069, 'text': 'so i need to get transactional data which will tell me the cost of the of the data services.', 'start': 4143.188, 'duration': 3.881}, {'end': 4150.582, 'text': 'i need to get data with respect to the demographics of my customers.', 'start': 4148.001, 'duration': 2.581}, {'end': 4151.903, 'text': 'i need to know where they are located.', 'start': 4150.582, 'duration': 1.321}, {'end': 4153.884, 'text': 'i need to know what is the age group.', 'start': 4151.903, 'duration': 1.981}, {'end': 4155.745, 'text': 'i need to know whether they are male or female.', 'start': 4153.884, 'duration': 1.861}, {'end': 4161.108, 'text': "i need to know what, like how many people they have in their home right so that's demographic.", 'start': 4155.745, 'duration': 5.363}, {'end': 4164.349, 'text': "third is, i need to know what is the kind of internet service that they're using right.", 'start': 4161.108, 'duration': 3.241}, {'end': 4172.073, 'text': 'so i need to you, i need to dig into the data which captures the the the internet, like the, the latest snapshot of the kind,', 'start': 4164.349, 'duration': 7.724}, {'end': 4175.635, 'text': 'the services that the user is subscribed to right.', 'start': 4172.073, 'duration': 3.562}, {'end': 4185.122, 'text': 'then i need to understand whether the customer has been satisfied with with the kind of services, the customer service experience of the customer.', 'start': 4175.635, 'duration': 9.487}, {'end': 4188.984, 'text': 'so i will basically log into the the call data right the call center data,', 'start': 4185.122, 'duration': 3.862}, {'end': 4194.667, 'text': 'and try to find out how many calls the customer has done and what was the average handle time of those requests.', 'start': 4188.984, 'duration': 5.683}, {'end': 4200.789, 'text': "or let's say, uh, whether the customer had a had a good experience or not, right, something of that sort.", 'start': 4194.667, 'duration': 6.122}, {'end': 4202.73, 'text': 'so this would essentially.', 'start': 4200.789, 'duration': 1.941}, {'end': 4211.047, 'text': 'this coping would essentially help me identify what are the possible data sources that i need to then collect data from right and when you do this.', 'start': 4202.73, 'duration': 8.317}, {'end': 4215.929, 'text': "when you do this, that's when this step would come, which is called data acquisition, right.", 'start': 4211.047, 'duration': 4.882}, {'end': 4218.449, 'text': 'so the first step is scoping the problem.', 'start': 4215.929, 'duration': 2.52}, {'end': 4226.351, 'text': 'the first step is understanding what data to collect and then going about collecting that data right from various databases, right.', 'start': 4218.449, 'duration': 7.902}, {'end': 4230.252, 'text': 'so it is possible that not all of this data is available right.', 'start': 4226.351, 'duration': 3.901}, {'end': 4234.633, 'text': 'it is possible that not all of this data is available at the same in the same database.', 'start': 4230.252, 'duration': 4.381}, {'end': 4238.142, 'text': 'You might have to go to different databases to capture all of this data.', 'start': 4235.481, 'duration': 2.661}, {'end': 4242.224, 'text': 'So that is something that comes under data acquisition.', 'start': 4239.363, 'duration': 2.861}, {'end': 4243.004, 'text': 'I hope this is clear.', 'start': 4242.264, 'duration': 0.74}, {'end': 4248.346, 'text': 'Now, the second step is data pre-processing.', 'start': 4244.344, 'duration': 4.002}, {'end': 4259.911, 'text': "What data pre-processing means is that when you collected data from various sources, let's say you collected data from 10 different sources.", 'start': 4248.846, 'duration': 11.065}, {'end': 4267.332, 'text': 'You have transactional data, Then you have customer service data.', 'start': 4259.951, 'duration': 7.381}, {'end': 4271.654, 'text': 'Customer service, please pardon my handwriting here.', 'start': 4267.692, 'duration': 3.962}, {'end': 4275.976, 'text': "Then you have data from, let's say, demographics.", 'start': 4272.874, 'duration': 3.102}, {'end': 4280.758, 'text': "And then you could have, let's say, four other data tables as well.", 'start': 4277.116, 'duration': 3.642}, {'end': 4287.221, 'text': "Now, what you will have to do is you'll have to merge all of this information together in a singular composite database.", 'start': 4281.278, 'duration': 5.943}, {'end': 4288.301, 'text': "You'll have to create a data model.", 'start': 4287.241, 'duration': 1.06}, {'end': 4289.582, 'text': "You'll have to create a table.", 'start': 4288.601, 'duration': 0.981}, {'end': 4297.136, 'text': 'And that table could be in R, it could be Python, it could be in any language.', 'start': 4293.735, 'duration': 3.401}, {'end': 4303.738, 'text': "You'll basically have to consolidate all this data together to create one composite database.", 'start': 4297.736, 'duration': 6.002}, {'end': 4313.542, 'text': 'You cannot let data be there across different data tables or data sources because a machine learning model can only work on one database at a time.', 'start': 4304.679, 'duration': 8.863}, {'end': 4316.863, 'text': 'So you will have to bring all of this information together.', 'start': 4314.702, 'duration': 2.161}, {'end': 4323.125, 'text': 'You will have to create one composite data set that comes under data processing, which is joining data from various data sources.', 'start': 4316.903, 'duration': 6.222}, {'end': 4328.09, 'text': 'Secondly, what you will have to do is you will have to improve the quality of your data.', 'start': 4323.829, 'duration': 4.261}, {'end': 4336.312, 'text': "Which means it is possible that you have transactional data for all your customers, but you do not have, let's say,", 'start': 4328.11, 'duration': 8.202}, {'end': 4338.472, 'text': 'demographics of 20% of your customers.', 'start': 4336.312, 'duration': 2.16}, {'end': 4344.594, 'text': "If you don't have demographics for 20% of your customers, it means you will have to reject their transactional data as well.", 'start': 4339.572, 'duration': 5.022}, {'end': 4350.955, 'text': "So when you're creating a composite database, you will have to make sure that the quality of your data is good.", 'start': 4345.914, 'duration': 5.041}, {'end': 4353.194, 'text': 'and the data is complete right.', 'start': 4351.753, 'duration': 1.441}, {'end': 4358.276, 'text': 'wherever there are variables which are missing for a particular customer,', 'start': 4353.194, 'duration': 5.082}, {'end': 4366.96, 'text': 'you will have to either impute that data with the most accurate interpretation or you will have to reject that data right.', 'start': 4358.276, 'duration': 8.684}, {'end': 4381.012, 'text': 'so basically, uh, joining data through various databases, aggregating data, summarizing data, transposing data, transforming data, cleaning of data,', 'start': 4366.96, 'duration': 14.052}, {'end': 4386.895, 'text': 'missing value treatment, outlier treatment all of that happens in data pre-processing.', 'start': 4381.012, 'duration': 5.883}, {'end': 4393.837, 'text': "Basically what you're doing is you're preparing your data so that it could be used for mathematical modeling.", 'start': 4386.915, 'duration': 6.922}, {'end': 4398.419, 'text': 'So this is what you do in the second step.', 'start': 4396.318, 'duration': 2.101}, {'end': 4400.84, 'text': 'Now the third step is called model building.', 'start': 4398.759, 'duration': 2.081}, {'end': 4405.902, 'text': 'What this step basically includes.', 'start': 4402.12, 'duration': 3.782}, {'end': 4408.663, 'text': 'this step is when you,', 'start': 4405.902, 'duration': 2.761}, {'end': 4418.641, 'text': 'this step is basically what you will do is you will determine the methods and techniques to draw the relationship between variables.', 'start': 4408.663, 'duration': 9.978}], 'summary': 'Identifying customer churn factors and data acquisition for problem scoping and data pre-processing before model building.', 'duration': 343.241, 'max_score': 4075.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4075400.jpg'}, {'end': 4386.895, 'src': 'embed', 'start': 4358.276, 'weight': 9, 'content': [{'end': 4366.96, 'text': 'you will have to either impute that data with the most accurate interpretation or you will have to reject that data right.', 'start': 4358.276, 'duration': 8.684}, {'end': 4381.012, 'text': 'so basically, uh, joining data through various databases, aggregating data, summarizing data, transposing data, transforming data, cleaning of data,', 'start': 4366.96, 'duration': 14.052}, {'end': 4386.895, 'text': 'missing value treatment, outlier treatment all of that happens in data pre-processing.', 'start': 4381.012, 'duration': 5.883}], 'summary': 'Data preprocessing involves imputing, joining, aggregating, summarizing, transposing, transforming, cleaning, and treating missing values and outliers.', 'duration': 28.619, 'max_score': 4358.276, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4358276.jpg'}, {'end': 4553.05, 'src': 'embed', 'start': 4524.499, 'weight': 10, 'content': [{'end': 4526.1, 'text': 'i try to choose that methodology.', 'start': 4524.499, 'duration': 1.601}, {'end': 4527.22, 'text': 'five really right.', 'start': 4526.1, 'duration': 1.12}, {'end': 4536.885, 'text': 'so model building is, when you use the data model, test out, iterate through various techniques right and then evaluate right,', 'start': 4527.22, 'duration': 9.665}, {'end': 4541.588, 'text': 'which technique gives you the best output, the best, most accurate solution.', 'start': 4536.885, 'duration': 4.703}, {'end': 4544.209, 'text': "that's the technique that you use and build your final model on.", 'start': 4541.588, 'duration': 2.621}, {'end': 4547.189, 'text': "okay, so that's all of this comes under model building.", 'start': 4544.588, 'duration': 2.601}, {'end': 4549.049, 'text': "okay, now, what you've done is okay.", 'start': 4547.189, 'duration': 1.86}, {'end': 4553.05, 'text': "so far, what you've done is you basically collected data through data acquisition.", 'start': 4549.049, 'duration': 4.001}], 'summary': 'Using model building to iterate through techniques and choose the most accurate solution for data modeling and acquisition.', 'duration': 28.551, 'max_score': 4524.499, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4524499.jpg'}, {'end': 4612.587, 'src': 'embed', 'start': 4582.771, 'weight': 11, 'content': [{'end': 4586.353, 'text': 'okay, it will tell you that this customer is most likely to turn.', 'start': 4582.771, 'duration': 3.582}, {'end': 4589.275, 'text': 'this customer is least likely to turn right.', 'start': 4586.353, 'duration': 2.922}, {'end': 4590.555, 'text': 'but where is the insight here?', 'start': 4589.275, 'duration': 1.28}, {'end': 4596.199, 'text': 'the insight is when you try to evaluate the pattern associated with your predictions,', 'start': 4590.555, 'duration': 5.644}, {'end': 4603.279, 'text': 'to understand what are the reasons for customer chance and what are the reasons for customer retention.', 'start': 4596.199, 'duration': 7.08}, {'end': 4604.6, 'text': 'what leads to customer churn?', 'start': 4603.279, 'duration': 1.321}, {'end': 4606.182, 'text': 'what leads to customer retention?', 'start': 4604.6, 'duration': 1.582}, {'end': 4612.587, 'text': 'right, unless you do that, your business will not have enough insight, right.', 'start': 4606.182, 'duration': 6.405}], 'summary': 'Identify customer churn patterns for better business insights.', 'duration': 29.816, 'max_score': 4582.771, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4582771.jpg'}, {'end': 4655.488, 'src': 'embed', 'start': 4626.858, 'weight': 12, 'content': [{'end': 4638.01, 'text': 'unless they know what is driving that customer to leave the organization, it is very difficult for business to come up with incentives,', 'start': 4626.858, 'duration': 11.152}, {'end': 4639.932, 'text': 'come up with interventions right.', 'start': 4638.01, 'duration': 1.922}, {'end': 4645.818, 'text': 'so in this step, what we do is we try to correlate the results of the model.', 'start': 4639.932, 'duration': 5.886}, {'end': 4655.488, 'text': "we try to interpret the results of the model, with the the independent variables that you've included, to understand the reasons for the output.", 'start': 4645.818, 'duration': 9.67}], 'summary': 'Understanding customer churn reasons is crucial for effective business interventions.', 'duration': 28.63, 'max_score': 4626.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4626858.jpg'}], 'start': 3355.64, 'title': 'Analytics domains and data science lifecycle', 'summary': "Discusses diagnostic, predictive, and prescriptive analytics, with data scientists focusing primarily on predictive analytics. it emphasizes the skill sets required for descriptive and diagnostic analytics. additionally, it explores the data science project lifecycle, with 80% of a data analyst's time concentrated on diagnostic analytics, and the five stages of a data science project, common in 85-90% of projects.", 'chapters': [{'end': 3578.384, 'start': 3355.64, 'title': 'Analytics domains and skill sets', 'summary': 'Discusses diagnostic analytics, predictive analytics, and prescriptive analytics, emphasizing the primary focus of data scientists on predictive analytics, while also highlighting the importance of having the skill set for descriptive and diagnostic analytics.', 'duration': 222.744, 'highlights': ["Prescriptive analytics involves using concepts of diagnostic, descriptive, and predictive analytics to prescribe outcomes and come up with recommendations, such as recommendation engines like Netflix's, which prescribes movie recommendations to users based on their viewing history. Prescriptive analytics leverages diagnostic, descriptive, and predictive analytics to provide recommendations, for example, Netflix's recommendation engine prescribes movie recommendations to users based on their viewing history.", 'Predictive analytics focuses on using historical data and techniques like machine learning to predict future occurrences, and is the primary focus of data scientists, although they should also have the skill set for descriptive and diagnostic analytics. Predictive analytics primarily uses historical data and techniques like machine learning to predict future occurrences, and it is the main focus of data scientists, who should also possess the skill set for descriptive and diagnostic analytics.', 'Diagnostic analytics is the analysis to either prove or disprove hypotheses, and is a primary skill set of business analysts and data analysts, essential for gaining a better understanding of data. Diagnostic analytics involves analyzing hypotheses to prove or disprove them and is a crucial skill set for business analysts and data analysts to gain a better understanding of data.']}, {'end': 3890.385, 'start': 3579.805, 'title': 'Data science project lifecycle', 'summary': "Discusses the focus areas of data scientists, analysts, and business intelligence officers, with 80% of a data analyst's time concentrated on diagnostic analytics, while 20-25% of a data scientist's time goes to descriptive and diagnostic analytics. the chapter also elaborates on the five stages of a data science project, including data acquisition, pre-processing, model building, pattern evaluation, and knowledge representation, which are common in 85-90% of projects.", 'duration': 310.58, 'highlights': ["The focus areas of data scientists, analysts, and business intelligence officers are explained, with 80% of a data analyst's time concentrated on diagnostic analytics and 20-25% of a data scientist's time going to descriptive and diagnostic analytics. 80% of a data analyst's time is concentrated on diagnostic analytics, while 20-25% of a data scientist's time goes to descriptive and diagnostic analytics.", 'The chapter elaborates on the five stages of a data science project, including data acquisition, pre-processing, model building, pattern evaluation, and knowledge representation, which are common in 85-90% of projects. The five stages of a data science project are data acquisition, pre-processing, model building, pattern evaluation, and knowledge representation, common in 85-90% of projects.']}, {'end': 4172.073, 'start': 3890.385, 'title': 'Scoping business problems for data analysis', 'summary': 'Emphasizes the importance of scoping business problems and coming up with hypothesis to identify parameters that could impact the problem, such as customer churn, before acquiring data, in order to build a predictive model.', 'duration': 281.688, 'highlights': ['Identifying parameters impacting business problem before data acquisition It is essential to identify parameters that could impact the business problem, such as customer churn, before acquiring data to build a predictive model.', 'Emphasizing the need for scoping and hypothesis generation The chapter stresses the importance of scoping business problems and generating various hypotheses to understand the factors impacting customer churn.', 'Factors impacting customer churn Various factors impacting customer churn, such as cost of services, demographics, internet service quality, and customer service experience, are highlighted as crucial parameters to consider.']}, {'end': 4726.65, 'start': 4172.073, 'title': 'Customer service data analysis', 'summary': 'Discusses scoping the problem, data acquisition, data pre-processing, model building, and pattern evaluation in customer service data analysis, emphasizing the need to collect and process data for model building and interpreting model results to understand reasons for customer churn and retention.', 'duration': 554.577, 'highlights': ['The chapter emphasizes the need to collect and process data for model building and interpreting model results to understand reasons for customer churn and retention. The chapter discusses the importance of data acquisition and data pre-processing to create a composite data structure for model building, as well as the significance of interpreting model results to understand the reasons for customer churn and retention.', 'Data pre-processing involves merging data from various sources and improving data quality, ensuring completeness and accuracy for mathematical modeling. Data pre-processing includes merging data from various sources into a composite database, improving data quality by ensuring completeness and accuracy, and preparing the data for mathematical modeling.', 'Model building involves testing and iterating through various machine learning techniques to determine the most accurate solution for building the final model. Model building includes testing and iterating through various machine learning techniques to determine the most accurate solution for building the final model, emphasizing the importance of evaluating and choosing the technique that provides the best output.', 'Pattern evaluation focuses on interpreting model results and understanding the reasons for customer churn and retention to provide insights for business interventions. Pattern evaluation involves interpreting model results and understanding the reasons for customer churn and retention, aiming to provide insights for business interventions by correlating the results with independent variables and creating segments for targeted influence.', 'The chapter underlines the importance of understanding the reasons for customer churn and retention to enable effective business interventions and targeted influence. The chapter emphasizes the need to understand the reasons for customer churn and retention to enable effective business interventions and targeted influence, highlighting the significance of correlating model results with independent variables to provide actionable insights.']}], 'duration': 1371.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k3355640.jpg', 'highlights': ["Prescriptive analytics leverages diagnostic, descriptive, and predictive analytics to provide recommendations, e.g., Netflix's recommendation engine.", 'Predictive analytics primarily uses historical data and techniques like machine learning to predict future occurrences, and it is the main focus of data scientists.', 'Diagnostic analytics involves analyzing hypotheses to prove or disprove them and is a crucial skill set for business analysts and data analysts.', "80% of a data analyst's time is concentrated on diagnostic analytics, while 20-25% of a data scientist's time goes to descriptive and diagnostic analytics.", 'The five stages of a data science project are data acquisition, pre-processing, model building, pattern evaluation, and knowledge representation, common in 85-90% of projects.', 'It is essential to identify parameters that could impact the business problem, such as customer churn, before acquiring data to build a predictive model.', 'The chapter stresses the importance of scoping business problems and generating various hypotheses to understand the factors impacting customer churn.', 'Various factors impacting customer churn, such as cost of services, demographics, internet service quality, and customer service experience, are highlighted as crucial parameters to consider.', 'The chapter discusses the importance of data acquisition and data pre-processing to create a composite data structure for model building, as well as the significance of interpreting model results to understand the reasons for customer churn and retention.', 'Data pre-processing includes merging data from various sources into a composite database, improving data quality by ensuring completeness and accuracy, and preparing the data for mathematical modeling.', 'Model building includes testing and iterating through various machine learning techniques to determine the most accurate solution for building the final model, emphasizing the importance of evaluating and choosing the technique that provides the best output.', 'Pattern evaluation involves interpreting model results and understanding the reasons for customer churn and retention, aiming to provide insights for business interventions by correlating the results with independent variables and creating segments for targeted influence.', 'The chapter emphasizes the need to understand the reasons for customer churn and retention to enable effective business interventions and targeted influence, highlighting the significance of correlating model results with independent variables to provide actionable insights.']}, {'end': 6656.727, 'segs': [{'end': 5439.797, 'src': 'heatmap', 'start': 4745.846, 'weight': 0, 'content': [{'end': 4750.989, 'text': 'to come up with recommendations, to come up with outputs which are actionable for the business to implement.', 'start': 4745.846, 'duration': 5.143}, {'end': 4753.65, 'text': 'okay, that comes under pattern evaluation, okay.', 'start': 4750.989, 'duration': 2.661}, {'end': 4760.474, 'text': 'and finally, finally, the last step of your, of your data science engagement, is called knowledge representation.', 'start': 4753.65, 'duration': 6.824}, {'end': 4766.977, 'text': 'right, knowledge representation is nothing, but when you present the output of your model, when you present the learnings of your model,', 'start': 4760.474, 'duration': 6.503}, {'end': 4774.599, 'text': "when you present the output of your model to an audience, let's say, any any machine, let's say, any project that you pick right,", 'start': 4766.977, 'duration': 7.622}, {'end': 4782.883, 'text': "any project that you take has to be finally presented to, let's say, the leadership of an organization or any any business stakeholder.", 'start': 4774.599, 'duration': 8.284}, {'end': 4785.664, 'text': 'right, that is what comes under knowledge representation.', 'start': 4782.883, 'duration': 2.781}, {'end': 4792.367, 'text': "or you might, let's say, if you if you were to summarize the results of your model in a presentation, in a powerpoint presentation, or, let's say,", 'start': 4785.664, 'duration': 6.703}, {'end': 4799.39, 'text': "in a in a, you have to publish, let's say, a paper, A research paper, which captures all the learnings of your model, etc.", 'start': 4792.367, 'duration': 7.023}, {'end': 4802.391, 'text': 'All of that comes under knowledge representation.', 'start': 4799.49, 'duration': 2.901}, {'end': 4805.732, 'text': 'Usually knowledge representation would mean visualization of our data.', 'start': 4802.731, 'duration': 3.001}, {'end': 4817.576, 'text': "Visualization of the output in the manner so that it basically becomes interpretable and understandable for audience who doesn't really understand data science so well.", 'start': 4806.713, 'duration': 10.863}, {'end': 4826.496, 'text': 'Usually the CXOs of your organization might not understand technical terminologies which are associated with data science models.', 'start': 4817.696, 'duration': 8.8}, {'end': 4829.157, 'text': 'So you will have to create visualizations.', 'start': 4827.176, 'duration': 1.981}, {'end': 4833.939, 'text': "You'll have to create graphs to break down those technical terms.", 'start': 4829.177, 'duration': 4.762}, {'end': 4839.702, 'text': 'Break down those technical analysis in a simpler manners which is understandable.', 'start': 4834.54, 'duration': 5.162}, {'end': 4845.225, 'text': 'Which is something that could be consumed well by your primary audience.', 'start': 4839.882, 'duration': 5.343}, {'end': 4847.346, 'text': "That's something which comes under knowledge representation.", 'start': 4845.285, 'duration': 2.061}, {'end': 4855.78, 'text': "When you do that, it means that the that, the last phase of engagement, has ended, which means you've presented your results.", 'start': 4848.227, 'duration': 7.553}, {'end': 4860.663, 'text': 'then the leadership will take our decision whether they need to productionize your model or whether they need to reject your model.', 'start': 4855.78, 'duration': 4.883}, {'end': 4864.945, 'text': 'you will tell them that our model is coming up with an accuracy which is 80 80 of the times.', 'start': 4860.663, 'duration': 4.282}, {'end': 4869.408, 'text': "we'll be able to tell you that this is the first customer who is going to churn right,", 'start': 4864.945, 'duration': 4.463}, {'end': 4875.171, 'text': "and we will also tell you that the reason why they're going to churn right and if our our predictions are good enough,", 'start': 4869.408, 'duration': 5.763}, {'end': 4878.473, 'text': "then we'll be able to you'll be based on what interventions you decide to take.", 'start': 4875.171, 'duration': 3.302}, {'end': 4881.035, 'text': "you'll be able to retain, let's say, 30 of these customers.", 'start': 4878.473, 'duration': 2.562}, {'end': 4885.377, 'text': "if the business is happy with the recommendations that you're giving right.", 'start': 4881.716, 'duration': 3.661}, {'end': 4891.019, 'text': 'if the business is satisfied yeah, this is a very good model that you made then the final step would be then productionizing your model,', 'start': 4885.377, 'duration': 5.642}, {'end': 4897.622, 'text': 'making it a part of your whole data infrastructure and then, so that this could be done in a more automated manner, right.', 'start': 4891.019, 'duration': 6.603}, {'end': 4904.985, 'text': 'then converting it into an application right, an ai tool which is integrated with your, with your, with your database.', 'start': 4897.622, 'duration': 7.363}, {'end': 4907.586, 'text': "okay, so that's knowledge representation, by the way.", 'start': 4904.985, 'duration': 2.601}, {'end': 4912.4, 'text': 'uh, when we, when we get into building machine learning models right.', 'start': 4907.586, 'duration': 4.814}, {'end': 4917.243, 'text': 'in fact, this whole, this whole course, is structured in a way that you do all of this one by one, right.', 'start': 4912.4, 'duration': 4.843}, {'end': 4922.626, 'text': "so data acquisition, of course we will not do, because it doesn't come under the purview of r.", 'start': 4917.243, 'duration': 5.383}, {'end': 4923.846, 'text': 'like you will, you will get.', 'start': 4922.626, 'duration': 1.22}, {'end': 4926.048, 'text': 'you already have some data available with you, right.', 'start': 4923.846, 'duration': 2.202}, {'end': 4929.49, 'text': 'so you might not do any data acquisition right?', 'start': 4926.048, 'duration': 3.442}, {'end': 4931.671, 'text': 'data acquisition is usually not done in r.', 'start': 4929.49, 'duration': 2.181}, {'end': 4936.294, 'text': "it's done in using etl tools like alter x, sql, etc.", 'start': 4931.671, 'duration': 4.623}, {'end': 4945.146, 'text': 'okay. so, assuming this step is done From here onwards, which is data pre-processing, model building pattern evaluation, knowledge representation,', 'start': 4936.294, 'duration': 8.852}, {'end': 4946.827, 'text': 'all of this we will do in R.', 'start': 4945.146, 'duration': 1.681}, {'end': 4952.351, 'text': 'In any problem statement that we do, we will basically walk through each of these stages.', 'start': 4946.827, 'duration': 5.524}, {'end': 4956.305, 'text': "So now let's take a look at the data science process.", 'start': 4954.104, 'duration': 2.201}, {'end': 4960.527, 'text': 'How does a data scientist go from analyzing the problem to coming up with a solution?', 'start': 4956.585, 'duration': 3.942}, {'end': 4965.09, 'text': 'So the first step, as always, is understanding the business problem.', 'start': 4961.868, 'duration': 3.222}, {'end': 4970.573, 'text': "It's quite important to understand the business problem and then understanding it correctly,", 'start': 4965.49, 'duration': 5.083}, {'end': 4976.135, 'text': 'so that on a later stage you are bound to be solving the right kind of problem that needs to be solved.', 'start': 4970.573, 'duration': 5.562}, {'end': 4978.596, 'text': 'then comes data gathering.', 'start': 4977.316, 'duration': 1.28}, {'end': 4983.457, 'text': 'this is why the first step was so important, because if we did not understand the problem correctly,', 'start': 4978.596, 'duration': 4.861}, {'end': 4987.018, 'text': 'then we would end up gathering data that is not really required.', 'start': 4983.457, 'duration': 3.561}, {'end': 4996.059, 'text': 'data gathering basically means the task of getting data from multiple sources and then storing it in a specified format, like csv file, sql files,', 'start': 4987.018, 'duration': 9.041}, {'end': 5002.12, 'text': 'so on and so forth, and then from that data we move on to the next step, which is data analysis.', 'start': 4996.059, 'duration': 6.061}, {'end': 5007.58, 'text': 'in this we extract some key highlights or information about the data that we have, The format of the data,', 'start': 5002.12, 'duration': 5.46}, {'end': 5014.283, 'text': 'what are the minimum and the maximum values, how much data do we have, how many rows, how many columns, so on and so forth.', 'start': 5007.58, 'duration': 6.703}, {'end': 5018.344, 'text': 'Then we move on to processing data in which we remove unnecessary columns.', 'start': 5014.883, 'duration': 3.461}, {'end': 5028.048, 'text': 'We understand how to convert one form of data into another to make them both conform to a similar format and so on and so forth.', 'start': 5018.785, 'duration': 9.263}, {'end': 5030.971, 'text': 'Then we take a look at data visualization.', 'start': 5029.229, 'duration': 1.742}, {'end': 5035.975, 'text': 'Visualize the data, to extract some information out of it, to understand the trend,', 'start': 5031.531, 'duration': 4.444}, {'end': 5041.22, 'text': 'to understand the size of some data or to figure out the range of the data.', 'start': 5035.975, 'duration': 5.245}, {'end': 5042.865, 'text': 'if there are some outliers,', 'start': 5041.724, 'duration': 1.141}, {'end': 5051.349, 'text': 'if there are some data points that are just way outside the limit of normal data points and they are influencing our predictions badly.', 'start': 5042.865, 'duration': 8.484}, {'end': 5053.089, 'text': 'Then we move on to data cleaning.', 'start': 5051.869, 'duration': 1.22}, {'end': 5063.214, 'text': 'We remove the outliers, we remove inconsistent data, we remove columns that do not have a good amount of data in them, and so on and so forth.', 'start': 5053.169, 'duration': 10.045}, {'end': 5067.796, 'text': 'And finally, after we have done with all that, we move on to creating a model.', 'start': 5063.314, 'duration': 4.482}, {'end': 5071.258, 'text': 'A model is a mathematical representation of a real world,', 'start': 5068.096, 'duration': 3.162}, {'end': 5078.144, 'text': 'Information that we have extracted from the data that has gone through all the previous steps.', 'start': 5072.302, 'duration': 5.842}, {'end': 5083.845, 'text': 'So the first step in the data science process, as already described, is understanding the business solution now.', 'start': 5078.144, 'duration': 5.701}, {'end': 5088.066, 'text': "This is really important, mainly because if you don't understand the business problem correctly,", 'start': 5083.845, 'duration': 4.221}, {'end': 5092.107, 'text': 'Then you end up solving the wrong problem and end up spending a lot of time.', 'start': 5088.066, 'duration': 4.041}, {'end': 5097.389, 'text': 'So, to give you an example, suppose that you were to build an a movie recommendation system.', 'start': 5092.107, 'duration': 5.282}, {'end': 5101.51, 'text': 'now this is pretty vague description of what the problem is,', 'start': 5097.389, 'duration': 4.121}, {'end': 5108.594, 'text': "and because a movie recommendation could be a movie recommendation engine that suggests you movies based on the movies that you've watched previously.", 'start': 5101.51, 'duration': 7.084}, {'end': 5114.856, 'text': 'It could also be a movie recommendation engine that recommends you movies based on your geographical location.', 'start': 5109.114, 'duration': 5.742}, {'end': 5126.541, 'text': 'Or it could be movie recommendation engine that looks at the movies that have been watched by users who are similar to you and then suggests you those movies.', 'start': 5115.396, 'duration': 11.145}, {'end': 5132.593, 'text': 'So, depending on the kind of problem that we choose out of these three, we will have to gather different kinds of data,', 'start': 5127.15, 'duration': 5.443}, {'end': 5136.615, 'text': 'which is why understanding the problem is the first and the most important part of this process.', 'start': 5132.593, 'duration': 4.022}, {'end': 5140.707, 'text': 'then we move on to understanding we.', 'start': 5138.044, 'duration': 2.663}, {'end': 5142.508, 'text': 'firstly we ask the why.', 'start': 5140.707, 'duration': 1.801}, {'end': 5144.37, 'text': 'so why are we trying to solve this problem?', 'start': 5142.508, 'duration': 1.862}, {'end': 5146.872, 'text': 'what benefit does it provide us?', 'start': 5144.37, 'duration': 2.502}, {'end': 5151.857, 'text': 'or why would this solving this problem, uh, is so important to us?', 'start': 5146.872, 'duration': 4.985}, {'end': 5158.443, 'text': "and then we move on to understanding the end product, as we discussed what kind of things we're trying to solve,", 'start': 5151.857, 'duration': 6.586}, {'end': 5164.348, 'text': 'what are the problems that we might face and what is the thing that our product is supposed to produce at the end of the day?', 'start': 5158.443, 'duration': 5.905}, {'end': 5168.02, 'text': 'And then we move on to determining the data sources for this problem.', 'start': 5165.239, 'duration': 2.781}, {'end': 5172.981, 'text': "Depending on the kind of problem that we're trying to solve, the data sources could be very different from each other.", 'start': 5168.18, 'duration': 4.801}, {'end': 5177.643, 'text': 'We could extract data from one source and we could also extract data from another source.', 'start': 5173.341, 'duration': 4.302}, {'end': 5183.264, 'text': 'Depending on the data source that we have chosen, we will get the right kind of data.', 'start': 5177.983, 'duration': 5.281}, {'end': 5190.026, 'text': "Or if you have chosen the wrong data source, then we'll get the wrong kind of data and we won't be able to solve the problem that we wish to solve.", 'start': 5183.685, 'duration': 6.341}, {'end': 5195.008, 'text': 'Then we move on to gathering information and gathering the required context.', 'start': 5190.927, 'duration': 4.081}, {'end': 5202.106, 'text': 'And when we get the required context and get all the information that we need to solve this problem, we move on to the next step,', 'start': 5195.558, 'duration': 6.548}, {'end': 5203.467, 'text': 'which is data gathering.', 'start': 5202.106, 'duration': 1.361}, {'end': 5210.736, 'text': 'So data gathering is the process of retrieving data from various sources, which is to be used in our data science process.', 'start': 5204.348, 'duration': 6.388}, {'end': 5217.961, 'text': 'So to give you an example, a data science process, the data can be gathered from various sources such as CSV files.', 'start': 5211.357, 'duration': 6.604}, {'end': 5222.284, 'text': 'We could get it from the internet using a web API or web script.', 'start': 5218.321, 'duration': 3.963}, {'end': 5225.466, 'text': 'We could also get data from SQL databases.', 'start': 5222.804, 'duration': 2.662}, {'end': 5227.427, 'text': 'We could get it from NoSQL databases.', 'start': 5225.526, 'duration': 1.901}, {'end': 5230.509, 'text': 'We could get it from a third party source.', 'start': 5227.828, 'duration': 2.681}, {'end': 5232.09, 'text': 'We could get it from user service.', 'start': 5230.529, 'duration': 1.561}, {'end': 5234.732, 'text': 'There are lots of ways that we can get the data from.', 'start': 5232.25, 'duration': 2.482}, {'end': 5238.377, 'text': "But the main important part is we're gathering the right kind of data.", 'start': 5235.376, 'duration': 3.001}, {'end': 5247.061, 'text': 'So if we end up gathering the wrong kind of data, what ends up happening is that we get the data that we wish to solve the problem.', 'start': 5238.778, 'duration': 8.283}, {'end': 5253.504, 'text': 'However, we have gotten the wrong kind of data and that data is not useful for us to solve the problem.', 'start': 5247.261, 'duration': 6.243}, {'end': 5257.866, 'text': "And we've essentially wasted a lot of time in gathering data that we do not need.", 'start': 5253.584, 'duration': 4.282}, {'end': 5265.14, 'text': 'So when we move on to the next step, we need to understand that we have gathered the right kind of data, which is why the first step was so important.', 'start': 5258.294, 'duration': 6.846}, {'end': 5267.482, 'text': 'Then comes data processing.', 'start': 5266.241, 'duration': 1.241}, {'end': 5275.422, 'text': "so when we're trying to process some data, what we're trying to do is we're trying to convert data into easily readable formats.", 'start': 5268.658, 'duration': 6.764}, {'end': 5278.524, 'text': 'now, when we say easily readable, i mean easily processable.', 'start': 5275.422, 'duration': 3.102}, {'end': 5299.058, 'text': 'so if we have multiple data sources from which we have extracted a lot of dates and if all those dates are in different formats some of them have months before dates and some of them have dates before months then it would be really difficult for our program or our data science process to understand just which one to use and when to convert which.', 'start': 5278.524, 'duration': 20.534}, {'end': 5306.984, 'text': 'So for that we need to perform data processing and then bring them all into a same format, and then our data science process.', 'start': 5299.598, 'duration': 7.386}, {'end': 5311.088, 'text': 'can you work on that unified format that we have converted our data into?', 'start': 5306.984, 'duration': 4.104}, {'end': 5313.669, 'text': 'Then comes data analysis.', 'start': 5312.128, 'duration': 1.541}, {'end': 5319.292, 'text': 'So data analysis is the task of analyzing data sets to summarize their main characteristics.', 'start': 5314.55, 'duration': 4.742}, {'end': 5320.593, 'text': 'What is the mean of the data?', 'start': 5319.412, 'duration': 1.181}, {'end': 5324.275, 'text': 'What is the most frequently occurring value in the data set?', 'start': 5320.633, 'duration': 3.642}, {'end': 5327.676, 'text': 'What is the minimum and maximum value?', 'start': 5325.175, 'duration': 2.501}, {'end': 5330.658, 'text': 'How much is the difference between the minimum and the maximum value?', 'start': 5328.057, 'duration': 2.601}, {'end': 5332.999, 'text': 'Is it normal to have that much of a difference?', 'start': 5330.858, 'duration': 2.141}, {'end': 5334.3, 'text': 'So on and so forth.', 'start': 5333.459, 'duration': 0.841}, {'end': 5339.143, 'text': 'Now these characteristics also allow us to understand the shape of our data.', 'start': 5334.78, 'duration': 4.363}, {'end': 5358.973, 'text': 'We can also take a look at the standard variance or the standard deviation of our data set to understand just how different our data is from each other and what are the things that we can do to make sure that our data and process does not get bulked up in later stages because of some problem in our data.', 'start': 5339.183, 'duration': 19.79}, {'end': 5361.675, 'text': 'Then we move on to data cleaning.', 'start': 5360.094, 'duration': 1.581}, {'end': 5364.428, 'text': 'Now data cleaning is also very important.', 'start': 5362.267, 'duration': 2.161}, {'end': 5373.41, 'text': 'Data cleaning basically means removing inaccurate or unwanted or unnecessary data from our data set.', 'start': 5364.928, 'duration': 8.482}, {'end': 5382.852, 'text': "Since we've gathered a lot of data, it's quite possible that we have data that is in incorrect format, that is unwanted, that is inaccurate.", 'start': 5373.79, 'duration': 9.062}, {'end': 5389.174, 'text': 'It could be that the people who are recording the data did not have the equipment to record the data.', 'start': 5383.733, 'duration': 5.441}, {'end': 5390.95, 'text': 'their equipment for faulty.', 'start': 5389.73, 'duration': 1.22}, {'end': 5397.012, 'text': 'It could be that the data that we have is in a format that is no longer used and the data needs.', 'start': 5391.39, 'duration': 5.622}, {'end': 5400.533, 'text': "there's nothing more we can do but to just drop the data set.", 'start': 5397.012, 'duration': 3.521}, {'end': 5406.255, 'text': 'Or maybe that is that that data set does not contain a lot of values.', 'start': 5401.193, 'duration': 5.062}, {'end': 5407.275, 'text': 'So, for instance,', 'start': 5406.455, 'duration': 0.82}, {'end': 5415.097, 'text': "if you're gathering a lot of data and at the end we have some sort of unique identification number and that was introduced on a later stage,", 'start': 5407.275, 'duration': 7.822}, {'end': 5417.878, 'text': 'lots of the records in that data set might not have that number.', 'start': 5415.097, 'duration': 2.781}, {'end': 5423.726, 'text': "So what to do in these situations is you need to take a look at the data and understand how you're going to clean it.", 'start': 5418.462, 'duration': 5.264}, {'end': 5431.311, 'text': 'You could just fill all the missing values with zero, or you could fill it with the mean of the entire data set,', 'start': 5424.106, 'duration': 7.205}, {'end': 5436.915, 'text': 'or you could also drop all the data sets that contain null values or that have missing values.', 'start': 5431.311, 'duration': 5.604}, {'end': 5439.797, 'text': 'Then comes data visualization.', 'start': 5438.576, 'duration': 1.221}], 'summary': 'Data science process involves understanding business problem, data gathering, analysis, and model creation to provide actionable recommendations, with knowledge representation for visualization and presentation.', 'duration': 693.951, 'max_score': 4745.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4745846.jpg'}, {'end': 4829.157, 'src': 'embed', 'start': 4806.713, 'weight': 2, 'content': [{'end': 4817.576, 'text': "Visualization of the output in the manner so that it basically becomes interpretable and understandable for audience who doesn't really understand data science so well.", 'start': 4806.713, 'duration': 10.863}, {'end': 4826.496, 'text': 'Usually the CXOs of your organization might not understand technical terminologies which are associated with data science models.', 'start': 4817.696, 'duration': 8.8}, {'end': 4829.157, 'text': 'So you will have to create visualizations.', 'start': 4827.176, 'duration': 1.981}], 'summary': 'Create visualizations to make data science models understandable for non-technical audience.', 'duration': 22.444, 'max_score': 4806.713, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4806713.jpg'}, {'end': 4978.596, 'src': 'embed', 'start': 4936.294, 'weight': 3, 'content': [{'end': 4945.146, 'text': 'okay. so, assuming this step is done From here onwards, which is data pre-processing, model building pattern evaluation, knowledge representation,', 'start': 4936.294, 'duration': 8.852}, {'end': 4946.827, 'text': 'all of this we will do in R.', 'start': 4945.146, 'duration': 1.681}, {'end': 4952.351, 'text': 'In any problem statement that we do, we will basically walk through each of these stages.', 'start': 4946.827, 'duration': 5.524}, {'end': 4956.305, 'text': "So now let's take a look at the data science process.", 'start': 4954.104, 'duration': 2.201}, {'end': 4960.527, 'text': 'How does a data scientist go from analyzing the problem to coming up with a solution?', 'start': 4956.585, 'duration': 3.942}, {'end': 4965.09, 'text': 'So the first step, as always, is understanding the business problem.', 'start': 4961.868, 'duration': 3.222}, {'end': 4970.573, 'text': "It's quite important to understand the business problem and then understanding it correctly,", 'start': 4965.49, 'duration': 5.083}, {'end': 4976.135, 'text': 'so that on a later stage you are bound to be solving the right kind of problem that needs to be solved.', 'start': 4970.573, 'duration': 5.562}, {'end': 4978.596, 'text': 'then comes data gathering.', 'start': 4977.316, 'duration': 1.28}], 'summary': 'In data science, understanding the business problem is crucial before proceeding to data gathering and analysis in r.', 'duration': 42.302, 'max_score': 4936.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4936294.jpg'}, {'end': 5078.144, 'src': 'embed', 'start': 5053.169, 'weight': 9, 'content': [{'end': 5063.214, 'text': 'We remove the outliers, we remove inconsistent data, we remove columns that do not have a good amount of data in them, and so on and so forth.', 'start': 5053.169, 'duration': 10.045}, {'end': 5067.796, 'text': 'And finally, after we have done with all that, we move on to creating a model.', 'start': 5063.314, 'duration': 4.482}, {'end': 5071.258, 'text': 'A model is a mathematical representation of a real world,', 'start': 5068.096, 'duration': 3.162}, {'end': 5078.144, 'text': 'Information that we have extracted from the data that has gone through all the previous steps.', 'start': 5072.302, 'duration': 5.842}], 'summary': 'Data preprocessing involves removing outliers, inconsistent data, and columns with insufficient data, followed by creating a mathematical model representing real-world information extracted from the processed data.', 'duration': 24.975, 'max_score': 5053.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k5053169.jpg'}, {'end': 5299.058, 'src': 'embed', 'start': 5258.294, 'weight': 5, 'content': [{'end': 5265.14, 'text': 'So when we move on to the next step, we need to understand that we have gathered the right kind of data, which is why the first step was so important.', 'start': 5258.294, 'duration': 6.846}, {'end': 5267.482, 'text': 'Then comes data processing.', 'start': 5266.241, 'duration': 1.241}, {'end': 5275.422, 'text': "so when we're trying to process some data, what we're trying to do is we're trying to convert data into easily readable formats.", 'start': 5268.658, 'duration': 6.764}, {'end': 5278.524, 'text': 'now, when we say easily readable, i mean easily processable.', 'start': 5275.422, 'duration': 3.102}, {'end': 5299.058, 'text': 'so if we have multiple data sources from which we have extracted a lot of dates and if all those dates are in different formats some of them have months before dates and some of them have dates before months then it would be really difficult for our program or our data science process to understand just which one to use and when to convert which.', 'start': 5278.524, 'duration': 20.534}], 'summary': 'Data processing involves converting data into easily readable and processable formats, ensuring consistency for effective use.', 'duration': 40.764, 'max_score': 5258.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k5258294.jpg'}, {'end': 5473.686, 'src': 'embed', 'start': 5438.576, 'weight': 6, 'content': [{'end': 5439.797, 'text': 'Then comes data visualization.', 'start': 5438.576, 'duration': 1.221}, {'end': 5446.154, 'text': 'So data visualization is a graphical representation of the information or the data that we have.', 'start': 5440.37, 'duration': 5.784}, {'end': 5448.076, 'text': 'It allows us to visualize the data.', 'start': 5446.675, 'duration': 1.401}, {'end': 5449.757, 'text': 'it allows us to understand it.', 'start': 5448.076, 'duration': 1.681}, {'end': 5454.06, 'text': 'it allows us to see some trends and some patterns in the data that we have in front of us.', 'start': 5449.757, 'duration': 4.303}, {'end': 5460.765, 'text': 'Taking a look at a large blob of numbers is not really helpful, but if you visualize it, if you convert it into a bar chart,', 'start': 5454.52, 'duration': 6.245}, {'end': 5465.789, 'text': "if you convert it into a scatter plot, then it's quite easy for us to understand what that data represents.", 'start': 5460.765, 'duration': 5.024}, {'end': 5473.686, 'text': 'If you want to make a career in data science, then Intellipaat has IIT Madras Advanced Data Science and AI Certification program.', 'start': 5466.283, 'duration': 7.403}], 'summary': 'Data visualization presents data graphically for better understanding and trend analysis. intellipaat offers iit madras advanced data science and ai certification program for aspiring data scientists.', 'duration': 35.11, 'max_score': 5438.576, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k5438576.jpg'}, {'end': 5755.453, 'src': 'embed', 'start': 5722.776, 'weight': 10, 'content': [{'end': 5725.018, 'text': "depending on the kind of predictions that we're trying to make.", 'start': 5722.776, 'duration': 2.242}, {'end': 5727.661, 'text': "so that's how you test a model.", 'start': 5725.018, 'duration': 2.643}, {'end': 5731.804, 'text': "so when you're trying to test a model, several key terminologies come into mind.", 'start': 5727.661, 'duration': 4.143}, {'end': 5735.127, 'text': 'one is confusion matrix and accuracy score, and so on and so forth.', 'start': 5731.804, 'duration': 3.323}, {'end': 5740.268, 'text': 'confusion matrix is just a way of visualizing the correctly interpreted data,', 'start': 5735.726, 'duration': 4.542}, {'end': 5748.611, 'text': 'and accuracy score is used to give a percentage to the amount of data that was correctly classified or correctly predicted.', 'start': 5740.268, 'duration': 8.343}, {'end': 5755.453, 'text': 'so if we had 10 records and we wanted to make predictions and eight of them were correctly predicted and two of them were incorrectly predicted,', 'start': 5748.611, 'duration': 6.842}], 'summary': 'Testing a model involves using confusion matrix and accuracy score to evaluate predictions, with 80% accuracy in a 10-record scenario.', 'duration': 32.677, 'max_score': 5722.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k5722776.jpg'}, {'end': 6037.071, 'src': 'embed', 'start': 6004.707, 'weight': 11, 'content': [{'end': 6016.912, 'text': "but this is an example I'm just taking to explain you that what anybody expects out of the data in the simple charts to describe,", 'start': 6004.707, 'duration': 12.205}, {'end': 6020.613, 'text': 'about statistics of the data.', 'start': 6016.912, 'duration': 3.701}, {'end': 6035.39, 'text': "so in this session I'm going to talk about more about descriptive statistics to the analysis of data that helps to describe or to summarize, okay,", 'start': 6020.613, 'duration': 14.777}, {'end': 6037.071, 'text': 'in a meaningful way.', 'start': 6035.39, 'duration': 1.681}], 'summary': 'Descriptive statistics analyze and summarize data for meaningful insights.', 'duration': 32.364, 'max_score': 6004.707, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6004707.jpg'}, {'end': 6535.883, 'src': 'embed', 'start': 6504.898, 'weight': 14, 'content': [{'end': 6510.24, 'text': 'so the percentage of growth is nothing but the difference between these by the original.', 'start': 6504.898, 'duration': 5.342}, {'end': 6511.56, 'text': 'so that is one measure.', 'start': 6510.24, 'duration': 1.32}, {'end': 6521.539, 'text': 'percentage of growth is one measure that gives you the complete understanding of the people who are speaking Bengali.', 'start': 6511.56, 'duration': 9.979}, {'end': 6527.581, 'text': 'but from this data they can be many, many questions can come out of it.', 'start': 6521.539, 'duration': 6.042}, {'end': 6535.883, 'text': 'so let us represent all of them in a simple form, so that that particular form, it may be a visualization graph,', 'start': 6527.581, 'duration': 8.302}], 'summary': "Percentage of growth measures bengali speakers' understanding. visualization graph represents data.", 'duration': 30.985, 'max_score': 6504.898, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6504898.jpg'}, {'end': 6587.944, 'src': 'embed', 'start': 6554.131, 'weight': 16, 'content': [{'end': 6556.272, 'text': 'okay, and we are trying to in.', 'start': 6554.131, 'duration': 2.141}, {'end': 6564.496, 'text': 'we are trying to understand this data and after understanding the data, we are trying to find out what is that useful information that is needed.', 'start': 6556.272, 'duration': 8.224}, {'end': 6567.417, 'text': 'so useful information means those are patterns in the data.', 'start': 6564.496, 'duration': 2.921}, {'end': 6569.82, 'text': 'there can be many patterns.', 'start': 6568.078, 'duration': 1.742}, {'end': 6576.528, 'text': 'you can retrieve many information out of it, many information points out of it.', 'start': 6569.82, 'duration': 6.708}, {'end': 6587.944, 'text': 'but from the business point of view, if a person asks a question from the business question, how do you really, interpretive, find out that pattern?', 'start': 6576.528, 'duration': 11.416}], 'summary': 'Analyzing data to uncover useful patterns for business insights.', 'duration': 33.813, 'max_score': 6554.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6554131.jpg'}, {'end': 6630.481, 'src': 'embed', 'start': 6602.633, 'weight': 15, 'content': [{'end': 6609.648, 'text': 'here in this data, south india is not well, not well defined, but that is the question I am asking you.', 'start': 6602.633, 'duration': 7.015}, {'end': 6615.211, 'text': 'so you know some of the South Indian places and you know some of the South Indian languages.', 'start': 6609.648, 'duration': 5.563}, {'end': 6623.576, 'text': 'so you filter out those languages and then, for example, Tamil, Telugu and any other languages.', 'start': 6615.211, 'duration': 8.365}, {'end': 6625.898, 'text': 'you can see Malayalam.', 'start': 6623.576, 'duration': 2.322}, {'end': 6630.481, 'text': 'so, for example, some of these South Indian languages, I filter them out.', 'start': 6625.898, 'duration': 4.583}], 'summary': 'Data includes south indian languages like tamil, telugu, and malayalam.', 'duration': 27.848, 'max_score': 6602.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6602633.jpg'}], 'start': 4726.65, 'title': 'Data science fundamentals', 'summary': 'Covers key steps in data science engagement, the process and production of models, importance of data visualization, and introduction to descriptive statistics. it emphasizes understanding business problems, simplifying complex information, and basics of descriptive statistics in presenting data effectively.', 'chapters': [{'end': 4847.346, 'start': 4726.65, 'title': 'Data science engagement steps', 'summary': 'Outlines the three key steps in a data science engagement: pattern evaluation, actionable recommendations, and knowledge representation, which involves presenting model outputs in a simplified and understandable manner for non-technical stakeholders.', 'duration': 120.696, 'highlights': ['The last step of a data science engagement is knowledge representation, where the model outputs and learnings are presented to non-technical stakeholders in a simplified, consumable manner.', 'Pattern evaluation involves interpreting the findings of the model and correlating them with the data and variables to come up with actionable recommendations for the business to implement.', 'Visualization of data and output is crucial in knowledge representation to make it interpretable and understandable for non-technical audience, such as CXOs of the organization.']}, {'end': 5505.506, 'start': 4848.227, 'title': 'Data science process & model production', 'summary': 'Explains the data science process, including data gathering, processing, analysis, cleaning, visualization, and the final steps for model production, with a focus on understanding the business problem, gathering the right data, and processing it effectively.', 'duration': 657.279, 'highlights': ['The chapter explains the data science process, including data gathering, processing, analysis, cleaning, visualization, and the final steps for model production. The chapter covers the key stages of the data science process, providing a comprehensive overview of the entire workflow.', 'Understanding the business problem and gathering the right data is emphasized as crucial in the data science process. Emphasizing the importance of understanding the business problem correctly to gather the required data for effective analysis and modeling.', 'Data processing involves converting data into easily readable and processable formats for effective analysis and modeling. Explaining the significance of data processing in unifying data formats for streamlined analysis and modeling.', 'Data visualization is highlighted as a crucial step in understanding trends and patterns within the data for effective analysis and decision-making. Underlining the importance of data visualization in gaining insights and understanding patterns within the data for informed decision-making.', 'The final steps for model production, including knowledge representation and converting the model into an application, are outlined. Providing insights into the final steps involved in producing a model, such as knowledge representation and application integration.']}, {'end': 5758.134, 'start': 5506.087, 'title': 'Importance of data visualization', 'summary': 'Emphasizes the importance of data visualization in understanding trends, correlations, simplifying complex information for user interpretation, and creating and testing models for prediction and classification, with a focus on accuracy scores and algorithm selection.', 'duration': 252.047, 'highlights': ['Data visualization is crucial for understanding trends, correlations, and simplifying complex information for user interpretation. Data visualization helps in understanding the trend of the data, correlation between variables, and simplifying complex information into user-friendly formats.', 'Creating a model involves extracting useful information, applying algorithms, and testing for accuracy scores. Creating a model involves extracting useful information from the data set, applying algorithms to create a mathematical representation, and testing the model using accuracy scores.', 'Testing the model involves key terminologies such as confusion matrix and accuracy score, with an emphasis on correctly predicted data. Testing the model involves using key terminologies like confusion matrix and accuracy score to evaluate the percentage of correctly classified or predicted data.']}, {'end': 6409.032, 'start': 5763.717, 'title': 'Introduction to descriptive statistics', 'summary': 'Introduces the basics of descriptive statistics, providing examples of data interpretation, such as population projections and language demographics, and emphasizes its importance in presenting data in a meaningful and simple way.', 'duration': 645.315, 'highlights': ['Descriptive statistics enables us to present the data in a more meaningful way, providing simple interpretation of the data. Descriptive statistics helps in presenting the data meaningfully and simply, allowing for easy interpretation of the data.', 'Examples of data interpretation, including population projections and language demographics, illustrate the application of descriptive statistics. The transcript provides examples of interpreting data, such as population projections and language demographics, showcasing the practical application of descriptive statistics.', 'Emphasis on the importance of descriptive statistics in summarizing data and representing it in a simple yet meaningful format. The importance of descriptive statistics in summarizing data and presenting it in a simple yet meaningful way is emphasized throughout the chapter.']}, {'end': 6656.727, 'start': 6409.032, 'title': 'Data analysis and interpretation', 'summary': 'Discusses the importance of simple measures and descriptive statistics in interpreting and visualizing data, with examples such as calculating percentage growth and filtering out specific data points for meaningful analysis.', 'duration': 247.695, 'highlights': ['Percentage of growth is a key measure for understanding demographic changes, exemplified by the growth of Bengali speakers from 1991 to 2001. The speaker illustrates the calculation of percentage growth as a measure to understand demographic changes, specifically focusing on the growth of Bengali speakers from 1991 to 2001.', 'Filtering out specific data points, such as South Indian languages, to analyze and represent patterns in the data. The importance of filtering out specific data points, like South Indian languages, to identify patterns and represent them in a simple form is emphasized for meaningful data interpretation.', 'Emphasizing the need to find useful information and patterns in the data from a business perspective. The discussion highlights the importance of identifying useful information and patterns in the data from a business perspective, emphasizing the need to interpret and find patterns for informed decision-making.']}], 'duration': 1930.077, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k4726650.jpg', 'highlights': ['The last step of a data science engagement is knowledge representation, where the model outputs and learnings are presented to non-technical stakeholders in a simplified, consumable manner.', 'Pattern evaluation involves interpreting the findings of the model and correlating them with the data and variables to come up with actionable recommendations for the business to implement.', 'Visualization of data and output is crucial in knowledge representation to make it interpretable and understandable for non-technical audience, such as CXOs of the organization.', 'The chapter explains the data science process, including data gathering, processing, analysis, cleaning, visualization, and the final steps for model production. The chapter covers the key stages of the data science process, providing a comprehensive overview of the entire workflow.', 'Understanding the business problem and gathering the right data is emphasized as crucial in the data science process. Emphasizing the importance of understanding the business problem correctly to gather the required data for effective analysis and modeling.', 'Data processing involves converting data into easily readable and processable formats for effective analysis and modeling. Explaining the significance of data processing in unifying data formats for streamlined analysis and modeling.', 'Data visualization is highlighted as a crucial step in understanding trends and patterns within the data for effective analysis and decision-making. Underlining the importance of data visualization in gaining insights and understanding patterns within the data for informed decision-making.', 'The final steps for model production, including knowledge representation and converting the model into an application, are outlined. Providing insights into the final steps involved in producing a model, such as knowledge representation and application integration.', 'Data visualization is crucial for understanding trends, correlations, and simplifying complex information for user interpretation. Data visualization helps in understanding the trend of the data, correlation between variables, and simplifying complex information into user-friendly formats.', 'Creating a model involves extracting useful information, applying algorithms, and testing for accuracy scores. Creating a model involves extracting useful information from the data set, applying algorithms to create a mathematical representation, and testing the model using accuracy scores.', 'Testing the model involves key terminologies such as confusion matrix and accuracy score, with an emphasis on correctly predicted data. Testing the model involves using key terminologies like confusion matrix and accuracy score to evaluate the percentage of correctly classified or predicted data.', 'Descriptive statistics enables us to present the data in a more meaningful way, providing simple interpretation of the data. Descriptive statistics helps in presenting the data meaningfully and simply, allowing for easy interpretation of the data.', 'Examples of data interpretation, including population projections and language demographics, illustrate the application of descriptive statistics. The transcript provides examples of interpreting data, such as population projections and language demographics, showcasing the practical application of descriptive statistics.', 'Emphasis on the importance of descriptive statistics in summarizing data and representing it in a simple yet meaningful format. The importance of descriptive statistics in summarizing data and presenting it in a simple yet meaningful way is emphasized throughout the chapter.', 'Percentage of growth is a key measure for understanding demographic changes, exemplified by the growth of Bengali speakers from 1991 to 2001. The speaker illustrates the calculation of percentage growth as a measure to understand demographic changes, specifically focusing on the growth of Bengali speakers from 1991 to 2001.', 'Filtering out specific data points, such as South Indian languages, to analyze and represent patterns in the data. The importance of filtering out specific data points, like South Indian languages, to identify patterns and represent them in a simple form is emphasized for meaningful data interpretation.', 'Emphasizing the need to find useful information and patterns in the data from a business perspective. The discussion highlights the importance of identifying useful information and patterns in the data from a business perspective, emphasizing the need to interpret and find patterns for informed decision-making.']}, {'end': 8581.846, 'segs': [{'end': 6779.116, 'src': 'embed', 'start': 6717.755, 'weight': 0, 'content': [{'end': 6724.322, 'text': "Here descriptive statistics means it's a collection, presentation, and description of sample data.", 'start': 6717.755, 'duration': 6.567}, {'end': 6729.568, 'text': 'So descriptive statistics are used by researchers to report on population and samples.', 'start': 6724.983, 'duration': 4.585}, {'end': 6731.39, 'text': "Just know that's what we have seen.", 'start': 6730.028, 'duration': 1.362}, {'end': 6746.515, 'text': 'And in sociality, and it is a kind of a tool that we use to comprehend the complete raw data in a more meaningful format, that is,', 'start': 6732.15, 'duration': 14.365}, {'end': 6748.155, 'text': 'descriptive statistics.', 'start': 6746.515, 'duration': 1.64}, {'end': 6753.296, 'text': 'so you got the data and you are explaining about the data very simple.', 'start': 6748.155, 'duration': 5.141}, {'end': 6756.417, 'text': 'so what is inferential statistics?', 'start': 6753.296, 'duration': 3.121}, {'end': 6767.224, 'text': 'inferential statistics are those from that data you make decisions or you draw some kind of conclusions out of the data.', 'start': 6756.417, 'duration': 10.807}, {'end': 6772.229, 'text': "For example, I'm going to the same data here.", 'start': 6767.924, 'duration': 4.305}, {'end': 6779.116, 'text': 'I was talking about number of people, maximum number of people, percentage of growth, and all these things.', 'start': 6773.05, 'duration': 6.066}], 'summary': 'Descriptive statistics explains sample data, inferential statistics draw conclusions from data.', 'duration': 61.361, 'max_score': 6717.755, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6717755.jpg'}, {'end': 6909.917, 'src': 'embed', 'start': 6875.82, 'weight': 1, 'content': [{'end': 6877.522, 'text': 'should I focus more on the urban?', 'start': 6875.82, 'duration': 1.702}, {'end': 6878.904, 'text': 'should I focus more on the rural?', 'start': 6877.522, 'duration': 1.382}, {'end': 6885.187, 'text': 'okay, so the descriptive statistics just give you the information about the data,', 'start': 6879.924, 'duration': 5.263}, {'end': 6893.052, 'text': 'but inferential statistics is the one that helps you to infer something from the other.', 'start': 6885.187, 'duration': 7.865}, {'end': 6895.226, 'text': 'I want to infer that.', 'start': 6894.085, 'duration': 1.141}, {'end': 6898.749, 'text': 'I will put it like this.', 'start': 6895.666, 'duration': 3.083}, {'end': 6907.275, 'text': 'For example, in the rural area, this is the population that is increasing.', 'start': 6898.869, 'duration': 8.406}, {'end': 6909.917, 'text': 'This is the population of female.', 'start': 6907.295, 'duration': 2.622}], 'summary': 'Comparing urban and rural population growth using inferential statistics.', 'duration': 34.097, 'max_score': 6875.82, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6875820.jpg'}, {'end': 6972.729, 'src': 'embed', 'start': 6941.247, 'weight': 3, 'content': [{'end': 6952.116, 'text': 'okay. in order to do that one, first we have to see the velocity velocity of the population growth rate in each year.', 'start': 6941.247, 'duration': 10.869}, {'end': 6953.778, 'text': 'what i mean by velocity?', 'start': 6952.116, 'duration': 1.662}, {'end': 6957.681, 'text': 'velocity is nothing but, uh, the number of popular.', 'start': 6953.778, 'duration': 3.903}, {'end': 6967.948, 'text': 'for example, from 2016, it is 11 827, okay, and 2015 then, of the same time, okay.', 'start': 6957.681, 'duration': 10.267}, {'end': 6972.729, 'text': "I'm calculating the velocity in one year 11, 000 827.", 'start': 6967.948, 'duration': 4.781}], 'summary': 'Calculate population growth velocity using annual data.', 'duration': 31.482, 'max_score': 6941.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6941247.jpg'}, {'end': 7052.86, 'src': 'embed', 'start': 7028.841, 'weight': 4, 'content': [{'end': 7037.886, 'text': 'then from the data, okay, once you get the data, that is, in the raw data form, fortunately this data is very clean.', 'start': 7028.841, 'duration': 9.045}, {'end': 7042.17, 'text': 'clean. what I mean by is that there are no missing values.', 'start': 7037.886, 'duration': 4.284}, {'end': 7052.86, 'text': 'okay, which has a very clean number, so we can draw, we can actually describe about this data in the more descriptive form.', 'start': 7042.17, 'duration': 10.69}], 'summary': 'Clean raw data with no missing values for descriptive analysis.', 'duration': 24.019, 'max_score': 7028.841, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k7028841.jpg'}, {'end': 7276.048, 'src': 'embed', 'start': 7244.398, 'weight': 6, 'content': [{'end': 7252.22, 'text': 'whether it is maximizing the sales or for deciding the or understanding the customer intentions.', 'start': 7244.398, 'duration': 7.822}, {'end': 7258.402, 'text': 'what i mean by intentions, customer intentions are what exactly is looking for in my e-commerce site?', 'start': 7252.22, 'duration': 6.182}, {'end': 7263.824, 'text': 'so for any business problem, we have to start collecting the data.', 'start': 7258.402, 'duration': 5.422}, {'end': 7265.204, 'text': 'so how do we collect this data?', 'start': 7263.824, 'duration': 1.38}, {'end': 7276.048, 'text': 'So do we collect this data from Monday to Friday or during the weekends or for continuous periods of months?', 'start': 7266.641, 'duration': 9.407}], 'summary': 'Collect data to understand customer intentions for maximizing sales.', 'duration': 31.65, 'max_score': 7244.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k7244398.jpg'}, {'end': 7327.766, 'src': 'embed', 'start': 7294.024, 'weight': 5, 'content': [{'end': 7297.587, 'text': 'so whether my sales have been improving or not?', 'start': 7294.024, 'duration': 3.563}, {'end': 7303.072, 'text': "so these are the some kinds of data points that i'm actually looking for.", 'start': 7297.587, 'duration': 5.485}, {'end': 7315.037, 'text': 'so now, if you take these four products, before I start analyzing or taking any decision,', 'start': 7303.072, 'duration': 11.965}, {'end': 7323.463, 'text': 'I would like to take the help of descriptive statistics to understand the data.', 'start': 7315.037, 'duration': 8.426}, {'end': 7327.766, 'text': 'let us take a very simple example here.', 'start': 7323.463, 'duration': 4.303}], 'summary': 'Analyzing sales data for four products using descriptive statistics.', 'duration': 33.742, 'max_score': 7294.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k7294024.jpg'}, {'end': 7513.803, 'src': 'embed', 'start': 7477.429, 'weight': 7, 'content': [{'end': 7479.431, 'text': 'Let us take an Excel example.', 'start': 7477.429, 'duration': 2.002}, {'end': 7491.668, 'text': "So these are the four products that I'm talking about and their sales.", 'start': 7487.546, 'duration': 4.122}, {'end': 7500.793, 'text': 'So when you want to summarize this data in Excel, okay, because everybody is having a good exposure to Excel.', 'start': 7492.589, 'duration': 8.204}, {'end': 7503.055, 'text': "So that's why I'm taking the example of Excel here.", 'start': 7500.853, 'duration': 2.202}, {'end': 7513.803, 'text': 'so, if I want to see, if I want to get the summary of this data, so I will prepare another table for each of this product.', 'start': 7504.015, 'duration': 9.788}], 'summary': 'Using excel, summarize sales data for four products in separate tables.', 'duration': 36.374, 'max_score': 7477.429, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k7477429.jpg'}, {'end': 7802.531, 'src': 'embed', 'start': 7771.601, 'weight': 8, 'content': [{'end': 7782.403, 'text': "which means that the distribution i'm going to talk about of any variable can be easily characterized by these two values.", 'start': 7771.601, 'duration': 10.802}, {'end': 7794.025, 'text': 'and also these two values help us in identifying the whether the distribution is the required distribution or are there any skewness in the distribution.', 'start': 7782.403, 'duration': 11.622}, {'end': 7802.531, 'text': 'so anyways, so keep in mind that these are very two important parameters of any profile.', 'start': 7794.025, 'duration': 8.506}], 'summary': 'Two values characterize variable distribution and identify skewness.', 'duration': 30.93, 'max_score': 7771.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k7771601.jpg'}, {'end': 7917.752, 'src': 'embed', 'start': 7864.942, 'weight': 9, 'content': [{'end': 7874.507, 'text': 'there are salaries of two companies in san francisco and new york of different different uh designations, like junior software engineer,', 'start': 7864.942, 'duration': 9.565}, {'end': 7878.829, 'text': 'software engineer, senior software engineer, project lead manager and senior manager.', 'start': 7874.507, 'duration': 4.322}, {'end': 7883.092, 'text': 'so these are the salaries of two different companies.', 'start': 7879.77, 'duration': 3.322}, {'end': 7886.315, 'text': 'one is in san francisco, another is in new york.', 'start': 7883.092, 'duration': 3.223}, {'end': 7893.72, 'text': 'now if somebody comes to you and asks you hey, can you just profile this data?', 'start': 7886.315, 'duration': 7.405}, {'end': 7898.083, 'text': 'when you profile, this data means the salaries of these people.', 'start': 7893.72, 'duration': 4.363}, {'end': 7900.004, 'text': 'what do we want?', 'start': 7898.083, 'duration': 1.921}, {'end': 7910.065, 'text': 'the first one is what is the minimum salary of the company that is located in san francisco, which is 5000, and what is the maximum salary,', 'start': 7900.004, 'duration': 10.061}, {'end': 7911.226, 'text': 'which is 55 000?', 'start': 7910.065, 'duration': 1.161}, {'end': 7915.75, 'text': "and it is good that it's already in the ordered one.", 'start': 7911.226, 'duration': 4.524}, {'end': 7917.752, 'text': 'right here the salaries.', 'start': 7915.75, 'duration': 2.002}], 'summary': 'Salaries of various designations in san francisco and new york companies with minimum of $5000 and maximum of $55,000.', 'duration': 52.81, 'max_score': 7864.942, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k7864942.jpg'}, {'end': 8128.141, 'src': 'embed', 'start': 8093.033, 'weight': 11, 'content': [{'end': 8101.275, 'text': 'so i calculated for all of them, and when i sum all these values, the mean absolute deviation is coming out to be 15 000 here,', 'start': 8093.033, 'duration': 8.242}, {'end': 8107.117, 'text': 'whereas for new york it is coming out to be 25 000.', 'start': 8101.275, 'duration': 5.842}, {'end': 8119.398, 'text': 'so which means what exactly this is actually giving gives me that the company in San Francisco, the people who are earning money,', 'start': 8107.117, 'duration': 12.281}, {'end': 8128.141, 'text': 'are very close to the moon, then the people who are in New York, right.', 'start': 8119.398, 'duration': 8.743}], 'summary': 'The mean absolute deviation for san francisco is 15,000, while for new york it is 25,000.', 'duration': 35.108, 'max_score': 8093.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8093033.jpg'}, {'end': 8350.492, 'src': 'embed', 'start': 8325.038, 'weight': 13, 'content': [{'end': 8334.782, 'text': 'so the coefficient of variation, so, which is coming out to be 57 percent, whereas here 83 percent.', 'start': 8325.038, 'duration': 9.744}, {'end': 8341.486, 'text': 'so if you look at these three measures, like mean absolute deviation, variance and coefficient of variation,', 'start': 8334.782, 'duration': 6.704}, {'end': 8350.492, 'text': 'these are all actually capturing almost on the same kind of information, but in a different units.', 'start': 8341.486, 'duration': 9.006}], 'summary': 'Measures like mean absolute deviation, variance, and coefficient of variation capture similar information in different units.', 'duration': 25.454, 'max_score': 8325.038, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8325038.jpg'}, {'end': 8479.914, 'src': 'embed', 'start': 8413.802, 'weight': 12, 'content': [{'end': 8417.564, 'text': 'In fact, I would explain all these concepts with plots as well.', 'start': 8413.802, 'duration': 3.762}, {'end': 8424.83, 'text': 'But this is an example, trying to show you that, whenever you get the data,', 'start': 8418.105, 'duration': 6.725}, {'end': 8431.315, 'text': 'what are the different characteristics that I should consider to describe the data?', 'start': 8424.83, 'duration': 6.485}, {'end': 8433.562, 'text': 'so coefficient of skewness?', 'start': 8432.302, 'duration': 1.26}, {'end': 8436.743, 'text': 'yes, coefficient of skewness is.', 'start': 8433.562, 'duration': 3.181}, {'end': 8438.483, 'text': 'um, is actually this is the.', 'start': 8436.743, 'duration': 1.74}, {'end': 8443.624, 'text': "i'm straightforward, giving you the formula here, which is the mean minus median.", 'start': 8438.483, 'duration': 5.141}, {'end': 8455.267, 'text': 'by standard deviation, whenever mean is equal to median, the coefficient of skewness will become zero.', 'start': 8443.624, 'duration': 11.643}, {'end': 8470.832, 'text': 'and whenever there is that, whenever the coordinate skewness is zero, then you should be very happy that the distribution is not skewed.', 'start': 8455.267, 'duration': 15.565}, {'end': 8479.914, 'text': 'skewness is a property of the data that is centered towards.', 'start': 8470.832, 'duration': 9.082}], 'summary': 'Data characteristics explained, including coefficient of skewness and its formula.', 'duration': 66.112, 'max_score': 8413.802, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8413802.jpg'}], 'start': 6656.727, 'title': 'Statistical analysis in business', 'summary': 'Covers the significance of descriptive and inferential statistics in interpreting demographic trends, population growth analysis, excel data summary, and salary analysis of two companies. it emphasizes the application of statistical measures in solving business problems and highlights the variability in salaries between different locations.', 'chapters': [{'end': 6909.917, 'start': 6656.727, 'title': 'Understanding descriptive and inferential statistics', 'summary': 'Emphasizes the importance of descriptive and inferential statistics in interpreting and drawing conclusions from raw data, and making decisions based on demographic trends, such as urban vs rural population growth.', 'duration': 253.19, 'highlights': ['Descriptive statistics involve the collection, presentation, and description of sample data, used by researchers to report on population and samples. Descriptive statistics help researchers report on population and samples by presenting and describing sample data.', 'Inferential statistics involve making decisions or drawing conclusions from data, such as analyzing demographic trends to make decisions regarding urban vs rural population growth. Inferential statistics are used to draw conclusions and make decisions based on data, such as analyzing demographic trends for decision-making.', 'Using statistics to infer demographic trends can help in making decisions, such as focusing on urban or rural populations based on growth rates and projections. Statistics can assist in making decisions, such as focusing on urban or rural populations based on growth rates and projections.']}, {'end': 7475.226, 'start': 6910.137, 'title': 'Population growth analysis and business problem solving', 'summary': 'Discusses the analysis of population growth velocity and the application of descriptive statistics in solving business problems, emphasizing the need for collecting clean data to draw conclusions and make informed decisions.', 'duration': 565.089, 'highlights': ['The chapter discusses the analysis of population growth velocity by calculating the difference in population numbers over different years to determine the growth rate. Increase in female population growth rate compared to male growth rate.', 'Emphasizes the importance of clean data for descriptive statistics and decision-making, stating that the provided data is free from missing values, enabling more accurate analysis and conclusions. Clean data with no missing values for accurate analysis.', 'Discusses the application of descriptive statistics in understanding sales data for different products over months to make informed business decisions, such as determining optimal prices for maximizing revenue. Application of descriptive statistics in understanding sales data for decision-making.', 'Stresses the need for collecting data to understand customer intentions and maximize sales, highlighting the importance of considering various dimensions in business problem-solving. Importance of collecting data to understand customer intentions and maximize sales.']}, {'end': 7864.942, 'start': 7477.429, 'title': 'Excel data summary', 'summary': 'Outlines how to summarize data in excel, presenting an example with four products and their sales, and emphasizes the importance of mean and median in characterizing the distribution of variables.', 'duration': 387.513, 'highlights': ['The chapter details the process of summarizing data in Excel, using an example of four products and their sales, and emphasizes the significance of mean and median in characterizing the distribution of variables.', 'It explains the specific measures used for summarizing the data, such as minimum sales (220), maximum sales (542), total sales (1753), average sales (350.6), and median sales (311).', 'The speaker highlights the importance of mean and median as key characteristics of any profile, crucial for understanding the distribution and identifying skewness.', 'The chapter indicates that the presented summary is not sufficient and introduces the idea of comparing distributions, emphasizing the need for additional characteristics to characterize the data effectively.']}, {'end': 8262.493, 'start': 7864.942, 'title': 'Salary analysis of two companies', 'summary': 'Discusses the salaries of two companies in san francisco and new york for different designations, where the minimum salary in san francisco is $5000, maximum salary is $55,000, and the mean absolute deviation is 15,000 for san francisco and 25,000 for new york, indicating a variation in salaries between the two locations.', 'duration': 397.551, 'highlights': ['The minimum salary in San Francisco is $5000, the maximum salary is $55,000, and the mean absolute deviation is 15,000 for San Francisco and 25,000 for New York, indicating a variation in salaries between the two locations. Minimum salary, maximum salary, mean absolute deviation, variation in salaries between the two locations', 'The senior software engineer in San Francisco earns higher than in New York for the same designation, while in the project lead designation, New York pays more than San Francisco. Salary comparison for different designations in San Francisco and New York', 'Standard deviation is 17,078 for San Francisco and 25,000 for New York, providing information about the salary distribution in the two organizations. Standard deviation for San Francisco and New York, providing information about salary distribution']}, {'end': 8581.846, 'start': 8263.114, 'title': 'Measures of variation and skewness', 'summary': 'Explains the measures of mean absolute deviation, variance, and coefficient of variation, highlighting their relevance in capturing salary variations, and then delves into the concept of skewness and its impact on data distribution.', 'duration': 318.732, 'highlights': ['The coefficient of skewness is zero when the mean is equal to the median, indicating a symmetrical distribution, while a non-zero coefficient signifies a skewed distribution. The coefficient of skewness measures the symmetry of the distribution, with a zero value indicating a symmetrical distribution and a non-zero value indicating a skewed distribution.', 'Mean absolute deviation, variance, and coefficient of variation provide similar information about salary variations, capturing data properties in different units. Mean absolute deviation, variance, and coefficient of variation offer comparable insights into salary variations, each presenting data properties in distinct units.', 'The chapter introduces the concept of skewness and its impact on data distribution, emphasizing the importance of considering various data characteristics for accurate data description. The chapter emphasizes the significance of understanding the impact of skewness on data distribution and the necessity of considering diverse data characteristics for precise data description.']}], 'duration': 1925.119, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k6656727.jpg', 'highlights': ['Inferential statistics are used to draw conclusions and make decisions based on data, such as analyzing demographic trends for decision-making.', 'Using statistics to infer demographic trends can help in making decisions, such as focusing on urban or rural populations based on growth rates and projections.', 'Descriptive statistics involve the collection, presentation, and description of sample data, used by researchers to report on population and samples.', 'The chapter discusses the analysis of population growth velocity by calculating the difference in population numbers over different years to determine the growth rate.', 'Emphasizes the importance of clean data with no missing values for accurate analysis.', 'Application of descriptive statistics in understanding sales data for decision-making.', 'Stresses the need for collecting data to understand customer intentions and maximize sales.', 'The chapter details the process of summarizing data in Excel, using an example of four products and their sales.', 'The speaker highlights the importance of mean and median as key characteristics of any profile, crucial for understanding the distribution and identifying skewness.', 'The minimum salary in San Francisco is $5000, the maximum salary is $55,000, and the mean absolute deviation is 15,000 for San Francisco and 25,000 for New York, indicating a variation in salaries between the two locations.', 'Salary comparison for different designations in San Francisco and New York.', 'Standard deviation for San Francisco and New York, providing information about salary distribution.', 'The coefficient of skewness measures the symmetry of the distribution, with a zero value indicating a symmetrical distribution and a non-zero value indicating a skewed distribution.', 'Mean absolute deviation, variance, and coefficient of variation offer comparable insights into salary variations, each presenting data properties in distinct units.', 'The chapter emphasizes the significance of understanding the impact of skewness on data distribution and the necessity of considering diverse data characteristics for precise data description.']}, {'end': 9581.946, 'segs': [{'end': 8643.297, 'src': 'embed', 'start': 8614.709, 'weight': 0, 'content': [{'end': 8619.453, 'text': 'i want to just summarize this data by using the descriptive statistics.', 'start': 8614.709, 'duration': 4.744}, {'end': 8623.277, 'text': 'so there, if i am actually going to do that.', 'start': 8619.453, 'duration': 3.824}, {'end': 8625.178, 'text': 'the first thing, summary of the data.', 'start': 8623.277, 'duration': 1.901}, {'end': 8633.145, 'text': 'i would say that number of rows is 100, okay, and number of columns, how many columns do i have?', 'start': 8625.178, 'duration': 7.967}, {'end': 8643.297, 'text': '11? now i want to calculate them mean max, mean median standard deviation and number of unique values.', 'start': 8633.145, 'duration': 10.152}], 'summary': 'Data summary: 100 rows, 11 columns, mean, max, median, std dev, unique values.', 'duration': 28.588, 'max_score': 8614.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8614709.jpg'}, {'end': 8956.913, 'src': 'embed', 'start': 8930.178, 'weight': 2, 'content': [{'end': 8940.103, 'text': "so, out of these variables, what kind of descriptive statistics take methods that I can apply on them, for example, if i'm telling you,", 'start': 8930.178, 'duration': 9.925}, {'end': 8951.51, 'text': "if i'm showing you the data of a product which is having different colors, then i might ask you a question which color is coming out to be the most?", 'start': 8940.103, 'duration': 11.407}, {'end': 8955.813, 'text': 'which color of this product is having the least?', 'start': 8951.51, 'duration': 4.303}, {'end': 8956.913, 'text': "i don't ask you that.", 'start': 8955.813, 'duration': 1.1}], 'summary': 'Descriptive statistics methods for analyzing product colors data.', 'duration': 26.735, 'max_score': 8930.178, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8930178.jpg'}, {'end': 9222.939, 'src': 'embed', 'start': 9193.495, 'weight': 1, 'content': [{'end': 9203.745, 'text': 'that shows that what is the nature of the nature of the or the kind of grading that it has been provided by the customer.', 'start': 9193.495, 'duration': 10.25}, {'end': 9213.572, 'text': 'alright. so my point here is that whenever you get the data on this data, first build this hierarchy.', 'start': 9204.546, 'duration': 9.026}, {'end': 9222.939, 'text': 'this is the first and fundamental rule that you should follow and that gives you very clear picture that what are my variables that I am having?', 'start': 9213.572, 'duration': 9.367}], 'summary': 'Build hierarchy for data analysis to understand variables clearly', 'duration': 29.444, 'max_score': 9193.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k9193495.jpg'}], 'start': 8582.668, 'title': 'Descriptive statistics and variable types', 'summary': 'Covers the importance of descriptive statistics for data summary, including mean, median, standard deviation, and unique values calculation for a dataset of 100 rows and 11 columns, and explains the types of variables in data analysis, such as quantitative, qualitative, discrete, and continuous, with examples and their implications.', 'chapters': [{'end': 8643.297, 'start': 8582.668, 'title': 'Applying descriptive statistics for data summary', 'summary': 'Discusses the importance of obtaining a summary of experimental study data, emphasizing the use of descriptive statistics to calculate the mean, median, standard deviation, and number of unique values for a dataset containing 100 rows and 11 columns.', 'duration': 60.629, 'highlights': ['Obtaining a summary of experimental study data is crucial before handling it, regardless of the data type.', 'Using descriptive statistics to calculate mean, median, standard deviation, and number of unique values for a dataset with 100 rows and 11 columns is essential.', 'The dataset contains 100 rows and 11 columns.']}, {'end': 9097.444, 'start': 8643.297, 'title': 'Types of variables in data analysis', 'summary': 'Explains the types of variables in data analysis, including quantitative and qualitative variables, discrete and continuous variables, with examples and their implications on statistical analysis.', 'duration': 454.147, 'highlights': ['The chapter explains the concept of quantitative and qualitative variables, with examples such as product colors and stock prices, and their implications on applying descriptive statistics methods.', 'The distinction between discrete and continuous variables is outlined, with examples such as number of licenses and product values, and their implications on data hierarchy and arithmetic operations.', 'The concept of nominal variables is discussed, with examples such as gender and academic grades, and their implications on relationships and grading systems.']}, {'end': 9581.946, 'start': 9097.444, 'title': 'Understanding variables and data summary', 'summary': 'Discusses the nature of qualitative and quantitative variables, emphasizing the importance of building a hierarchy of variables to gain a clear understanding of the data, and highlights the process of data summary and analysis, focusing on mean, median, dispersion, and visualization for effective communication and reference throughout statistical studies.', 'duration': 484.502, 'highlights': ['The importance of building a hierarchy of variables is emphasized to gain a clear picture of the data. Emphasizes the need to categorize qualitative, nominal, discrete, and continuous variables to understand the nature of the data.', 'Data summary involves analyzing mean, median, dispersion, and visualization for effective communication and reference throughout statistical studies. Focuses on analyzing mean, median, dispersion, and visualization to effectively communicate and reference throughout statistical studies.', 'Introduction of the concept of data dictionary for complete information on data variables, their distributions, skewness, and percentiles. Introduces the concept of a data dictionary to provide complete information on data variables, their distributions, skewness, and percentiles.']}], 'duration': 999.278, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k8582668.jpg', 'highlights': ['Using descriptive statistics to calculate mean, median, standard deviation, and number of unique values for a dataset with 100 rows and 11 columns is essential.', 'The importance of building a hierarchy of variables is emphasized to gain a clear picture of the data. Emphasizes the need to categorize qualitative, nominal, discrete, and continuous variables to understand the nature of the data.', 'The chapter explains the concept of quantitative and qualitative variables, with examples such as product colors and stock prices, and their implications on applying descriptive statistics methods.']}, {'end': 10940.657, 'segs': [{'end': 9613.394, 'src': 'embed', 'start': 9581.946, 'weight': 0, 'content': [{'end': 9584.188, 'text': 'so what are the applications of this day?', 'start': 9581.946, 'duration': 2.242}, {'end': 9591.255, 'text': 'where exactly this data dictionary will be useful if you have this data dictionary in handy in hand?', 'start': 9584.188, 'duration': 7.067}, {'end': 9597.721, 'text': 'so in the future, when you are going to build a machine learning model, or you are going to date,', 'start': 9591.255, 'duration': 6.466}, {'end': 9606.207, 'text': 'infer some kind of rules out of it or you are going to take decisions in those places.', 'start': 9597.721, 'duration': 8.486}, {'end': 9613.394, 'text': 'these values would help you to see that whether your decision that you are actually taking is valid or not.', 'start': 9606.207, 'duration': 7.187}], 'summary': 'Data dictionary aids decision-making in ml models by validating decisions with values.', 'duration': 31.448, 'max_score': 9581.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k9581946.jpg'}, {'end': 9918.111, 'src': 'embed', 'start': 9891.6, 'weight': 1, 'content': [{'end': 9898.063, 'text': 'right, so we are going to cover what are those different types of probabilities in the later session.', 'start': 9891.6, 'duration': 6.463}, {'end': 9900.244, 'text': 'okay, there are subject to probability.', 'start': 9898.063, 'duration': 2.181}, {'end': 9904.586, 'text': "okay, it is a person's belief, empirical probability, which is nothing.", 'start': 9900.244, 'duration': 4.342}, {'end': 9907.847, 'text': 'but he takes the data and talks about it.', 'start': 9904.586, 'duration': 3.261}, {'end': 9909.048, 'text': 'and then theoretical probability.', 'start': 9907.847, 'duration': 1.201}, {'end': 9918.111, 'text': 'theoretical probability is mostly, like i mentioned, right, whatever, the high school standards will learn all the theoretical probabilities, okay,', 'start': 9909.628, 'duration': 8.483}], 'summary': 'The session covers subjective, empirical, and theoretical probabilities, with a focus on high school standards.', 'duration': 26.511, 'max_score': 9891.6, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k9891600.jpg'}, {'end': 10617.016, 'src': 'embed', 'start': 10590.688, 'weight': 3, 'content': [{'end': 10598.551, 'text': 'Why is it that R is an important and a useful resource for all of us? There are several reasons for it.', 'start': 10590.688, 'duration': 7.863}, {'end': 10606.993, 'text': "I can just briefly talk about why R studio or R is very popular and it's very useful.", 'start': 10599.031, 'duration': 7.962}, {'end': 10611.514, 'text': "The first reason, of course, is that it's a free open source platform.", 'start': 10608.113, 'duration': 3.401}, {'end': 10617.016, 'text': "It's available under an open source license, which means anyone can download and modify the code.", 'start': 10612.195, 'duration': 4.821}], 'summary': 'R is popular for its open source platform, allowing free access and code modification.', 'duration': 26.328, 'max_score': 10590.688, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k10590688.jpg'}], 'start': 9581.946, 'title': 'Data, probability, and r programming', 'summary': 'Discusses data dictionary applications in decision-making and machine learning, probability basics including different types and sample space, and the benefits of r programming including open-source nature and extensive library of packages.', 'chapters': [{'end': 9686.134, 'start': 9581.946, 'title': 'Data dictionary applications and decision making', 'summary': 'Discusses the applications of a data dictionary in decision-making, machine learning model building, and providing insights for inventory cost predictions and customer behavior analysis.', 'duration': 104.188, 'highlights': ['The data dictionary can be used to make decisions in machine learning model building, inventory cost predictions, and customer behavior analysis, providing insights for better decision-making.', "The data dictionary can provide answers to questions like 'who is more interested in visiting my shop?', 'what will be my inventory cost for the next festival?', and 'what will be the airfare during the summer season?'", "The data dictionary can help in capturing customer behaviors and providing insights into inventory cost predictions, with an example of predicting an inventory cost of 'two hundred thousand dollars'."]}, {'end': 10278.44, 'start': 9687.093, 'title': 'Understanding probability basics', 'summary': 'Covers the basics of probability, including different types such as subject to, empirical, and theoretical probability, with examples and explanations, emphasizing the importance of defining sample space and events when solving probability problems.', 'duration': 591.347, 'highlights': ['Different Types of Probability The transcript discusses different types of probability, including subject to, empirical, and theoretical, providing examples and explanations to understand their distinctions and applications.', 'Importance of Defining Sample Space and Events The importance of defining sample space and events when solving probability problems is emphasized, with examples such as airfare during the summer season and share prices of Facebook, highlighting the significance of properly defining the problem.', 'Relevance of Probability in Problem Solving The transcript stresses the relevance of probability in problem-solving across different industries and the necessity of formulating problems in probabilistic terms to achieve meaningful results, cautioning against solving problems without understanding them in those terms.']}, {'end': 10565.807, 'start': 10278.44, 'title': 'Understanding probability in decision making', 'summary': 'Discusses the importance of understanding subjective, empirical, and theoretical probability in making informed decisions, emphasizing the relevance of personal beliefs, empirical data, and theoretical calculations in various scenarios, such as project evaluation and sports predictions.', 'duration': 287.367, 'highlights': ['Theoretical probability vs. empirical probability Distinguishes between theoretical probability, based on theory and possible combinations, and empirical probability, derived from collected data and real values, with emphasis on the relevance to decision making.', "Subjective probability in industry decisions Explains the use of subjective probabilities in industry decisions, where subject matter experts' experience and empirical data are combined to provide powerful insights for business decisions.", "Project evaluation based on subjective probability Illustrates the decision-making process for project evaluation, where personal belief in the project's success, despite low success probability, is highlighted, emphasizing the importance of subjective probabilities in decision making."]}, {'end': 10940.657, 'start': 10565.887, 'title': 'Benefits of r programming', 'summary': 'Discusses the advantages of using r programming, such as its open-source nature, compatibility with various systems, extensive library of packages, and active community support.', 'duration': 374.77, 'highlights': ['R is a free open source platform, available under an open source license, allowing anyone to download and modify the code, making it a cost-effective option for building applications and web applications. R is a free open source platform, allowing anyone to download and modify the code.', 'R runs on different types of hardwares and softwares, making it compatible with all operating systems, and supports a wide variety of extensions for data manipulation, statistical modeling, and graphics. R runs on different types of hardwares and softwares, making it compatible with all operating systems, and supports a wide variety of extensions.', 'Developers can easily write their own software and distribute it in the form of an add-on package, leading to the existence of thousands of such packages in the market, reducing the effort and time required for coding from scratch. Developers can easily write and distribute their own software, leading to the existence of thousands of packages in the market.', 'R can connect with SQL, a lot of databases, and provides a very engaged community, allowing users to easily seek and find solutions to problems, making it an advantageous platform for data science and AI. R can connect with SQL, a lot of databases, and provides a very engaged community, allowing users to easily seek and find solutions to problems.']}], 'duration': 1358.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k9581946.jpg', 'highlights': ['The data dictionary can be used to make decisions in machine learning model building, inventory cost predictions, and customer behavior analysis, providing insights for better decision-making.', 'Different Types of Probability The transcript discusses different types of probability, including subject to, empirical, and theoretical, providing examples and explanations to understand their distinctions and applications.', 'Theoretical probability vs. empirical probability Distinguishes between theoretical probability, based on theory and possible combinations, and empirical probability, derived from collected data and real values, with emphasis on the relevance to decision making.', 'R is a free open source platform, available under an open source license, allowing anyone to download and modify the code, making it a cost-effective option for building applications and web applications. R is a free open source platform, allowing anyone to download and modify the code.']}, {'end': 12403.037, 'segs': [{'end': 11096.412, 'src': 'embed', 'start': 11025.754, 'weight': 0, 'content': [{'end': 11032.861, 'text': "so on the same, on the same software, on the same platform, you're able to write your queries, you're able to see how they're executed.", 'start': 11025.754, 'duration': 7.107}, {'end': 11035.643, 'text': "if there are any errors, you're able to see it in front of you.", 'start': 11033.201, 'duration': 2.442}, {'end': 11042.346, 'text': 'all the objects that you created, that you manipulated, are also in front of you, and any output right in the form of charts, visualizations,', 'start': 11035.643, 'duration': 6.703}, {'end': 11096.412, 'text': 'is also available to you in the R studio itself, right.', 'start': 11042.346, 'duration': 54.066}], 'summary': 'R studio allows users to write queries, view execution, and access visualizations in one platform.', 'duration': 70.658, 'max_score': 11025.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k11025754.jpg'}, {'end': 11162.296, 'src': 'embed', 'start': 11133.945, 'weight': 1, 'content': [{'end': 11137.548, 'text': 'okay, so you will need both the softwares to be present on your machine.', 'start': 11133.945, 'duration': 3.603}, {'end': 11139.35, 'text': 'only then you can build programs.', 'start': 11137.548, 'duration': 1.802}, {'end': 11140.651, 'text': 'only then you can build out reports.', 'start': 11139.35, 'duration': 1.301}, {'end': 11141.388, 'text': 'got it.', 'start': 11140.987, 'duration': 0.401}, {'end': 11144.853, 'text': "so r studio is essentially the interface right which you'll be using.", 'start': 11141.388, 'duration': 3.465}, {'end': 11145.514, 'text': "you wouldn't?", 'start': 11144.853, 'duration': 0.661}, {'end': 11153.267, 'text': 'you would probably never even go and try to open your r, this, this, uh, this software right.', 'start': 11145.514, 'duration': 7.753}, {'end': 11155.973, 'text': 'so If I press R, there are two softwares that you see right?', 'start': 11153.267, 'duration': 2.706}, {'end': 11157.594, 'text': "This R and there's this RStudio.", 'start': 11156.033, 'duration': 1.561}, {'end': 11162.296, 'text': 'R. this one right, which is just R, is essentially the base programming package.', 'start': 11157.734, 'duration': 4.562}], 'summary': 'R and rstudio are both required to build programs and reports, with rstudio serving as the primary interface.', 'duration': 28.351, 'max_score': 11133.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k11133945.jpg'}, {'end': 11556.146, 'src': 'embed', 'start': 11528.892, 'weight': 3, 'content': [{'end': 11535.135, 'text': 'Any program, any code that I write today could be of use for me tomorrow or day after tomorrow or next week.', 'start': 11528.892, 'duration': 6.243}, {'end': 11537.957, 'text': 'Also, I want to make sure I keep building on top of it.', 'start': 11535.776, 'duration': 2.181}, {'end': 11543.56, 'text': "It's important that you're able to save your codes and it's important that you're also documenting your codes well.", 'start': 11538.657, 'duration': 4.903}, {'end': 11547.221, 'text': 'This is the window where you will write all your codes.', 'start': 11544.34, 'duration': 2.881}, {'end': 11551.904, 'text': "This bit, that's when I will refer back to your questions and try and answer them.", 'start': 11547.922, 'duration': 3.982}, {'end': 11556.146, 'text': 'The next window that you have is called the console window, this window.', 'start': 11553.044, 'duration': 3.102}], 'summary': 'Importance of saving and documenting code for future use and building upon it.', 'duration': 27.254, 'max_score': 11528.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k11528892.jpg'}, {'end': 11646.944, 'src': 'embed', 'start': 11623.374, 'weight': 4, 'content': [{'end': 11630.377, 'text': 'right. so only if a code deserves an output, only if your code is expecting an output, it will show up in your script in your console.', 'start': 11623.374, 'duration': 7.003}, {'end': 11632.518, 'text': "okay, so that's the purpose of the console.", 'start': 11630.377, 'duration': 2.141}, {'end': 11638.62, 'text': 'another thing that your console does is that it shows you any errors or any bugs that your code is generating.', 'start': 11632.518, 'duration': 6.102}, {'end': 11646.944, 'text': "okay, for example, if i write buggy code, let's say, if i write something like this, okay, now, this thing that i've written is not a valid syntax.", 'start': 11638.62, 'duration': 8.324}], 'summary': 'Console displays code output and errors, aiding in debugging.', 'duration': 23.57, 'max_score': 11623.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k11623374.jpg'}, {'end': 11866.853, 'src': 'embed', 'start': 11828.043, 'weight': 5, 'content': [{'end': 11831.264, 'text': 'right, and you can have multiple R scripts open at the same time.', 'start': 11828.043, 'duration': 3.221}, {'end': 11833.304, 'text': 'right, you have different tabs in Excel.', 'start': 11831.264, 'duration': 2.04}, {'end': 11835.745, 'text': 'you can have different script files open as well.', 'start': 11833.304, 'duration': 2.441}, {'end': 11837.745, 'text': 'okay, so this is the environment window.', 'start': 11836.044, 'duration': 1.701}, {'end': 11844.331, 'text': "right, uh, it will show up all the objects, all the, all the variables, all the uh functions that you're creating.", 'start': 11837.745, 'duration': 6.586}, {'end': 11847.513, 'text': 'right, anything that you have created will show up in your environment.', 'start': 11844.331, 'duration': 3.182}, {'end': 11850.115, 'text': 'okay, it also has something which is called the history window.', 'start': 11847.513, 'duration': 2.602}, {'end': 11853.918, 'text': "right, history is is like it's import.", 'start': 11850.115, 'duration': 3.803}, {'end': 11855.219, 'text': "i mean i don't know if it's important.", 'start': 11853.918, 'duration': 1.301}, {'end': 11863.631, 'text': 'i have not used history a lot, but what it stores is essentially all the codes right, all the executed codes right, all the executed programs,', 'start': 11855.219, 'duration': 8.412}, {'end': 11866.853, 'text': 'all the executed scripts will show up in your history.', 'start': 11863.631, 'duration': 3.222}], 'summary': 'The environment window in r displays objects, variables, and functions, while the history window stores executed codes and programs.', 'duration': 38.81, 'max_score': 11828.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k11828043.jpg'}, {'end': 11968.005, 'src': 'embed', 'start': 11936.949, 'weight': 7, 'content': [{'end': 11942.912, 'text': "it's actually one of the most important tabs in in our studio is basically this plots window, or the packages window.", 'start': 11936.949, 'duration': 5.963}, {'end': 11949.335, 'text': 'There are multiple tabs in this window and each of them are very important.', 'start': 11943.292, 'duration': 6.043}, {'end': 11951.756, 'text': 'For example, you have something called as a plots window.', 'start': 11949.355, 'duration': 2.401}, {'end': 11953.377, 'text': 'Something called as a plots window.', 'start': 11952.157, 'duration': 1.22}, {'end': 11958.24, 'text': 'Any visualization that you create, any chart that you create will show up in the plots window.', 'start': 11953.697, 'duration': 4.543}, {'end': 11968.005, 'text': "I can give you an example of that, but I'm just trying to show you how a plot gets populated in the, let's say, in your R script,", 'start': 11958.64, 'duration': 9.365}], 'summary': 'The plots window in our studio is important, used for visualizations and charts from r script.', 'duration': 31.056, 'max_score': 11936.949, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k11936949.jpg'}, {'end': 12047.504, 'src': 'embed', 'start': 12019.583, 'weight': 8, 'content': [{'end': 12021.905, 'text': 'okay, then you have something called as the packages window.', 'start': 12019.583, 'duration': 2.322}, {'end': 12024.307, 'text': "right, it's, this is a very important window, guys.", 'start': 12021.905, 'duration': 2.402}, {'end': 12029.852, 'text': 'okay, uh, when you work in r studio, you will basically leverage several packages, several libraries.', 'start': 12024.307, 'duration': 5.545}, {'end': 12031.754, 'text': 'okay, some of them native to r.', 'start': 12029.852, 'duration': 1.902}, {'end': 12039.119, 'text': "okay, some of them native to the r software, that you don't wrote it, okay, but some of them that you have, let's say, installed from outside.", 'start': 12031.754, 'duration': 7.365}, {'end': 12044.543, 'text': "okay, so from when i say outside, it doesn't mean you you've installed it from from like.", 'start': 12039.119, 'duration': 5.424}, {'end': 12047.504, 'text': "it's still essentially an r program.", 'start': 12044.543, 'duration': 2.961}], 'summary': 'In r studio, leveraging various packages and libraries is important for programming, including both native and externally installed ones.', 'duration': 27.921, 'max_score': 12019.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k12019583.jpg'}, {'end': 12180.525, 'src': 'embed', 'start': 12154.829, 'weight': 9, 'content': [{'end': 12159.25, 'text': 'okay, so you can install packages, you can update packages, right you can.', 'start': 12154.829, 'duration': 4.421}, {'end': 12161.59, 'text': 'uh, you can keep referring to your packages from here.', 'start': 12159.25, 'duration': 2.34}, {'end': 12163.431, 'text': 'okay, so packages are important.', 'start': 12161.59, 'duration': 1.841}, {'end': 12167.752, 'text': 'and then the last tab, which is important for all of us here is the help window.', 'start': 12163.431, 'duration': 4.321}, {'end': 12170.68, 'text': 'Help window is very, very important for all of us.', 'start': 12168.599, 'duration': 2.081}, {'end': 12176.483, 'text': 'The reason for that is because we are starting out learning about R or RStudio.', 'start': 12171.18, 'duration': 5.303}, {'end': 12180.525, 'text': "It's very, very likely that we will be stuck with things.", 'start': 12176.583, 'duration': 3.942}], 'summary': 'Installing, updating, and referencing packages in rstudio are important. the help window is crucial for beginners in r or rstudio.', 'duration': 25.696, 'max_score': 12154.829, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k12154829.jpg'}, {'end': 12403.037, 'src': 'embed', 'start': 12349.966, 'weight': 2, 'content': [{'end': 12354.929, 'text': 'I would never recommend you to spend energy, to spend time on just remembering syntaxes.', 'start': 12349.966, 'duration': 4.963}, {'end': 12361.275, 'text': "okay, because that's the last thing, which is which you would want to spend your time on right, because all of that is already available to you,", 'start': 12354.929, 'duration': 6.346}, {'end': 12362.657, 'text': "right. it's very, very easily.", 'start': 12361.275, 'duration': 1.382}, {'end': 12366.702, 'text': "it's very, very frequently available, either on on the help window or even on the web.", 'start': 12362.657, 'duration': 4.045}, {'end': 12370.948, 'text': "right. if you google for a function the syntax of the function, it's it's.", 'start': 12366.702, 'duration': 4.246}, {'end': 12375.034, 'text': "it's probably the the first search output that you would get all right.", 'start': 12370.948, 'duration': 4.086}, {'end': 12377.74, 'text': 'So never spend time in remembering syntaxes.', 'start': 12375.618, 'duration': 2.122}, {'end': 12383.083, 'text': 'Rather, just try and understand how a particular function would work and where you could possibly use it.', 'start': 12377.76, 'duration': 5.323}, {'end': 12387.466, 'text': "So that's about it in terms of the whole interface.", 'start': 12383.443, 'duration': 4.023}, {'end': 12392.95, 'text': "These four tabs, what is the purpose of each of them? That's the whole interface of RStudio.", 'start': 12387.526, 'duration': 5.424}, {'end': 12394.651, 'text': "That's the purpose of each of these panes.", 'start': 12393.01, 'duration': 1.641}, {'end': 12399.534, 'text': 'Now we would start going deep into understanding how programming works.', 'start': 12395.111, 'duration': 4.423}, {'end': 12403.037, 'text': 'How would you perform certain operations, certain manipulations in R, in RStudio?', 'start': 12399.554, 'duration': 3.483}], 'summary': "Avoid spending time on remembering syntaxes, focus on understanding functions and their application. exploring rstudio's interface and delving into programming operations.", 'duration': 53.071, 'max_score': 12349.966, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k12349966.jpg'}], 'start': 10941.578, 'title': 'Efficient use of functions in r', 'summary': "Discusses r studio interface, scripting and console usage, environment window functionality, rstudio environment overview, and emphasizes efficient use of functions in r, covering the significance, functions, and usage details of rstudio's interface and environment.", 'chapters': [{'end': 11338.023, 'start': 10941.578, 'title': 'Understanding r studio interface', 'summary': 'Discusses the significance of r studio interface, the need for both r and r studio softwares, and the functions of different panes in r studio, including source, console, environment, and plots.', 'duration': 396.445, 'highlights': ["R Studio is an IDE that provides a user-friendly interface for writing codes, executing queries, and visualizing outputs, making the coder's life easier. R Studio provides a user-friendly interface for writing codes, executing queries, and visualizing outputs, making the coder's life easier.", 'The need for both R and R Studio softwares is explained, emphasizing that R Studio fetches outputs from the base software, and both are essential for building programs and reports. Both R and R Studio softwares are essential for building programs and reports, with R Studio fetching outputs from the base software.', 'The functions of different panes in R Studio are explained, including the source window for scripts, console for executing commands, environment for storing objects, and plots window for visualizing data. Different panes in R Studio, such as source window for scripts, console for executing commands, environment for storing objects, and plots window for visualizing data, are explained.']}, {'end': 11695.737, 'start': 11338.023, 'title': 'Rstudio: scripting and console', 'summary': "Explains the usage of rstudio's script and console windows for writing, executing, and saving code, with details on code execution methods, saving scripts, importance of saving codes, and the purpose of the console window.", 'duration': 357.714, 'highlights': ['The purpose of the run button is to execute your code, and pressing Control+Enter can also execute the code, transferring it to the console window. Explanation of code execution methods using the run button and Control+Enter.', "The option to save scripts is available, and it's essential for making codes repeatable and building on them, emphasizing the importance of saving and documenting codes. Importance of saving scripts for code reusability, future use, and documentation.", 'The console window in RStudio shows all the outputs and errors generated by the code, being a critical tool for code validation and debugging. Explanation of the purpose of the console window for displaying outputs, errors, and code validation.']}, {'end': 11902.937, 'start': 11695.977, 'title': 'Understanding rstudio environment window', 'summary': "Explains the functionality of the rstudio environment window, which displays all created objects, variables, and functions, allowing for their reuse across multiple scripts within the same session, while also highlighting the history window's role in storing executed codes.", 'duration': 206.96, 'highlights': ['The environment window in RStudio displays all generated objects, variables, and functions, facilitating their reuse across multiple scripts within the same session. It gives a summary of all the objects or functions, variables created, and stored for future repetitions.', 'The history window in RStudio stores all executed codes and programs, allowing access to the code history even after clearing the console. It stores all the executed codes, programs, and scripts, enabling access to the code history even after clearing the console.', 'New R script windows can be opened within RStudio to work on multiple projects simultaneously, utilizing the common environment for leveraging the same set of objects across different programs. It facilitates the opening of multiple R script windows, enabling the use of the same set of objects across different programs within the common environment.']}, {'end': 12176.483, 'start': 11902.937, 'title': 'Rstudio environment overview', 'summary': 'Introduces the rstudio environment, highlighting the key features such as history, connections, environment window, plots window, and packages window, emphasizing the importance of visualizations and the use of packages in rstudio.', 'duration': 273.546, 'highlights': ['The plots window is crucial for visualizations, allowing the generation and export of charts and plots, providing the ability to zoom, and supporting export options such as images, PDFs, and copying to other documents. The plots window is essential for visualizations, enabling the creation and export of charts and plots, with zooming capabilities and export options including images, PDFs, and copying to other documents.', 'The packages window is vital for leveraging R packages and libraries, enabling the installation, updating, and management of packages, with the example of installing the ggplot2 package for custom visualizations. The packages window plays a crucial role in utilizing R packages and libraries, facilitating the installation, updating, and management of packages, demonstrated through the installation of the ggplot2 package for custom visualizations.', 'The help window is emphasized as essential for beginners in learning R or RStudio, providing valuable support and guidance. The help window is highlighted as crucial for beginners learning R or RStudio, offering valuable support and guidance.']}, {'end': 12403.037, 'start': 12176.583, 'title': 'Efficient use of functions in r', 'summary': 'Emphasizes the importance of not memorizing syntaxes of functions in r, as they can be easily accessed through the help window or by googling, and instead focuses on understanding the purpose and usage of each function.', 'duration': 226.454, 'highlights': ['The importance of not memorizing syntaxes of functions in R Emphasizes the inefficiency of spending time and energy on memorizing syntaxes of functions, as they are easily accessible through the help window or by Googling.', 'Easy access to function syntaxes through the help window or Google The help window and Google provide reliable and frequently available resources for accessing function syntaxes, including examples and arguments.', 'Focus on understanding the purpose and usage of functions Encourages understanding the purpose and usage of functions rather than memorizing syntaxes, as it is more efficient and practical in programming.']}], 'duration': 1461.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k10941578.jpg', 'highlights': ["R Studio provides a user-friendly interface for writing codes, executing queries, and visualizing outputs, making the coder's life easier.", 'Both R and R Studio softwares are essential for building programs and reports, with R Studio fetching outputs from the base software.', 'Different panes in R Studio, such as source window for scripts, console for executing commands, environment for storing objects, and plots window for visualizing data, are explained.', 'Importance of saving scripts for code reusability, future use, and documentation.', 'Explanation of the purpose of the console window for displaying outputs, errors, and code validation.', 'The environment window in RStudio displays all generated objects, variables, and functions, facilitating their reuse across multiple scripts within the same session.', 'The history window in RStudio stores all executed codes, programs, and scripts, enabling access to the code history even after clearing the console.', 'The plots window is essential for visualizations, enabling the creation and export of charts and plots, with zooming capabilities and export options including images, PDFs, and copying to other documents.', 'The packages window plays a crucial role in utilizing R packages and libraries, facilitating the installation, updating, and management of packages, demonstrated through the installation of the ggplot2 package for custom visualizations.', 'The help window is highlighted as crucial for beginners learning R or RStudio, offering valuable support and guidance.', 'Emphasizes the inefficiency of spending time and energy on memorizing syntaxes of functions, as they are easily accessible through the help window or by Googling.', 'The help window and Google provide reliable and frequently available resources for accessing function syntaxes, including examples and arguments.', 'Encourages understanding the purpose and usage of functions rather than memorizing syntaxes, as it is more efficient and practical in programming.']}, {'end': 13754.443, 'segs': [{'end': 12476.683, 'src': 'embed', 'start': 12450.161, 'weight': 0, 'content': [{'end': 12457.686, 'text': 'you generally decide to do that okay, by creating something which is called as a variable right.', 'start': 12450.161, 'duration': 7.525}, {'end': 12466.453, 'text': 'a variable is that temporary storage space in any programming language right, whose value can be changed right based on your convenience, for example,', 'start': 12457.686, 'duration': 8.767}, {'end': 12469.899, 'text': 'For example, I want to create an object.', 'start': 12467.538, 'duration': 2.361}, {'end': 12473.862, 'text': 'I want to create a variable where I store the price of Apple.', 'start': 12469.959, 'duration': 3.903}, {'end': 12476.683, 'text': 'I want to store the price of Apple.', 'start': 12475.382, 'duration': 1.301}], 'summary': 'Creating and using variables in programming allows for dynamic value storage and manipulation.', 'duration': 26.522, 'max_score': 12450.161, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k12450161.jpg'}, {'end': 12768.562, 'src': 'embed', 'start': 12737.281, 'weight': 1, 'content': [{'end': 12738.762, 'text': "Now let's continue with the session.", 'start': 12737.281, 'duration': 1.481}, {'end': 12747.747, 'text': 'okay, a variable which is generally used to store information which is binary, true or false, yes or no, one or zero, right.', 'start': 12740.362, 'duration': 7.385}, {'end': 12750.769, 'text': 'the data type that you assign to it is logical data type.', 'start': 12747.747, 'duration': 3.022}, {'end': 12755.012, 'text': 'okay. the third kind of data type, and then the fourth data type, is essentially complex data type.', 'start': 12750.769, 'duration': 4.243}, {'end': 12762.057, 'text': "when you're storing a complex number right, in the form of where you have a principal component and imaginary component right,", 'start': 12755.012, 'duration': 7.045}, {'end': 12768.562, 'text': '2 plus 3 at 30 plus 2 at 30 minus 2 at such numbers you would store as a data type which is called complex.', 'start': 12762.057, 'duration': 6.505}], 'summary': 'Introduction to logical and complex data types in programming.', 'duration': 31.281, 'max_score': 12737.281, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k12737281.jpg'}, {'end': 13082.466, 'src': 'embed', 'start': 13050.421, 'weight': 2, 'content': [{'end': 13051.561, 'text': 'this is an equal to operator.', 'start': 13050.421, 'duration': 1.14}, {'end': 13053.482, 'text': 'this is a left foot assignment operator.', 'start': 13051.561, 'duration': 1.921}, {'end': 13059.863, 'text': 'but the most important one, or at least at least 80 to 90 percent of the statisticians or data scientists use this right.', 'start': 13053.482, 'duration': 6.381}, {'end': 13062.504, 'text': 'this is the most commonly used assignment operator.', 'start': 13059.863, 'duration': 2.641}, {'end': 13067.565, 'text': "okay, so i'm just going to zoom in and show you how you can use a left-foot assignment operator for performing an operation.", 'start': 13062.504, 'duration': 5.061}, {'end': 13068.546, 'text': 'okay, cool.', 'start': 13067.565, 'duration': 0.981}, {'end': 13069.646, 'text': 'so the first thing, by the way.', 'start': 13068.546, 'duration': 1.1}, {'end': 13073.541, 'text': 'uh, if you want to comment out anything in your script window right, if you want to,', 'start': 13069.646, 'duration': 3.895}, {'end': 13082.466, 'text': "if you write something but you don't want to be wanted to be part of your execution, you can just comment it by using a hashtag right.", 'start': 13073.541, 'duration': 8.925}], 'summary': 'Most statisticians and data scientists use the right-foot assignment operator, which is the most commonly used.', 'duration': 32.045, 'max_score': 13050.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13050421.jpg'}, {'end': 13115.411, 'src': 'embed', 'start': 13090.711, 'weight': 3, 'content': [{'end': 13096.835, 'text': 'it just means that anything that you that has started with hashtag will be excluded from your operation.', 'start': 13090.711, 'duration': 6.124}, {'end': 13101.925, 'text': 'okay, i will show you later when how you could, how you could comment multiple lines of code.', 'start': 13096.835, 'duration': 5.09}, {'end': 13105.366, 'text': 'Okay, but for now, just use a hashtag.', 'start': 13102.465, 'duration': 2.901}, {'end': 13110.569, 'text': "Okay, so I'm going to show you how you can create variables using a left-foot assignment operator.", 'start': 13106.427, 'duration': 4.142}, {'end': 13115.411, 'text': "Let's say, first of all, I'm going to clear my environment, right, because it has a bunch of values.", 'start': 13111.209, 'duration': 4.202}], 'summary': 'Excluding lines starting with hashtag, commenting multiple lines with hashtag, creating variables using left-foot assignment operator', 'duration': 24.7, 'max_score': 13090.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13090711.jpg'}, {'end': 13286.281, 'src': 'embed', 'start': 13262.899, 'weight': 4, 'content': [{'end': 13269.783, 'text': "but it's a common mistake to even to forget that assignment that the relational operator has double equal twos or not a single equal to right,", 'start': 13262.899, 'duration': 6.884}, {'end': 13271.484, 'text': "and for those reasons it's avoided right.", 'start': 13269.783, 'duration': 1.701}, {'end': 13273.985, 'text': 'generally, the best practice is to avoid an equal to operator.', 'start': 13271.484, 'duration': 2.501}, {'end': 13276.746, 'text': 'rather, use something which is called the leftward assignment operator.', 'start': 13273.985, 'duration': 2.761}, {'end': 13278.687, 'text': 'okay, so this is how you create a variable.', 'start': 13276.746, 'duration': 1.941}, {'end': 13282.029, 'text': 'right, uh, you can also use a right word assignment, and this is how you would do it right.', 'start': 13278.687, 'duration': 3.342}, {'end': 13286.281, 'text': 'if you want to write the name of the variable, you want to write the value first 10, Okay,', 'start': 13282.029, 'duration': 4.252}], 'summary': 'Best practice is to avoid equal to operator and use leftward assignment operator for creating variables.', 'duration': 23.382, 'max_score': 13262.899, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13262899.jpg'}, {'end': 13372.902, 'src': 'embed', 'start': 13341.933, 'weight': 5, 'content': [{'end': 13346.117, 'text': 'the other thing, by the way, is that our is extremely case sensitive.', 'start': 13341.933, 'duration': 4.184}, {'end': 13347.962, 'text': 'okay, What do I mean by case sensitive??', 'start': 13346.117, 'duration': 1.845}, {'end': 13352.186, 'text': 'It means that it is able to distinguish between lowercase or uppercase.', 'start': 13348.022, 'duration': 4.164}, {'end': 13357.91, 'text': 'If you write something in capital letters and then you write something in small letters, R will treat them as two different values.', 'start': 13352.226, 'duration': 5.684}, {'end': 13359.431, 'text': "I'll show it to you.", 'start': 13358.511, 'duration': 0.92}, {'end': 13364.896, 'text': 'For example, I created this variable called my first var underscore R.', 'start': 13359.451, 'duration': 5.445}, {'end': 13372.902, 'text': "Let's say I create another variable which has similar notation, absolutely similar notation, but instead of a small m, I just write a capital M.", 'start': 13364.896, 'duration': 8.006}], 'summary': 'R is case sensitive, treating lowercase and uppercase as different values.', 'duration': 30.969, 'max_score': 13341.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13341933.jpg'}], 'start': 12403.497, 'title': 'R programming fundamentals', 'summary': "Covers variables, data types, and operators in r programming, including numeric, character, and logical data types, and leftward assignment as the primary operator. it also delves into scripting basics, commenting, variable assignment, and best practices, as well as r's case sensitivity and intuitive variable declaration process.", 'chapters': [{'end': 12737.281, 'start': 12403.497, 'title': 'Variables and data types in programming', 'summary': 'Discusses the concept of variables, their importance in programming, and the different data types in programming including numeric, character, and logical data types, and their usage in storing and manipulating information.', 'duration': 333.784, 'highlights': ['Variables are temporary storage spaces in programming that can keep changing values, serving as a crucial concept in programming languages. Variables are essential in programming as they act as temporary storage spaces where values can be changed, providing the ability to store and manipulate information across variables.', 'Data types in programming denote the nature of the variable, with four major data types being numeric, character, logical, and complex, used to store different kinds of values. Data types in programming denote the nature of the variable, such as whether it is a number, character, binary value, or true/false, and the four major data types are numeric, character, logical, and complex.', 'Numeric data type is used to store any number, character data type is used for storing names or combinations of alphabets, and logical data type is used for variables with binary outcomes. Numeric data type is used to store numbers, character data type is used for storing names or combinations of alphabets, and logical data type is used for variables with binary outcomes, such as true/false.']}, {'end': 13068.546, 'start': 12737.281, 'title': 'R data types and operators', 'summary': 'Discussed four primary data types (numeric, character, logical, and complex) and four major operators (assignment, arithmetic, relational, and logical). it elaborated on the usage and importance of each data type and operator, emphasizing the leftward assignment operator as the most commonly used for assigning values to variables.', 'duration': 331.265, 'highlights': ['The chapter explained the four primary data types in R, which are numeric, character, logical, and complex, emphasizing their usage and relevance in programming.', 'The chapter detailed the four major operators in R, including assignment, arithmetic, relational, and logical, highlighting their specific functions and importance in performing operations and comparisons.', 'The chapter emphasized the leftward assignment operator as the most commonly used method for assigning values to variables, with a usage preference of 80 to 90 percent among statisticians and data scientists.', 'The chapter described the different assignment operators available in R, such as the rightward assignment operator and the equal to assignment operator, while emphasizing the widespread usage of the leftward assignment operator.', 'The chapter provided hands-on guidance on using the leftward assignment operator for performing operations and assigning values to variables in R.']}, {'end': 13341.933, 'start': 13068.546, 'title': 'R scripting basics', 'summary': 'Covers commenting out code using hashtags, creating variables with leftward and rightward assignment operators, and the preference for leftward assignment due to potential confusion with equal to operator, with examples and best practices.', 'duration': 273.387, 'highlights': ['Using a hashtag to comment out code allows exclusion from execution, demonstrated by clearing the environment and creating a variable. The speaker explains how to use a hashtag to comment out code and demonstrates clearing the environment and creating a variable as an example.', 'Creating variables with leftward and rightward assignment operators is showcased using examples with values 100 and 10, with emphasis on naming conventions and restrictions. The process of creating variables with leftward and rightward assignment operators is demonstrated, emphasizing naming conventions and restrictions while showcasing examples with values 100 and 10.', 'The preference for leftward assignment over equal to operator is explained due to potential confusion with the double equal to relational operator, with a recommendation to align codes with the leftward assignment operator for consistency. The speaker explains the preference for leftward assignment over the equal to operator due to potential confusion with the double equal to relational operator and recommends aligning codes with the leftward assignment operator for consistency.']}, {'end': 13754.443, 'start': 13341.933, 'title': 'R programming basics', 'summary': 'Explains the case sensitivity of r, the intuitive variable declaration, and arithmetic operations. r is case sensitive, creating different variables for lowercase and uppercase letters. it intuitively assigns data types, making variable declaration unnecessary and supports arithmetic operators for easy computation.', 'duration': 412.51, 'highlights': ['R is case sensitive, creating different variables for lowercase and uppercase letters, inducing errors in programming if not used carefully.', 'R intuitively assigns data types, making variable declaration unnecessary, and allowing easy overwriting of data types if required.', 'R supports arithmetic operators for easy computation, allowing the creation and manipulation of variables for various mathematical operations.']}], 'duration': 1350.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k12403497.jpg', 'highlights': ['Variables are essential in programming as they act as temporary storage spaces where values can be changed, providing the ability to store and manipulate information across variables.', 'Data types in programming denote the nature of the variable, such as whether it is a number, character, binary value, or true/false, and the four major data types are numeric, character, logical, and complex.', 'The chapter emphasized the leftward assignment operator as the most commonly used method for assigning values to variables, with a usage preference of 80 to 90 percent among statisticians and data scientists.', 'Using a hashtag to comment out code allows exclusion from execution, demonstrated by clearing the environment and creating a variable.', 'The preference for leftward assignment over equal to operator is explained due to potential confusion with the double equal to relational operator, with a recommendation to align codes with the leftward assignment operator for consistency.', 'R is case sensitive, creating different variables for lowercase and uppercase letters, inducing errors in programming if not used carefully.']}, {'end': 15094.498, 'segs': [{'end': 13863.242, 'src': 'embed', 'start': 13813.573, 'weight': 0, 'content': [{'end': 13816.755, 'text': 'Okay, you can do division right that says Z divided by X.', 'start': 13813.573, 'duration': 3.182}, {'end': 13818.196, 'text': 'Okay, you can do exponent.', 'start': 13816.755, 'duration': 1.441}, {'end': 13820.01, 'text': 'Okay, you can do exponent.', 'start': 13818.649, 'duration': 1.361}, {'end': 13825.555, 'text': "You can do 5 raised to the power 3, 5 raised to the, or let's say x raised to the power 3.", 'start': 13820.03, 'duration': 5.525}, {'end': 13831.259, 'text': 'Exponent is nothing but power of, right? So when you want to do square, cube, 10 to the power 100, etc.', 'start': 13825.555, 'duration': 5.704}, {'end': 13835.783, 'text': ', you can make use of this, right? x to the power 3 is this, right? x is 100.', 'start': 13831.259, 'duration': 4.524}, {'end': 13840.407, 'text': 'x to the power 3 would be nothing but 10 raised to the power 6, right?', 'start': 13835.783, 'duration': 4.624}, {'end': 13842.728, 'text': "That's what you get here, okay?", 'start': 13840.427, 'duration': 2.301}, {'end': 13844.75, 'text': 'So you can do exponent right?', 'start': 13843.509, 'duration': 1.241}, {'end': 13848.313, 'text': 'And these are the primary operations that you would do right?', 'start': 13844.87, 'duration': 3.443}, {'end': 13854.839, 'text': 'using arithmetic operators, okay.', 'start': 13851.258, 'duration': 3.581}, {'end': 13857.38, 'text': 'so these are some of the arithmetic operators that you could use.', 'start': 13854.839, 'duration': 2.541}, {'end': 13863.242, 'text': 'you can do plus, minus, division, subtraction, multiplication, exponent, anything that you like.', 'start': 13857.38, 'duration': 5.862}], 'summary': 'The transcript covers arithmetic operations including division, exponentiation, and primary operators.', 'duration': 49.669, 'max_score': 13813.573, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13813573.jpg'}, {'end': 13955.898, 'src': 'embed', 'start': 13924.842, 'weight': 1, 'content': [{'end': 13929.604, 'text': 'okay. so relational operators are used to check relationship between two variables.', 'start': 13924.842, 'duration': 4.762}, {'end': 13935.439, 'text': 'right, if a particular operation, if a particular relationship is true, the output will be true.', 'start': 13929.604, 'duration': 5.835}, {'end': 13937.261, 'text': 'if it is false, then it will be false.', 'start': 13935.439, 'duration': 1.822}, {'end': 13938.682, 'text': 'okay, yeah.', 'start': 13937.261, 'duration': 1.421}, {'end': 13945.708, 'text': "so let's say, you can check if, if something is less than some another variable, you can check if a variable is greater than another variable.", 'start': 13938.682, 'duration': 7.026}, {'end': 13948.751, 'text': 'right, this is, this is for less than this, is for greater than?', 'start': 13945.708, 'duration': 3.043}, {'end': 13955.898, 'text': 'uh. then you have something called greater than or equal to as well, right, you can do greater than or equal to okay.', 'start': 13948.751, 'duration': 7.147}], 'summary': 'Relational operators compare variables for true/false relationship, e.g., less than, greater than, and greater than or equal to.', 'duration': 31.056, 'max_score': 13924.842, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13924842.jpg'}, {'end': 14876.911, 'src': 'embed', 'start': 14846.503, 'weight': 2, 'content': [{'end': 14852.087, 'text': "then the object that you're going to use, right, to store this information, is a data frame.", 'start': 14846.503, 'duration': 5.584}, {'end': 14856.331, 'text': 'okay, so matrix is used to store multi-dimensional information which is homogeneous,', 'start': 14852.087, 'duration': 4.244}, {'end': 14860.234, 'text': 'and data frame is used to store and store multi-dimensional information which is heterogeneous.', 'start': 14856.331, 'duration': 3.903}, {'end': 14864.338, 'text': "so i'll just give you a very simple example of a matrix and a data frame.", 'start': 14860.234, 'duration': 4.104}, {'end': 14866.439, 'text': 'okay, and there could be many examples.', 'start': 14864.338, 'duration': 2.101}, {'end': 14869.422, 'text': "right, it's a, it's a fairly simple concept.", 'start': 14866.439, 'duration': 2.983}, {'end': 14870.443, 'text': 'there could be many examples.', 'start': 14869.422, 'duration': 1.021}, {'end': 14876.911, 'text': "but just to give you a couple of uh simple examples, let's say, right, let's say,", 'start': 14870.443, 'duration': 6.468}], 'summary': 'Data frames store heterogeneous multi-dimensional information, while matrices store homogeneous multi-dimensional information.', 'duration': 30.408, 'max_score': 14846.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k14846503.jpg'}], 'start': 13754.543, 'title': 'Arithmetic, relational operators, and data objects', 'summary': 'Covers arithmetic operators, variable assignment, relational operators, and data objects in r, including examples of arithmetic operations, relational operator usage, and data object types such as vectors, lists, matrices, and data frames.', 'chapters': [{'end': 13863.242, 'start': 13754.543, 'title': 'Arithmetic operators and variable assignment', 'summary': "Discusses arithmetic operators and variable assignment, emphasizing the user's freedom to choose variable names and demonstrating various arithmetic operations, including addition, subtraction, multiplication, division, and exponentiation.", 'duration': 108.699, 'highlights': ['The user has full liberty to choose the name for the variable, and the left side of the assignment operator is user-defined.', 'Exponentiation allows for calculations like x raised to the power of 3, providing flexibility for complex mathematical operations.', 'The chapter demonstrates various arithmetic operations such as addition, subtraction, multiplication, division, and exponentiation, showcasing the functionality of these arithmetic operators.']}, {'end': 14101.374, 'start': 13863.242, 'title': 'Relational operators in programming', 'summary': 'Discusses relational operators used to define relationships between variables, including examples of less than, greater than, equal to, and not equal to, and their application in conditional statements and loops.', 'duration': 238.132, 'highlights': ['The chapter discusses various relational operators, including less than, greater than, greater than or equal to, less than or equal to, equal to, and not equal to, used to check relationships between variables.', 'The example of checking if 100 is equal to 100 demonstrates the use of the equal to operator, resulting in a true output, illustrating the functionality of the relational operators.', 'The practical application of relational operators in if-else loops and conditional statements is emphasized as a common usage scenario for these operators in programming.', "The demonstration of using relational operators to compare the values of variables x and y, and obtaining true or false output, provides a clear understanding of the operators' functionality in evaluating relationships between variables."]}, {'end': 14574.073, 'start': 14101.394, 'title': 'Introduction to data objects in r', 'summary': 'Introduces data objects in r, explaining the concept of objects, their types based on dimensionality and homogeneity, and provides examples of using vectors to store unidimensional and homogeneous data.', 'duration': 472.679, 'highlights': ['Data objects in R are used to store and modify large pieces of information, such as a sequence of values, names of participants, or a whole data set with multiple columns and rows.', 'Objects in R are created based on the dimensionality of the data and whether the information stored is homogeneous or heterogeneous, which determines the type of object needed.', 'One example of a data object in R is a vector, which is used to store unidimensional and homogeneous information, such as names of participants, their age, or their profession.']}, {'end': 15094.498, 'start': 14574.073, 'title': 'Data storage objects in data science', 'summary': 'Discusses the concept of storing heterogeneous and unstructured data using list objects, which are one-dimensional and can store multiple data types, and introduces matrix and data frame objects for storing multi-dimensional data with homogeneous and heterogeneous information respectively.', 'duration': 520.425, 'highlights': ['List objects are used to store one-dimensional unstructured data with multiple data types. Lists are used to store heterogeneous information, such as unstructured data with character and numeric values not segregated across different columns.', 'Matrix objects are utilized to store multi-dimensional data with homogeneous information. Matrices are suitable for storing multi-dimensional data with consistent data types across columns, making it homogeneous data.', 'Data frame objects are employed to store multi-dimensional data with heterogeneous information. Data frames are used for multi-dimensional data with different data types across columns, making it heterogeneous data, commonly utilized in data science projects.']}], 'duration': 1339.955, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k13754543.jpg', 'highlights': ['The chapter demonstrates various arithmetic operations such as addition, subtraction, multiplication, division, and exponentiation, showcasing the functionality of these arithmetic operators.', "The demonstration of using relational operators to compare the values of variables x and y, and obtaining true or false output, provides a clear understanding of the operators' functionality in evaluating relationships between variables.", 'Data frame objects are employed to store multi-dimensional data with heterogeneous information. Data frames are used for multi-dimensional data with different data types across columns, making it heterogeneous data, commonly utilized in data science projects.', 'Exponentiation allows for calculations like x raised to the power of 3, providing flexibility for complex mathematical operations.']}, {'end': 16493.067, 'segs': [{'end': 15118.352, 'src': 'embed', 'start': 15094.498, 'weight': 0, 'content': [{'end': 15102.804, 'text': 'these are the four objects that we are going to use right and we are going to manipulate them to see how you perform operations in r?', 'start': 15094.498, 'duration': 8.306}, {'end': 15110.744, 'text': 'okay. so yeah, now what we could start doing is we could start exploring each of these objects right, one by one, and see how do you create them,', 'start': 15102.804, 'duration': 7.94}, {'end': 15111.705, 'text': 'how do you manipulate them?', 'start': 15110.744, 'duration': 0.961}, {'end': 15115.549, 'text': 'how do you extract, extend and do multiple operations on them?', 'start': 15111.705, 'duration': 3.844}, {'end': 15118.352, 'text': 'okay, so again, open up your RStudios.', 'start': 15115.549, 'duration': 2.803}], 'summary': 'Exploring and performing operations on four objects in r using rstudios.', 'duration': 23.854, 'max_score': 15094.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15094498.jpg'}, {'end': 15166.261, 'src': 'embed', 'start': 15140.249, 'weight': 2, 'content': [{'end': 15145.53, 'text': 'okay, we will start with the simplest data object right and see how do we create it, how do we extrapolate it,', 'start': 15140.249, 'duration': 5.281}, {'end': 15149.33, 'text': 'how do we manipulate it right in our studio?', 'start': 15145.53, 'duration': 3.8}, {'end': 15156.672, 'text': "okay. so just to repeat, what is a vector vector is nothing, but because it's a sequence of data elements, right, a collection of data elements,", 'start': 15149.33, 'duration': 7.342}, {'end': 15161.579, 'text': 'all of them having the same data type, right.', 'start': 15157.657, 'duration': 3.922}, {'end': 15166.261, 'text': 'so a vector can have only one kind of data type.', 'start': 15161.579, 'duration': 4.682}], 'summary': 'Learn how to create, extrapolate, and manipulate a vector, a sequence of data elements with the same data type.', 'duration': 26.012, 'max_score': 15140.249, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15140249.jpg'}, {'end': 15249.7, 'src': 'embed', 'start': 15218.881, 'weight': 1, 'content': [{'end': 15221.523, 'text': 'okay, so how do you create a vector using this function c?', 'start': 15218.881, 'duration': 2.642}, {'end': 15224.645, 'text': 'this function c basically stands for concatenate or combine.', 'start': 15221.523, 'duration': 3.122}, {'end': 15230.009, 'text': "right, what you're trying to do is you're trying to combine multiple values together to create one data object.", 'start': 15224.645, 'duration': 5.364}, {'end': 15231.87, 'text': "right, that's what you're trying to do.", 'start': 15230.009, 'duration': 1.861}, {'end': 15236.154, 'text': "so that's why the function is no named as small c.", 'start': 15231.87, 'duration': 4.284}, {'end': 15238.235, 'text': "okay, please don't get confused with it's a.", 'start': 15236.154, 'duration': 2.081}, {'end': 15241.017, 'text': "as i said, it's a case sensitive.", 'start': 15238.235, 'duration': 2.782}, {'end': 15243.259, 'text': 'uh, r is a case sensitive language.', 'start': 15241.017, 'duration': 2.242}, {'end': 15246.197, 'text': "right. So make sure you're using a small C right?", 'start': 15243.259, 'duration': 2.938}, {'end': 15249.7, 'text': 'A lower case C and not a capital or an upper case C here, right?', 'start': 15246.358, 'duration': 3.342}], 'summary': 'Using the function c in r to create a vector by combining multiple values.', 'duration': 30.819, 'max_score': 15218.881, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15218881.jpg'}, {'end': 15312.209, 'src': 'embed', 'start': 15285.851, 'weight': 3, 'content': [{'end': 15292.615, 'text': 'right, use the leftward assignment operator and then the fun, the function c, the function combined, and then, within parenthesis,', 'start': 15285.851, 'duration': 6.764}, {'end': 15295.396, 'text': 'all the values that you want to store in that vector.', 'start': 15292.615, 'duration': 2.781}, {'end': 15297.177, 'text': 'okay, this is how you can create a vector.', 'start': 15295.396, 'duration': 1.781}, {'end': 15302.864, 'text': 'right. and this simple underscore vector that i am named right, you can give any name to it, right, it has to be.', 'start': 15297.177, 'duration': 5.687}, {'end': 15307.186, 'text': "it's a user defined value, so you can name it whatever you like.", 'start': 15302.864, 'duration': 4.322}, {'end': 15312.209, 'text': "just make sure it doesn't have any special characters except underscore.", 'start': 15307.186, 'duration': 5.023}], 'summary': 'Create a vector using leftward assignment operator and function c, with user-defined values.', 'duration': 26.358, 'max_score': 15285.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15285851.jpg'}, {'end': 15401.127, 'src': 'embed', 'start': 15377.466, 'weight': 4, 'content': [{'end': 15385.728, 'text': "so if you, because i've created a numeric vector, right if i do a type of, or if i do a class of simple vector if i do class of simple vector,", 'start': 15377.466, 'duration': 8.262}, {'end': 15390.844, 'text': 'then it gives me numeric.', 'start': 15388.203, 'duration': 2.641}, {'end': 15394.945, 'text': 'I can also make use of another function called type of okay type.', 'start': 15390.844, 'duration': 4.101}, {'end': 15401.127, 'text': 'So basically class gives you the primary data type.', 'start': 15395.105, 'duration': 6.022}], 'summary': "Using 'class' function on numeric vector returns 'numeric' data type.", 'duration': 23.661, 'max_score': 15377.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15377466.jpg'}, {'end': 15604.248, 'src': 'embed', 'start': 15575.58, 'weight': 5, 'content': [{'end': 15578.303, 'text': "rather, you're creating a named vector.", 'start': 15575.58, 'duration': 2.723}, {'end': 15584.768, 'text': 'each value in this vector also has a corresponding name designed to it name designated to it.', 'start': 15578.303, 'duration': 6.465}, {'end': 15592.846, 'text': 'okay. so when you run this right, you create a named numeric vector, right, which has four unique values.', 'start': 15584.768, 'duration': 8.078}, {'end': 15601.308, 'text': 'right, and if you output it, if you output it right, it gives you that the first card is 10, the second card is 8,', 'start': 15592.846, 'duration': 8.462}, {'end': 15604.248, 'text': 'the third card is 6 and the fourth card is 9..', 'start': 15601.308, 'duration': 2.94}], 'summary': 'Creating named vector with four unique values: 10, 8, 6, and 9.', 'duration': 28.668, 'max_score': 15575.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15575580.jpg'}, {'end': 15834.651, 'src': 'embed', 'start': 15807.976, 'weight': 6, 'content': [{'end': 15815.062, 'text': 'you can use square brackets and within square brackets you could mention the index of the value that you want to extract.', 'start': 15807.976, 'duration': 7.086}, {'end': 15819.084, 'text': 'okay, index in the sense that the position at the value that you want to extract.', 'start': 15815.542, 'duration': 3.542}, {'end': 15822.425, 'text': 'if you want to extract the first element, you can write one within parenthesis.', 'start': 15819.084, 'duration': 3.341}, {'end': 15824.847, 'text': 'if you want to extract the second element, you can write two.', 'start': 15822.425, 'duration': 2.422}, {'end': 15827.448, 'text': 'if you want to extract third, fourth, fifth, etc.', 'start': 15824.847, 'duration': 2.601}, {'end': 15830.329, 'text': 'depending on what you want to extract, you can just write it there, right.', 'start': 15827.448, 'duration': 2.881}, {'end': 15834.651, 'text': "let's say, if i want to extract the fourth value, okay, which is, or let's say, fifth value, which is.", 'start': 15830.329, 'duration': 4.322}], 'summary': 'Extract values using square brackets with index. example: extract 5th value.', 'duration': 26.675, 'max_score': 15807.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15807976.jpg'}, {'end': 15973.234, 'src': 'embed', 'start': 15947.311, 'weight': 7, 'content': [{'end': 15953.054, 'text': "I mean, let's say, if you have a CSV file, if you have a flat file in R, if you have a file in R, right?", 'start': 15947.311, 'duration': 5.743}, {'end': 15957.484, 'text': "How do you import that in an R to create, let's say, a data frame or a data set?", 'start': 15953.717, 'duration': 3.767}, {'end': 15959.528, 'text': 'How do you do that?', 'start': 15958.866, 'duration': 0.662}, {'end': 15962.834, 'text': 'So there are two, three steps that you need to follow.', 'start': 15961.03, 'duration': 1.804}, {'end': 15965.499, 'text': 'The first step, of course, of X.', 'start': 15962.874, 'duration': 2.625}, {'end': 15967.032, 'text': 'When you do this.', 'start': 15966.172, 'duration': 0.86}, {'end': 15970.853, 'text': "length of X is nothing, but it's the total number of values that you have in in the vector,", 'start': 15967.032, 'duration': 3.821}, {'end': 15973.234, 'text': "and I'm going to enclose this with an X within parenthesis.", 'start': 15970.853, 'duration': 2.381}], 'summary': 'Importing csv file into r to create a data frame involves following two to three steps.', 'duration': 25.923, 'max_score': 15947.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15947311.jpg'}, {'end': 16016.033, 'src': 'embed', 'start': 15983.736, 'weight': 8, 'content': [{'end': 15984.536, 'text': 'So this is how you could do it.', 'start': 15983.736, 'duration': 0.8}, {'end': 15986.536, 'text': 'But how do you import data in our right?', 'start': 15984.796, 'duration': 1.74}, {'end': 15988.497, 'text': 'There are a couple of steps that you need to follow.', 'start': 15986.556, 'duration': 1.941}, {'end': 15991.638, 'text': 'the first step, right? is, of course you should have a flat file.', 'start': 15988.497, 'duration': 3.141}, {'end': 15992.779, 'text': 'you should have a csv file.', 'start': 15991.638, 'duration': 1.141}, {'end': 15997.602, 'text': "what i'm showing you here is how do you import a csv file in r, a comma separated value file?", 'start': 15992.779, 'duration': 4.823}, {'end': 16003.926, 'text': "okay, i'm sure all of you have a comma separated value file in r, so not in r but in your in your local machines, right.", 'start': 15997.602, 'duration': 6.324}, {'end': 16009.69, 'text': "uh, the first thing that you have to do, right, whenever you're trying to import data, is that you have to set a working directory.", 'start': 16003.926, 'duration': 5.764}, {'end': 16016.033, 'text': "You have to set a working directory and that working directory is nothing, but it's the folder of the file that you want to import.", 'start': 16010.371, 'duration': 5.662}], 'summary': "To import a csv file in r, set a working directory to the file's folder.", 'duration': 32.297, 'max_score': 15983.736, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15983736.jpg'}, {'end': 16141.587, 'src': 'embed', 'start': 16116.567, 'weight': 9, 'content': [{'end': 16122.728, 'text': "or let's say, if i want to call it uh, raw data, okay, raw underscore data, whatever you want to call it right, it's up to you.", 'start': 16116.567, 'duration': 6.161}, {'end': 16124.549, 'text': 'use this function called read.csv.', 'start': 16122.728, 'duration': 1.821}, {'end': 16130.37, 'text': 'read.csv is basically used for reading and importing comma separated value files in our studio.', 'start': 16124.549, 'duration': 5.821}, {'end': 16136.458, 'text': 'okay, so read.csv and then within parenthesis, within parenthesis, the name of the file that you want to import.', 'start': 16130.37, 'duration': 6.088}, {'end': 16141.587, 'text': "okay, let's say the name of the file that i want to import is customer underscore, churn dot csv.", 'start': 16137.125, 'duration': 4.462}], 'summary': 'Use read.csv to import comma separated value files in rstudio, such as customer_churn.csv.', 'duration': 25.02, 'max_score': 16116.567, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k16116567.jpg'}, {'end': 16405.916, 'src': 'embed', 'start': 16358.482, 'weight': 10, 'content': [{'end': 16363.526, 'text': 'Okay, but before that, I think in most cases, majority of the files,', 'start': 16358.482, 'duration': 5.044}, {'end': 16367.59, 'text': 'or majority of the data that you would need right in a project would be stored in one folder.', 'start': 16363.526, 'duration': 4.064}, {'end': 16369.051, 'text': "So, assuming that's the case,", 'start': 16367.61, 'duration': 1.441}, {'end': 16378.519, 'text': 'you would set your working directory as the folder where you are expecting to get data out from data into our studio from okay.', 'start': 16369.051, 'duration': 9.468}, {'end': 16386.085, 'text': "So let's say how do you set your working directory? Okay, what you do is you go to session here.", 'start': 16378.899, 'duration': 7.186}, {'end': 16388.443, 'text': 'go to set working directory.', 'start': 16387.122, 'duration': 1.321}, {'end': 16391.125, 'text': "there's an option called set working directory.", 'start': 16388.443, 'duration': 2.682}, {'end': 16394.167, 'text': 'within this option, go to choose directory.', 'start': 16391.125, 'duration': 3.042}, {'end': 16403.254, 'text': 'when you click on, choose directory, okay, it will let you select one of the folders anywhere in your machine.', 'start': 16394.167, 'duration': 9.087}, {'end': 16405.916, 'text': 'okay, anyone, anywhere in your local machine.', 'start': 16403.254, 'duration': 2.662}], 'summary': 'Most project data is stored in one folder, set working directory in rstudio to access it.', 'duration': 47.434, 'max_score': 16358.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k16358482.jpg'}], 'start': 15094.498, 'title': 'Manipulating and importing data in r', 'summary': 'Covers manipulating data objects, creating and working with vectors, and importing csv files in r. it includes functions for concatenating values into data objects, creating named vectors, importing csv files, and setting up the working directory in r studio.', 'chapters': [{'end': 15258.346, 'start': 15094.498, 'title': 'Manipulating data objects in r', 'summary': "Introduces manipulating data objects in r, starting with exploring different data objects and learning to create, manipulate, and extrapolate vectors using the function 'c' for concatenating multiple values into one data object.", 'duration': 163.848, 'highlights': ['The chapter introduces manipulating data objects in R The chapter sets the stage for learning about manipulating data objects in R, which is the primary focus of the discussion.', "Introducing the function 'c' for creating vectors The function 'c' is introduced as the method for creating vectors in R, emphasizing its role in concatenating multiple values to form a single data object.", 'Exploring different data objects and learning to create, manipulate, and extrapolate vectors The discussion focuses on exploring various data objects and delves into the process of creating, manipulating, and extrapolating vectors, providing a comprehensive understanding of these operations.']}, {'end': 15983.576, 'start': 15258.506, 'title': 'Creating and working with vectors in r', 'summary': 'Discusses creating and working with vectors in r, including how to create numeric and character vectors, checking the type of vector, creating named vectors, and extracting values from vectors. it also covers important functions for working with vectors and provides insights on importing data in r.', 'duration': 725.07, 'highlights': ['Creating a vector in R involves specifying the name, using the leftward assignment operator, and the function c to combine the values within parenthesis. Creating a vector involves specifying the name, using the leftward assignment operator, and the function c to combine the values within parenthesis.', 'Using the function is.vector() helps to determine if the object is a vector, and the class and type of functions provide information on the data type and its sub-classification. The function is.vector() helps determine if the object is a vector, and the class and type of functions provide information on the data type and its sub-classification.', 'Creating named vectors involves assigning names to each value, allowing for easier extraction of information using the assigned names. Creating named vectors involves assigning names to each value, allowing for easier extraction of information using the assigned names.', 'To extract specific elements from a vector, one can use square brackets with the index of the value to be extracted, noting that the index in R starts from one. To extract specific elements from a vector, one can use square brackets with the index of the value to be extracted, noting that the index in R starts from one.', 'Importing data in R involves several steps, including reading the file and creating a data frame or data set from the imported data. Importing data in R involves several steps, including reading the file and creating a data frame or data set from the imported data.']}, {'end': 16252.216, 'start': 15983.736, 'title': 'Importing csv files in r', 'summary': 'Explains the process of importing a csv file in r by setting up the working directory, using the read.csv function, and saving the imported file as a csv utf-8 format, with a demonstration of creating and importing a csv file, providing insight into the process with practical examples and tips.', 'duration': 268.48, 'highlights': ['To import a CSV file in R, the first step is to set the working directory to the folder containing the file, which can be done through the session tab and choosing the directory where the file is stored.', "The read.csv function in R is used to import comma separated value files, and after running the function, an object containing the imported data is created in the environment, with an example showing the creation of 'raw data' with 7043 observations and 21 variables.", "The process of saving the imported file involves using the 'save as' function, choosing the CSV UTF-8 format, and saving it as a comma delimited file, with instructions on how to save an Excel file as a CSV file and set the working directory in R."]}, {'end': 16493.067, 'start': 16252.216, 'title': 'Importing csv file to r studio', 'summary': 'Explains how to import a csv file into r studio, including setting the working directory and using the read.csv function, aimed at facilitating the data import process for efficient project work.', 'duration': 240.851, 'highlights': ['The importance of setting the working directory is emphasized as it facilitates accessing and importing data for project work, ensuring a seamless data import process. Setting the working directory helps in accessing and importing data for project work, improving efficiency and organization of the data import process.', 'The process of setting the working directory in R Studio is explained, demonstrating how to choose a folder within the local machine to serve as the working directory for importing data into R Studio. The explanation includes the steps to select a folder within the local machine to become the working directory for importing data into R Studio, enhancing the understanding of the process.', 'The method of importing a CSV file into R Studio using the read.csv function is demonstrated, showcasing the direct import process for efficient data handling in project work. The demonstration includes the use of the read.csv function to directly import a CSV file into R Studio, demonstrating an efficient method for data import in project work.']}], 'duration': 1398.569, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k15094498.jpg', 'highlights': ['The chapter introduces manipulating data objects in R, setting the stage for learning about manipulating data objects in R.', "The function 'c' is introduced as the method for creating vectors in R, emphasizing its role in concatenating multiple values to form a single data object.", 'Exploring various data objects and delving into the process of creating, manipulating, and extrapolating vectors provides a comprehensive understanding of these operations.', 'Creating a vector involves specifying the name, using the leftward assignment operator, and the function c to combine the values within parenthesis.', 'The function is.vector() helps determine if the object is a vector, and the class and type of functions provide information on the data type and its sub-classification.', 'Creating named vectors involves assigning names to each value, allowing for easier extraction of information using the assigned names.', 'To extract specific elements from a vector, one can use square brackets with the index of the value to be extracted, noting that the index in R starts from one.', 'Importing data in R involves several steps, including reading the file and creating a data frame or data set from the imported data.', 'To import a CSV file in R, the first step is to set the working directory to the folder containing the file, which can be done through the session tab and choosing the directory where the file is stored.', "The read.csv function in R is used to import comma separated value files, and after running the function, an object containing the imported data is created in the environment, with an example showing the creation of 'raw data' with 7043 observations and 21 variables.", 'The importance of setting the working directory is emphasized as it facilitates accessing and importing data for project work, ensuring a seamless data import process.', 'The process of setting the working directory in R Studio is explained, demonstrating how to choose a folder within the local machine to serve as the working directory for importing data into R Studio.', 'The method of importing a CSV file into R Studio using the read.csv function is demonstrated, showcasing the direct import process for efficient data handling in project work.']}, {'end': 18067.517, 'segs': [{'end': 16667.95, 'src': 'embed', 'start': 16635.303, 'weight': 3, 'content': [{'end': 16640.083, 'text': 'while mentioning the name of the file in your, in your read.csv command.', 'start': 16635.303, 'duration': 4.78}, {'end': 16647.606, 'text': "You can also give the entire path location right the file location and that's what our friend here saw Vic has done.", 'start': 16640.104, 'duration': 7.502}, {'end': 16654.107, 'text': "It's just this is this is a command that saw Vic has written which is why is the name of the object that he's creating.", 'start': 16648.086, 'duration': 6.021}, {'end': 16657.348, 'text': 'Okay, read.csv and within parenthesis within double quotes.', 'start': 16654.467, 'duration': 2.881}, {'end': 16660.248, 'text': 'Okay, this is the location of the file.', 'start': 16658.567, 'duration': 1.681}, {'end': 16667.95, 'text': "It's along with the path of the file, which is in Telepad via D drive in Telepad our program and then within our program.", 'start': 16660.268, 'duration': 7.682}], 'summary': 'The transcript discusses using read.csv command with file path and location.', 'duration': 32.647, 'max_score': 16635.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k16635303.jpg'}, {'end': 16753.651, 'src': 'embed', 'start': 16699.3, 'weight': 2, 'content': [{'end': 16707.985, 'text': "But this file that you've imported, because it's a large file, it has multiple rows and multiple columns, it gets imported as a data frame.", 'start': 16699.3, 'duration': 8.685}, {'end': 16716.484, 'text': 'So first of all, how do you go about viewing this file? What you could do is, You could just use a function called view.', 'start': 16708.625, 'duration': 7.859}, {'end': 16728.274, 'text': 'Okay And within view write the name of the file write the name of the object that you imported right? My first import is the object.', 'start': 16717.505, 'duration': 10.769}, {'end': 16732.677, 'text': 'I could just do view of my first import this view starts with capital V.', 'start': 16728.333, 'duration': 4.344}, {'end': 16736.4, 'text': 'Okay, click on it and it will start showing you displaying you the content of this file.', 'start': 16732.677, 'duration': 3.723}, {'end': 16740.924, 'text': 'Okay, to start displaying you the content of this file.', 'start': 16738.122, 'duration': 2.802}, {'end': 16751.571, 'text': 'your file has 7043 observations right, which means 7043 rows okay and 21 variables okay.', 'start': 16740.924, 'duration': 10.647}, {'end': 16753.651, 'text': 'there are 21 different columns in this file.', 'start': 16751.571, 'duration': 2.08}], 'summary': 'The imported large file is displayed as a data frame. it contains 7043 observations (rows) and 21 variables (columns).', 'duration': 54.351, 'max_score': 16699.3, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k16699300.jpg'}, {'end': 16898.998, 'src': 'embed', 'start': 16854.623, 'weight': 0, 'content': [{'end': 16858.867, 'text': 'whether the customer is a senior citizen, whether the customer is a, has dependents, etc.', 'start': 16854.623, 'duration': 4.244}, {'end': 16865.474, 'text': "and then we also have information about certain the kind of services that they're using, the kind of transactions that they do.", 'start': 16858.867, 'duration': 6.607}, {'end': 16867.936, 'text': 'all of that we have this in this data.', 'start': 16865.474, 'duration': 2.462}, {'end': 16874.142, 'text': 'the objective, finally, for us, while we use this data, would be to build a prediction model,', 'start': 16867.936, 'duration': 6.206}, {'end': 16884.609, 'text': 'build a prediction model using machine learning that can help us predict who are the customers that are likely to churn in the future,', 'start': 16875.423, 'duration': 9.186}, {'end': 16885.69, 'text': 'who are the customers?', 'start': 16884.609, 'duration': 1.081}, {'end': 16889.852, 'text': 'what are the attributes of customers that have high likelihood to churn?', 'start': 16885.69, 'duration': 4.162}, {'end': 16896.977, 'text': 'we are going to build a churn prediction model, churn prediction model, using various algorithms, various techniques,', 'start': 16889.852, 'duration': 7.125}, {'end': 16898.998, 'text': 'using this data set as a reference data set.', 'start': 16896.977, 'duration': 2.021}], 'summary': 'Build a churn prediction model using machine learning to predict future customer churn based on customer attributes and service usage data.', 'duration': 44.375, 'max_score': 16854.623, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k16854623.jpg'}, {'end': 17399.765, 'src': 'embed', 'start': 17358.356, 'weight': 5, 'content': [{'end': 17359.337, 'text': "okay, that's what i want to do.", 'start': 17358.356, 'duration': 0.981}, {'end': 17361.132, 'text': 'how can i do that?', 'start': 17360.091, 'duration': 1.041}, {'end': 17368.876, 'text': "okay, that's a function in r, okay, which lets you identify missing values in your data object.", 'start': 17361.132, 'duration': 7.744}, {'end': 17378.861, 'text': 'that function is called is dot na, is dot na is a function which will identify the positions in a vector right that have a missing value.', 'start': 17368.876, 'duration': 9.985}, {'end': 17386.706, 'text': 'okay, so if i run, is dot na, is dot na and, within parenthesis, random underscore vec right and run this, it will.', 'start': 17378.861, 'duration': 7.845}, {'end': 17399.765, 'text': 'basically, it will basically output true and false right as different values corresponding to different elements that I have in my vector wherever I see a true,', 'start': 17386.706, 'duration': 13.059}], 'summary': 'Using is.na function in r to identify missing values in a data object.', 'duration': 41.409, 'max_score': 17358.356, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k17358356.jpg'}, {'end': 17466.055, 'src': 'embed', 'start': 17444.465, 'weight': 6, 'content': [{'end': 17454.305, 'text': 'you can identify which values in your vector are missing and then you can decide to impute them to replace them with a different value.', 'start': 17444.465, 'duration': 9.84}, {'end': 17455.166, 'text': 'right?. How do you do that?', 'start': 17454.305, 'duration': 0.861}, {'end': 17463.973, 'text': "You can do that by using a function called, if else okay, there's a, if else, function which you can, which you can use in a vector.", 'start': 17455.446, 'duration': 8.527}, {'end': 17466.055, 'text': 'Okay, and within, if else you can put it.', 'start': 17464.333, 'duration': 1.722}], 'summary': 'Identify and impute missing values in a vector using if else function.', 'duration': 21.59, 'max_score': 17444.465, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k17444465.jpg'}, {'end': 17779.933, 'src': 'embed', 'start': 17750.478, 'weight': 8, 'content': [{'end': 17752.039, 'text': 'i use the function called matrix right.', 'start': 17750.478, 'duration': 1.561}, {'end': 17759.797, 'text': "within that matrix i input, let's say, values between 1 to 10, okay, and then this is one.", 'start': 17752.039, 'duration': 7.758}, {'end': 17764.18, 'text': 'this is the first argument that I need to provide it, which is the data that needs to go into my Matrix.', 'start': 17759.797, 'duration': 4.383}, {'end': 17771.686, 'text': 'Okay, the second argument that I need to provide to my function Matrix is the is either the number of rows.', 'start': 17765.121, 'duration': 6.565}, {'end': 17777.03, 'text': 'Okay, or the number of columns that I want to create in my Matrix right?', 'start': 17772.767, 'duration': 4.263}, {'end': 17779.933, 'text': 'Why is it an R condition and not an AND condition?', 'start': 17777.151, 'duration': 2.782}], 'summary': "Using the 'matrix' function to input values between 1 to 10 into a matrix and specifying the number of rows or columns as a second argument.", 'duration': 29.455, 'max_score': 17750.478, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k17750478.jpg'}, {'end': 17952.206, 'src': 'embed', 'start': 17925.904, 'weight': 9, 'content': [{'end': 17930.207, 'text': 'then go up and then fill the subset first from the first row itself.', 'start': 17925.904, 'duration': 4.303}, {'end': 17931.768, 'text': 'start filling up the second column.', 'start': 17930.207, 'duration': 1.561}, {'end': 17937.632, 'text': "that's the default way in which you have to, in which data will get inserted into a matrix.", 'start': 17931.768, 'duration': 5.864}, {'end': 17941.317, 'text': 'right, But if you want, you can change this manner.', 'start': 17937.632, 'duration': 3.685}, {'end': 17952.206, 'text': 'You can change the default order of insertion of data into a matrix, right? So in you could say the default insertion.', 'start': 17941.797, 'duration': 10.409}], 'summary': 'Default way of inserting data into a matrix can be changed.', 'duration': 26.302, 'max_score': 17925.904, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k17925904.jpg'}], 'start': 16494.301, 'title': 'Telecom customer churn analysis', 'summary': 'Discusses building a machine learning churn prediction model using a dataset with 7043 telecom customers, aiming to identify potential churners based on attributes like gender, tenure, services used, and monthly charges.', 'chapters': [{'end': 16815.639, 'start': 16494.301, 'title': 'Importing and processing data in r', 'summary': 'Discusses the two methods of importing data in r, including using a user-defined name for the imported file and providing the entire file path, along with the process of viewing the imported data frame, which contains 7043 rows and 21 columns, necessary for building machine learning models.', 'duration': 321.338, 'highlights': ["The function 'read.csv' is used to import a CSV file in R, and the imported file gets created as an object in the environment, such as 'my first import'. The function 'read.csv' is used to import a CSV file in R, creating an object, like 'my first import', in the environment.", "Providing the entire file path for import allows flexibility in choosing the working directory and is demonstrated through a command given by 'saw Vic'. Providing the entire file path for import allows flexibility in choosing the working directory, as demonstrated by a command given by 'saw Vic'.", "The imported file, a data frame, can be viewed using the 'view' function, displaying 7043 rows and 21 columns. The imported file, a data frame, can be viewed using the 'view' function, displaying 7043 rows and 21 columns."]}, {'end': 17270.243, 'start': 16815.639, 'title': 'Telecom customer churn analysis', 'summary': 'Discusses a dataset containing information on 7043 telecom customers and aims to build a machine learning churn prediction model to identify customers likely to churn based on attributes, such as gender, tenure, services used, and monthly charges.', 'duration': 454.604, 'highlights': ["The dataset contains information on 7043 customers of a telecom provider, including demographics, services used, and transaction details. The dataset provides comprehensive information on the telecom provider's customer base, including key demographics and transaction details, offering a rich source for analysis and modeling.", 'The objective is to build a prediction model using machine learning to identify customers likely to churn based on attributes such as gender, tenure, services used, and monthly charges. The primary goal is to leverage the dataset to construct a machine learning model that can effectively predict customer churn by analyzing key attributes such as gender, tenure, services used, and monthly charges.', 'Key attributes in the dataset include customer demographics, tenure, type of services used (phone, internet), contract type, payment method, monthly charges, total charges, and churn status. The dataset encompasses crucial customer attributes like demographics, tenure, service types, contract details, payment methods, and churn status, providing a comprehensive foundation for predictive modeling.']}, {'end': 17486.562, 'start': 17270.243, 'title': 'Imputing missing values in r', 'summary': "Demonstrates how to identify and replace missing values in a vector using functions like 'c', 'is.na', and 'ifelse', and outlines the process of converting missing values in a vector to zeros.", 'duration': 216.319, 'highlights': ["The function 'is.na' is used to identify missing values in a vector, with the output of 'true' and 'false' corresponding to the presence of missing values, allowing for the subsequent replacement of missing values with a constant value such as zero.", "The 'ifelse' function is employed to conditionally replace missing values in a vector with a specified value, providing flexibility in the imputation process.", "The process of creating a vector using the 'c' function and the utilization of 'na' as a keyword to impute missing values is explained, with the demonstration of replacing all missing values in the vector with zeros."]}, {'end': 18067.517, 'start': 17486.562, 'title': 'Syntax of if-else and creating matrices', 'summary': 'Explains the syntax of if-else statements in r, and demonstrates how to use if-else to identify and replace missing values with a specified value, followed by the process of creating matrices using the matrix function in r, including specifying the data, number of rows and columns, and changing the default order of data insertion.', 'duration': 580.955, 'highlights': ['Demonstrating the syntax of if-else statements in R and using it to identify and replace missing values Explains the syntax of if-else statements, demonstrating how to use if-else to check for missing values in a vector and replace them with a specified value, such as replacing missing values with zero, showing the process and the outcome of the replacement.', 'Creating matrices using the matrix function in R and specifying the data, number of rows and columns Describes the process of creating matrices using the matrix function, including specifying the data to be included in the matrix, and either the number of rows or columns to be created, demonstrating the creation of matrices with different numbers of rows and columns using the function.', "Changing the default order of data insertion into a matrix Explains how to change the default order of data insertion into a matrix, demonstrating the use of the 'byrow' argument to insert data row-wise instead of column-wise, and showcases the difference in the outcome by changing the order of data insertion."]}], 'duration': 1573.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k16494301.jpg', 'highlights': ['The primary goal is to leverage the dataset to construct a machine learning model that can effectively predict customer churn by analyzing key attributes such as gender, tenure, services used, and monthly charges.', 'The dataset encompasses crucial customer attributes like demographics, tenure, service types, contract details, payment methods, and churn status, providing a comprehensive foundation for predictive modeling.', "The function 'read.csv' is used to import a CSV file in R, creating an object, like 'my first import', in the environment.", "Providing the entire file path for import allows flexibility in choosing the working directory, as demonstrated by a command given by 'saw Vic'.", "The imported file, a data frame, can be viewed using the 'view' function, displaying 7043 rows and 21 columns.", "The function 'is.na' is used to identify missing values in a vector, with the output of 'true' and 'false' corresponding to the presence of missing values, allowing for the subsequent replacement of missing values with a constant value such as zero.", "The 'ifelse' function is employed to conditionally replace missing values in a vector with a specified value, providing flexibility in the imputation process.", 'Explains the syntax of if-else statements, demonstrating how to use if-else to check for missing values in a vector and replace them with a specified value, such as replacing missing values with zero, showing the process and the outcome of the replacement.', 'Describes the process of creating matrices using the matrix function, including specifying the data to be included in the matrix, and either the number of rows or columns to be created, demonstrating the creation of matrices with different numbers of rows and columns using the function.', "Explains how to change the default order of data insertion into a matrix, demonstrating the use of the 'byrow' argument to insert data row-wise instead of column-wise, and showcases the difference in the outcome by changing the order of data insertion."]}, {'end': 20133.887, 'segs': [{'end': 19302.711, 'src': 'embed', 'start': 19274.867, 'weight': 2, 'content': [{'end': 19277.628, 'text': 'this is how you will extend your matrix in a columnar manner.', 'start': 19274.867, 'duration': 2.761}, {'end': 19279.909, 'text': 'okay, cbind is the function that you would use.', 'start': 19277.628, 'duration': 2.281}, {'end': 19285.372, 'text': "now what we've done here is that we've created a new one, like we've just shown this in the output.", 'start': 19279.909, 'duration': 5.463}, {'end': 19295.576, 'text': 'unless you assign it back to the same object, right, mat underscore big this will not have any change in your mat in your existing object, right.', 'start': 19285.372, 'duration': 10.204}, {'end': 19302.711, 'text': 'so if you want to change your mat underscore big and you want to add the 11th column, then you will have to assign it back to Matt underscore big,', 'start': 19295.576, 'duration': 7.135}], 'summary': 'Use cbind function to extend matrix in a columnar manner', 'duration': 27.844, 'max_score': 19274.867, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k19274867.jpg'}, {'end': 19575.42, 'src': 'embed', 'start': 19546.957, 'weight': 0, 'content': [{'end': 19550.8, 'text': "um, what we've seen so far is how do you create a matrix using the matrix function?", 'start': 19546.957, 'duration': 3.843}, {'end': 19552.981, 'text': 'how do you extract values out from a matrix?', 'start': 19550.8, 'duration': 2.181}, {'end': 19556.003, 'text': 'right if, if you want to right, using the row index and the column index?', 'start': 19552.981, 'duration': 3.022}, {'end': 19559.946, 'text': 'how do you extend your matrix right by using c bind and r bind?', 'start': 19556.003, 'duration': 3.943}, {'end': 19565.491, 'text': "how do you, how do you essentially uh, let's say, do arithmetic operations on your matrix?", 'start': 19559.946, 'duration': 5.545}, {'end': 19570.016, 'text': 'and then, if you want to add or subtract matrices right from each other, then you have to.', 'start': 19565.491, 'duration': 4.525}, {'end': 19575.42, 'text': 'you can also do that, but you have to make sure that the dimensions of the two matrix, that on which you want to do the operation,', 'start': 19570.016, 'duration': 5.404}], 'summary': 'Learn matrix creation, value extraction, extension, and arithmetic operations.', 'duration': 28.463, 'max_score': 19546.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k19546957.jpg'}, {'end': 19829.156, 'src': 'embed', 'start': 19798.427, 'weight': 1, 'content': [{'end': 19799.688, 'text': "There's always dot character as well.", 'start': 19798.427, 'duration': 1.261}, {'end': 19809.231, 'text': 'Okay, so Once you import data, you also have the opportunity to edit it, to change the data type if you wanted to.', 'start': 19800.148, 'duration': 9.083}, {'end': 19816.012, 'text': 'Whenever you want to create a data frame, we would usually do that by importing a data set into RStudio,', 'start': 19809.411, 'duration': 6.601}, {'end': 19823.034, 'text': 'like we did with the case of customer underscore churn, where we imported a CSV file and created a data frame.', 'start': 19816.012, 'duration': 7.022}, {'end': 19824.775, 'text': "That's how you would do it.", 'start': 19823.954, 'duration': 0.821}, {'end': 19829.156, 'text': 'The read.csv command only lets you import a CSV file.', 'start': 19825.095, 'duration': 4.061}], 'summary': "In rstudio, importing data allows editing and creating data frames, like with the 'customer_churn' csv file.", 'duration': 30.729, 'max_score': 19798.427, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k19798427.jpg'}], 'start': 18067.637, 'title': 'Matrix operations and data frames', 'summary': 'Covers matrix creation, arithmetic operations, extraction, extension, adding columns and rows, data frame management, and creating data frames in rstudio, including the use of cbind, rbind, data.frame, and vector combinations for data frame formation.', 'chapters': [{'end': 18568.54, 'start': 18067.637, 'title': 'Matrix operations and extraction', 'summary': 'Covers the creation of matrices, performing arithmetic operations on matrices, and extracting information from matrices using row and column indices. it also explains the types of matrices and their data types.', 'duration': 500.903, 'highlights': ['The chapter covers the creation of matrices, performing arithmetic operations on matrices, and extracting information from matrices using row and column indices. It explains the process of creating matrices, performing arithmetic operations such as addition, subtraction, multiplication, and division on matrices, and extracting specific values using row and column indices.', "It also explains the types of matrices and their data types. The chapter elaborates on the distinction between numeric and character matrices and demonstrates how to determine the data type of a matrix using the 'type of' function."]}, {'end': 19248.874, 'start': 18568.54, 'title': 'Matrix extraction and extension', 'summary': 'Covers matrix extraction using row and column indexes to subset data, including examples of extracting specific rows and columns, and also demonstrates how to extend a matrix using cbind and rbind functions.', 'duration': 680.334, 'highlights': ['Using row and column indexes to subset data The speaker demonstrates extracting values from a matrix using row and column indexes, such as extracting the value from the second row and fifth column, showcasing the practical application of matrix extraction.', 'Using cbind to add a column to a matrix The speaker illustrates using the cbind function to add a column of values to an existing matrix, providing a practical example of extending the width of the matrix.', 'Using rbind to add a row to a matrix The speaker explains the usage of the rbind function to add a row of values to a matrix, demonstrating the process of increasing the vertical height of the matrix.', 'Demonstrating the impact of subset operations on the original matrix The speaker emphasizes that subset operations do not impact the original matrix unless explicitly assigned back to the same object, highlighting the importance of assigning subset results to update or create a new matrix.', "Changing the default order of data insertion in a matrix The speaker demonstrates how to change the default columnar order of data insertion in a matrix to a row-wise order using the 'byrow' parameter, providing a useful technique for modifying the data structure."]}, {'end': 19478.437, 'start': 19250.094, 'title': 'Adding columns and rows to matrix', 'summary': 'Explains how to add columns and rows to an existing matrix using the cbind and rbind functions, demonstrating the process and the impact on the dimensions of the matrix, including the need to assign the modified object back to the original object to update it.', 'duration': 228.343, 'highlights': ['The chapter explains how to use the cbind function to add a new column to an existing matrix, demonstrating the process and the impact on the dimensions of the matrix, emphasizing the need to assign the modified object back to the original object to update it. Demonstrates the addition of a new column to an existing matrix and the impact on its dimensions.', 'The chapter demonstrates the use of rbind to add a new row to an existing matrix, showcasing the process and the impact on the dimensions of the matrix, highlighting the requirement to assign the modified object back to the original object to update it. Illustrates the addition of a new row to an existing matrix and the impact on its dimensions.', 'The chapter emphasizes the need to assign the modified object back to the original object to update it after adding columns or rows to the matrix. Emphasizes the requirement to assign the modified object back to the original object for updating.']}, {'end': 19798.327, 'start': 19478.437, 'title': 'Matrix operations and data frame management', 'summary': 'Discusses performing operations like mean, max, and subset on a matrix, and managing data frames including importing, modifying data types, and checking structure.', 'duration': 319.89, 'highlights': ['Performing mean, max, and subset operations on a matrix The mean of the entire matrix is 61.81, and the max of the first five rows and columns is 45.', 'Managing data frames, including importing and modifying data types Data frames store data as observations and variables, and the read.csv command can import data as a data frame. The STR function shows the structure of a data frame and allows for modifying data types using functions like as.logical and as.numeric.']}, {'end': 20133.887, 'start': 19798.427, 'title': 'Creating data frames in rstudio', 'summary': 'Covers the process of creating a data frame in rstudio by importing a dataset and using vectors to form columns, with an emphasis on the function data.frame and the combination of independent vectors to form a data frame.', 'duration': 335.46, 'highlights': ['The process of creating a data frame in RStudio involves importing a dataset and using vectors to form columns, with the function data.frame being a key tool for this task. Importing a dataset, using vectors to form columns, function data.frame', 'The combination of independent vectors to form a data frame is explained, emphasizing that a data frame is essentially a collection of vectors of the same length. Combining independent vectors, data frame as a collection of vectors', 'The detailed process of creating a data frame using the data.frame function and assigning column names is demonstrated, highlighting the importance of intuitive column names for better understanding. Creating a data frame using data.frame function, assigning intuitive column names']}], 'duration': 2066.25, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k18067637.jpg', 'highlights': ['Covers matrix creation, arithmetic operations, extraction, extension, adding columns and rows, data frame management, and creating data frames in rstudio, including the use of cbind, rbind, data.frame, and vector combinations for data frame formation.', 'The process of creating a data frame in RStudio involves importing a dataset and using vectors to form columns, with the function data.frame being a key tool for this task. Importing a dataset, using vectors to form columns, function data.frame', 'Using cbind to add a column to a matrix The speaker illustrates using the cbind function to add a column of values to an existing matrix, providing a practical example of extending the width of the matrix.', 'The chapter covers the creation of matrices, performing arithmetic operations on matrices, and extracting information from matrices using row and column indices. It explains the process of creating matrices, performing arithmetic operations such as addition, subtraction, multiplication, and division on matrices, and extracting specific values using row and column indices.', 'The chapter explains how to use the cbind function to add a new column to an existing matrix, demonstrating the process and the impact on the dimensions of the matrix, emphasizing the need to assign the modified object back to the original object to update it. Demonstrates the addition of a new column to an existing matrix and the impact on its dimensions.']}, {'end': 21328.707, 'segs': [{'end': 20204.458, 'src': 'embed', 'start': 20179.997, 'weight': 0, 'content': [{'end': 20186.402, 'text': "For example, you can see up here, here's a teacher on the screen who is teaching the machine by showing different images of the apple.", 'start': 20179.997, 'duration': 6.405}, {'end': 20188.744, 'text': 'So once the machine is trained.', 'start': 20186.783, 'duration': 1.961}, {'end': 20197.051, 'text': "so in future, when we show a different image of an apple, the machine can easily identify that it's an apple with some accuracy.", 'start': 20188.744, 'duration': 8.307}, {'end': 20202.556, 'text': 'Okay, for example, the image shown up here is an apple with 97% accuracy.', 'start': 20197.512, 'duration': 5.044}, {'end': 20204.458, 'text': 'So this is supervised learning.', 'start': 20203.077, 'duration': 1.381}], 'summary': 'A teacher trains a machine to identify apples with 97% accuracy through supervised learning.', 'duration': 24.461, 'max_score': 20179.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k20179997.jpg'}, {'end': 20268.99, 'src': 'embed', 'start': 20241.521, 'weight': 1, 'content': [{'end': 20247.505, 'text': 'Text filter work by using algorithm to detect which word and phrases are most often used in the spam email.', 'start': 20241.521, 'duration': 5.984}, {'end': 20254.394, 'text': 'Phrases like lottery, you won, free bitcoin are often an immediate flag for removal by filters.', 'start': 20247.967, 'duration': 6.427}, {'end': 20264.105, 'text': 'Spammers have gotten wise to this and often use bland misspellings or even substitute of characters like free dollar character in order to make it pass the filter.', 'start': 20254.915, 'duration': 9.19}, {'end': 20268.99, 'text': 'Fortunately, the modern spam filter can also make advances for these type of misspellings.', 'start': 20264.445, 'duration': 4.545}], 'summary': "Spam filters detect common phrases like 'lottery', 'you won', and 'free bitcoin' to flag spam emails, but spammers use misspellings and character substitutions to bypass filters. modern filters can advance to detect these tactics.", 'duration': 27.469, 'max_score': 20241.521, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k20241521.jpg'}, {'end': 20761.686, 'src': 'embed', 'start': 20732.619, 'weight': 2, 'content': [{'end': 20739.882, 'text': 'the data is generated from dozens of in-house and freelance staff watch every minute or every show on Netflix and tag it.', 'start': 20732.619, 'duration': 7.263}, {'end': 20750.564, 'text': "Now all these tags and the user behavior data are taken and is fed to a very sophisticated machine learning algorithm that figures out what's most important and what should it weigh like.", 'start': 20740.342, 'duration': 10.222}, {'end': 20754.985, 'text': 'Or how much should it matter if a consumer has watched something yesterday?', 'start': 20751.184, 'duration': 3.801}, {'end': 20761.686, 'text': 'Should that count twice as much or 10 times as much compared to what they have watched a whole year ago?', 'start': 20755.385, 'duration': 6.301}], 'summary': 'Netflix uses data from staff and users to train a sophisticated machine learning algorithm for content recommendation.', 'duration': 29.067, 'max_score': 20732.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k20732619.jpg'}, {'end': 20988.926, 'src': 'embed', 'start': 20960.248, 'weight': 3, 'content': [{'end': 20964.63, 'text': 'One of the application or the use case of reinforcement learning is self-driving car.', 'start': 20960.248, 'duration': 4.382}, {'end': 20970.352, 'text': 'A recent study has shown that over 90% of road accidents are caused by human error.', 'start': 20965.35, 'duration': 5.002}, {'end': 20974.974, 'text': 'To err is human, but behind the wheel, mistakes are often more catastrophic.', 'start': 20970.672, 'duration': 4.302}, {'end': 20978.155, 'text': 'Accidents have led to a massive number of unnecessary death.', 'start': 20975.414, 'duration': 2.741}, {'end': 20981.176, 'text': 'Lives that could have been saved otherwise through self-driving.', 'start': 20978.615, 'duration': 2.561}, {'end': 20984.58, 'text': 'so this is where self-driving cars comes into picture.', 'start': 20981.676, 'duration': 2.904}, {'end': 20988.926, 'text': 'the autonomous car or the self-driving cars are much safer than human driven car.', 'start': 20984.58, 'duration': 4.346}], 'summary': 'Over 90% of road accidents are caused by human error, leading to unnecessary deaths - self-driving cars offer a safer alternative.', 'duration': 28.678, 'max_score': 20960.248, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k20960248.jpg'}], 'start': 20135.308, 'title': 'Machine learning applications', 'summary': "Explains supervised learning in spam detection with 97% accuracy, machine learning in voice assistants like amazon alexa, and netflix's 80% success in tv show recommendations through algorithms, along with reinforcement learning in self-driving cars and pavlo's dog training.", 'chapters': [{'end': 20329.098, 'start': 20135.308, 'title': 'Supervised learning and spam detection', 'summary': 'Explains supervised learning and its use in spam detection, highlighting the types of filters used, such as text and client filters, and mentioning the 97% accuracy of identifying an apple in a machine learning model.', 'duration': 193.79, 'highlights': ['Supervised learning involves training a machine with a labeled dataset, achieving 97% accuracy in identifying an apple in the example.', "Spam detection uses text and client filters to identify spam emails, with phrases like 'lottery' and 'free bitcoin' being flagged for removal.", "Spam filters also utilize the client's identity and history to block malicious or annoying spam emails, and blacklisting is used to prevent inbound messages from known spammers."]}, {'end': 20633.133, 'start': 20329.559, 'title': 'Machine learning and voice assistants', 'summary': "Covers the applications of supervised and unsupervised learning, including fingerprint analysis and voice-based personal assistant, focusing on amazon alexa's capabilities, voice recognition, and integration with various services and devices.", 'duration': 303.574, 'highlights': ["The chapter explains the applications of supervised and unsupervised learning, including fingerprint analysis and voice-based personal assistant, with a focus on Amazon Alexa's capabilities and integration with various services and devices.", "The Amazon Echo device, powered by Amazon Alexa, recognizes the wake word 'Alexa,' records voice commands, and processes them through Amazon's AVS, enabling a wide range of tasks and interactions.", "Amazon Alexa's integration with various online services, smart devices like Philips Hue lights, and its ability to perform tasks by voice command, such as ordering from Domino's or requesting an Uber, demonstrates its versatility and expanding list of features.", 'Unsupervised learning is exemplified through the identification of clusters and patterns in images, without the need for labeled data, while supervised learning is demonstrated through the process of saving and verifying fingerprints in machines.', "Voice-based personal assistants like Amazon Alexa rely on internet connectivity and cloud-based services for functionality, with potential limitations or dependencies on the service provider's decisions."]}, {'end': 21328.707, 'start': 20633.153, 'title': 'Netflix recommendation & self-driving cars', 'summary': "Discusses how netflix uses machine learning to recommend content, with over 80% of tv shows discovered through its algorithm, and explains reinforcement learning with examples of pavlo's dog training and the application in self-driving cars.", 'duration': 695.554, 'highlights': ['Netflix Recommendation Algorithm Netflix uses machine learning to recommend over 80% of TV shows watched on the platform, leveraging user behavior data and content tags to create taste communities and personalize recommendations.', 'Reinforcement Learning in Self-Driving Cars Reinforcement learning is utilized in training self-driving cars, with a focus on positive and negative point systems for actions, leading to safer alternatives to human-driven cars.', 'Linear Regression in Machine Learning Linear regression is discussed as a technique to find relationships between variables, with an example of the linear equation y = mx + c and the extension to multiple variables.']}], 'duration': 1193.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k20135308.jpg', 'highlights': ['Supervised learning achieves 97% accuracy in identifying an apple in the example.', "Spam detection uses text and client filters to identify spam emails with phrases like 'lottery' and 'free bitcoin' being flagged for removal.", 'Netflix uses machine learning to recommend over 80% of TV shows watched on the platform, leveraging user behavior data and content tags to create taste communities and personalize recommendations.', 'Reinforcement learning is utilized in training self-driving cars, with a focus on positive and negative point systems for actions, leading to safer alternatives to human-driven cars.']}, {'end': 24125.94, 'segs': [{'end': 21372.642, 'src': 'embed', 'start': 21348.131, 'weight': 2, 'content': [{'end': 21355.775, 'text': "So if you draw this line, then this is I mean, you see, like, there is a lot of difference between lines, right? So that's how it looks.", 'start': 21348.131, 'duration': 7.644}, {'end': 21358.577, 'text': 'And there is a concept of R squared.', 'start': 21356.035, 'duration': 2.542}, {'end': 21360.925, 'text': 'R square means mean squared error.', 'start': 21359.251, 'duration': 1.674}, {'end': 21364.678, 'text': 'Okay That is also known as goodness of fit.', 'start': 21361.248, 'duration': 3.43}, {'end': 21367.5, 'text': 'Okay That we will be covering in the next sessions.', 'start': 21365.039, 'duration': 2.461}, {'end': 21372.642, 'text': 'So you will see when R square gets minimum, that means the model has huge error.', 'start': 21367.68, 'duration': 4.962}], 'summary': 'R square measures goodness of fit, with minimum value indicating huge error.', 'duration': 24.511, 'max_score': 21348.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k21348131.jpg'}, {'end': 21467.834, 'src': 'embed', 'start': 21442.455, 'weight': 4, 'content': [{'end': 21447.156, 'text': 'so if temperature drops down, the guys will start putting more jackets.', 'start': 21442.455, 'duration': 4.701}, {'end': 21451.438, 'text': 'i mean more jackets to avoid the cold, right to keep them warm.', 'start': 21447.156, 'duration': 4.282}, {'end': 21454.098, 'text': "so that's a straightforward relation between two variables.", 'start': 21451.438, 'duration': 2.66}, {'end': 21455.879, 'text': 'this is an example of linear regression.', 'start': 21454.098, 'duration': 1.781}, {'end': 21461.581, 'text': "okay, temperature versus number of cones sold at the ice cream store, if it's, if it's, uh, gets hot at the outside,", 'start': 21455.879, 'duration': 5.702}, {'end': 21465.972, 'text': 'then the number of ice cream sold will be more right.', 'start': 21462.648, 'duration': 3.324}, {'end': 21467.834, 'text': 'inches of rain versus new car sold.', 'start': 21465.972, 'duration': 1.862}], 'summary': 'Temperature affects jacket usage and ice cream sales in a linear relationship.', 'duration': 25.379, 'max_score': 21442.455, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k21442455.jpg'}, {'end': 21671.589, 'src': 'embed', 'start': 21644.198, 'weight': 7, 'content': [{'end': 21647.239, 'text': 'that there is no linear relationship between these two data points.', 'start': 21644.198, 'duration': 3.041}, {'end': 21648.62, 'text': 'Okay These two data points.', 'start': 21647.559, 'duration': 1.061}, {'end': 21650.32, 'text': "Okay So that's how it goes.", 'start': 21648.96, 'duration': 1.36}, {'end': 21661.045, 'text': 'Okay So next we will be going to a few scenarios when we can use linear regression and when do we go for logistic regression.', 'start': 21650.681, 'duration': 10.364}, {'end': 21666.487, 'text': 'Okay These are a few types or a few factors which will help us deciding that.', 'start': 21661.445, 'duration': 5.042}, {'end': 21671.589, 'text': 'Okay So see here, first of all, it will be continuous for linear regression.', 'start': 21666.847, 'duration': 4.742}], 'summary': 'No linear relationship between two data points. exploring scenarios for using linear and logistic regression.', 'duration': 27.391, 'max_score': 21644.198, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k21644198.jpg'}, {'end': 21837.564, 'src': 'embed', 'start': 21809.076, 'weight': 1, 'content': [{'end': 21810.777, 'text': 'we are telling about our variables.', 'start': 21809.076, 'duration': 1.701}, {'end': 21815.257, 'text': 'okay, those parameters which impact the outcome, like dependent variable.', 'start': 21810.777, 'duration': 4.48}, {'end': 21822.279, 'text': "So let's say if we have 10 rows and 15 columns then 14 columns in that will be dependent independent variables.", 'start': 21815.297, 'duration': 6.982}, {'end': 21823.5, 'text': 'Those are X values.', 'start': 21822.719, 'duration': 0.781}, {'end': 21824.58, 'text': 'They can go anywhere.', 'start': 21823.54, 'duration': 1.04}, {'end': 21833.783, 'text': 'Okay, the variables, the target variables, will be the dependent variables, right when we say continuous and categorical, that is,', 'start': 21824.92, 'duration': 8.863}, {'end': 21837.564, 'text': 'that is the dependent variables, that is the Y values, that is the outcome values.', 'start': 21833.783, 'duration': 3.781}], 'summary': 'Variables impact outcomes. 14 out of 15 columns are independent. y values are the target dependent variables.', 'duration': 28.488, 'max_score': 21809.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k21809076.jpg'}, {'end': 22012.63, 'src': 'embed', 'start': 21985.432, 'weight': 5, 'content': [{'end': 21991.597, 'text': 'right. so with the help of this we will predict a housing price, correct.', 'start': 21985.432, 'duration': 6.165}, {'end': 21993.478, 'text': "let's say we have a survival data set.", 'start': 21991.597, 'duration': 1.881}, {'end': 21995.039, 'text': "let's say cancer data set.", 'start': 21993.478, 'duration': 1.561}, {'end': 21999.445, 'text': 'okay, so for predicting cancers, our data set like that.', 'start': 21995.039, 'duration': 4.406}, {'end': 22005.427, 'text': "for that we will have internal or external, let's say some kind of some things like that.", 'start': 21999.445, 'duration': 5.982}, {'end': 22005.987, 'text': 'come on again.', 'start': 22005.427, 'duration': 0.56}, {'end': 22009.509, 'text': 'it went to scratch mode, internal or external.', 'start': 22005.987, 'duration': 3.522}, {'end': 22012.63, 'text': 'right, then we will have a size.', 'start': 22009.509, 'duration': 3.121}], 'summary': 'Using a dataset, we will predict housing prices and cancer occurrences.', 'duration': 27.198, 'max_score': 21985.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k21985432.jpg'}, {'end': 22966.422, 'src': 'embed', 'start': 22936.181, 'weight': 0, 'content': [{'end': 22946.024, 'text': 'if the data is continuous and spread across a good amount of range, then only the line can help you in predicting the values right?', 'start': 22936.181, 'duration': 9.843}, {'end': 22955.807, 'text': "But instead, if it's a categorical problem, like where the data is queued in two ends or two to three ends, like, let's say, values of zero,", 'start': 22946.364, 'duration': 9.443}, {'end': 22957.868, 'text': 'and one about the best scenario that we have.', 'start': 22955.807, 'duration': 2.061}, {'end': 22966.422, 'text': 'So Typical examples of logistic regression would include tumor prediction.', 'start': 22958.388, 'duration': 8.034}], 'summary': 'For continuous data, use linear regression; for categorical data, use logistic regression for predicting tumor outcomes.', 'duration': 30.241, 'max_score': 22936.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k22936181.jpg'}, {'end': 23297.009, 'src': 'embed', 'start': 23267.392, 'weight': 3, 'content': [{'end': 23269.473, 'text': 'so this value will asymptote to 1.', 'start': 23267.392, 'duration': 2.081}, {'end': 23277.035, 'text': "so that's how the sigmoid function works, and we will establish our logistic regression functions and everything based on this.", 'start': 23269.473, 'duration': 7.562}, {'end': 23279.777, 'text': 'this function itself, okay, this function itself.', 'start': 23277.035, 'duration': 2.742}, {'end': 23280.817, 'text': 'so now what i will do.', 'start': 23279.777, 'duration': 1.04}, {'end': 23284.179, 'text': 'i will go to the ppt and will cover the things that are there.', 'start': 23280.817, 'duration': 3.362}, {'end': 23286.901, 'text': 'okay, then i will again come back to the curves and all.', 'start': 23284.179, 'duration': 2.722}, {'end': 23290.984, 'text': 'so this is how the basic concepts of logistic regression stands.', 'start': 23286.901, 'duration': 4.083}, {'end': 23297.009, 'text': 'So this is how this sigmoid function comes into picture, instead of a linear regression line.', 'start': 23291.445, 'duration': 5.564}], 'summary': 'The sigmoid function asymptotes to 1, forming the basis for logistic regression.', 'duration': 29.617, 'max_score': 23267.392, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k23267392.jpg'}], 'start': 21328.747, 'title': 'Regression analysis and modeling', 'summary': 'Covers linear and logistic regression, emphasizing their applications, differences, and use cases. it explains predictive modeling, regression analysis, and the concept of goodness of fit using the r squared method. logistic regression basics and its role in classification problems are also discussed.', 'chapters': [{'end': 21642.082, 'start': 21328.747, 'title': 'Understanding linear regression', 'summary': "Explains linear regression, including the concept of r squared, with an emphasis on its application in model performance evaluation and examples of linear relationships between variables, with the r square value indicating the model's performance.", 'duration': 313.335, 'highlights': ['Linear regression is a technique used to display the relationship between variables, with examples such as temperature versus number of jackets worn, temperature versus ice cream sales, and snowfall versus skiing park visitors. Examples of linear relationships between variables are provided, such as temperature versus number of jackets worn, temperature versus ice cream sales, and snowfall versus skiing park visitors, illustrating the application of linear regression in real-life scenarios.', "The concept of R squared, or mean squared error, is discussed, and a low R square value indicates a poor fit for the model, while a high R square value indicates a good fit. The concept of R squared, or mean squared error, is explained, where a low R square value (e.g., 0.06) indicates a poor fit for the model, while a high R square value indicates a good fit, emphasizing its significance in evaluating the model's performance.", 'The distinction between linear regression and logistic regression is made, with an example illustrating the failure of linear regression in a scenario requiring logistic regression. The distinction between linear regression and logistic regression is highlighted, with an example illustrating the failure of linear regression in a scenario requiring logistic regression, demonstrating the limitations of linear regression in certain contexts.']}, {'end': 21956.798, 'start': 21644.198, 'title': 'Linear vs logistic regression', 'summary': 'Covers the differences between linear and logistic regression, emphasizing the need for continuous variables in linear regression and categorical variables in logistic regression, along with the use cases for each, and the importance of understanding the outcome type before choosing the modeling approach.', 'duration': 312.6, 'highlights': ['Linear regression requires continuous variables and a straight line curve, while logistic regression is suitable for categorical variables and classification issues. Linear regression needs continuous variables, a good fit to some data points represented by a straight line curve, and the ability to solve regression issues, while logistic regression is appropriate for classification issues and categorical variables with skewed values.', 'Understanding the outcome type is crucial when deciding between linear and logistic regression, as linear regression is suitable for continuous variables like housing prices, while logistic regression is applicable for categorical variables like spam classification. It is important to consider the output type, with linear regression being ideal for continuous variables such as housing prices, and logistic regression being suitable for categorical variables like spam classification.', 'The distinction between continuous and categorical variables is crucial, with independent variables impacting the outcome, and the need to differentiate between dependent and independent variables when choosing the modeling approach. Understanding the distinction between continuous and categorical variables, the impact of independent variables on the outcome, and the differentiation between dependent and independent variables is essential when determining the appropriate modeling approach.']}, {'end': 22850.256, 'start': 21957.158, 'title': 'Predictive modeling and regression analysis', 'summary': 'Discusses the process of predictive modeling and regression analysis, explaining the features used for predicting housing prices and determining cancer types, and delves into the concept of goodness of fit using the r squared method in regression analysis.', 'duration': 893.098, 'highlights': ['The chapter discusses the process of predictive modeling and regression analysis The transcript covers the process of predictive modeling and regression analysis for predicting housing prices and determining cancer types.', 'Explaining the features used for predicting housing prices and determining cancer types The discussion includes the features used for predicting housing prices, such as area, garden space, number of flats, and amenities, as well as the features used for determining cancer types, including size, malignancy, and benignity.', 'Delves into the concept of goodness of fit using the R squared method in regression analysis The transcript provides a detailed explanation of the goodness of fit using the R squared method in regression analysis, involving the calculation of squared errors from the regression line and the mean of the data set, and the determination of the R squared value to evaluate the fit of the model.']}, {'end': 23267.392, 'start': 22850.256, 'title': 'Logistic regression and linear equations', 'summary': 'Covers the concept of logistic regression and linear equations, explaining the difference between supervised and unsupervised learning, the need for logistic regression in categorical problems, and the use of the sigmoid function for classification.', 'duration': 417.136, 'highlights': ['Logistic regression is used for categorical problems with skewed data, such as tumor prediction, spam classification, and fraudulent transaction detection. Logistic regression is crucial for classifying categorical data with skewed distribution, such as tumor prediction and spam classification.', 'Supervised learning involves a definite objective, while unsupervised learning provides indefinite outputs, like cluster of points. Supervised learning is goal-oriented, while unsupervised learning deals with indefinite outputs, like clusters of points.', 'The sigmoid function in logistic regression is an asymptotic curve that never reaches 0 or 1, making it suitable for classification problems. The sigmoid function in logistic regression is an asymptotic curve that never reaches 0 or 1, making it suitable for classification problems.']}, {'end': 23594.571, 'start': 23267.392, 'title': 'Logistic regression basics', 'summary': 'Covers the basics of logistic regression, including the sigmoid function, linear regression for property size prediction, and the limitations of linear regression in categorical prediction, emphasizing the need for logistic regression.', 'duration': 327.179, 'highlights': ['The sigmoid function is fundamental to logistic regression and asymptotes to 1. The sigmoid function is crucial in logistic regression, asymptoting to 1, which influences the logistic regression functions and distinguishes it from linear regression.', 'Explaining the limitations of linear regression in predicting categorical values and the need for logistic regression. The transcript emphasizes the limitations of linear regression in predicting categorical values and the need for logistic regression, as illustrated with the example of predicting neighborhood preference.', "Linear regression's application in predicting property size based on money spent and its limitations in predicting categorical values. The discussion highlights the application of linear regression in predicting property size based on money spent and its limitations in predicting categorical values like neighborhood preference."]}, {'end': 24125.94, 'start': 23594.571, 'title': 'Logistic regression for classification', 'summary': 'Introduces logistic regression as a tool for classification problems, emphasizing its application for categorical dependent variables and its role in predicting probabilities, as well as explaining the difference between univariate and multinomial problems in logistic regression.', 'duration': 531.369, 'highlights': ['Logistic regression is used for classification problems, particularly for categorical dependent variables, and can predict probabilities. Logistic regression is introduced as a tool for classification problems, specifically dealing with categorical dependent variables and providing probabilistic outputs.', 'Explanation of the difference between univariate and multinomial logistic regression problems, including examples related to spam classification. The distinction between univariate and multinomial logistic regression problems is explained, with examples related to spam classification and the consideration of single or multiple features.', 'Clarification on the asymptotic nature of logistic regression predictions, yielding outputs between 0 and 1 and the application of a threshold at 0.5 for classification. The asymptotic nature of logistic regression predictions is clarified, indicating outputs between 0 and 1 and the use of a threshold at 0.5 for classification purposes.']}], 'duration': 2797.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k21328747.jpg', 'highlights': ['Logistic regression is crucial for classifying categorical data with skewed distribution, such as tumor prediction and spam classification.', 'Understanding the distinction between continuous and categorical variables, the impact of independent variables on the outcome, and the differentiation between dependent and independent variables is essential when determining the appropriate modeling approach.', "The concept of R squared, or mean squared error, is explained, where a low R square value (e.g., 0.06) indicates a poor fit for the model, while a high R square value indicates a good fit, emphasizing its significance in evaluating the model's performance.", 'The sigmoid function in logistic regression is an asymptotic curve that never reaches 0 or 1, making it suitable for classification problems.', 'Linear regression is a technique used to display the relationship between variables, with examples such as temperature versus number of jackets worn, temperature versus ice cream sales, and snowfall versus skiing park visitors, illustrating the application of linear regression in real-life scenarios.', 'The chapter discusses the process of predictive modeling and regression analysis for predicting housing prices and determining cancer types.', 'Logistic regression is introduced as a tool for classification problems, specifically dealing with categorical dependent variables and providing probabilistic outputs.', 'The distinction between linear regression and logistic regression is highlighted, with an example illustrating the failure of linear regression in a scenario requiring logistic regression, demonstrating the limitations of linear regression in certain contexts.']}, {'end': 25246.413, 'segs': [{'end': 24184.54, 'src': 'embed', 'start': 24145.266, 'weight': 1, 'content': [{'end': 24146.848, 'text': 'Okay So this is my Jupyter notebook.', 'start': 24145.266, 'duration': 1.582}, {'end': 24150.971, 'text': "So the very first thing that I'll be doing up here is importing all the required libraries.", 'start': 24146.988, 'duration': 3.983}, {'end': 24155.275, 'text': "So I'm importing pandas and from pandas I'm importing scatter matrix.", 'start': 24151.272, 'duration': 4.003}, {'end': 24158.318, 'text': "Next I'll be importing matplotlib as PLT.", 'start': 24155.515, 'duration': 2.803}, {'end': 24169.673, 'text': 'Then we are importing model selection from sklearn classification report from sklearn.matrix confusion matrix from sklearn.matrix accuracy score again from sklearn.', 'start': 24158.678, 'duration': 10.995}, {'end': 24173.238, 'text': 'These are various machine learning models.', 'start': 24170.013, 'duration': 3.225}, {'end': 24180.337, 'text': 'KNN classifier, linear discriminant analysis, Gaussian Naive Bayes, and support vector machine.', 'start': 24174.534, 'duration': 5.803}, {'end': 24184.54, 'text': 'Okay So we are importing all these models from our scikit-learn library.', 'start': 24180.638, 'duration': 3.902}], 'summary': 'Imported pandas, matplotlib, sklearn libraries for machine learning models.', 'duration': 39.274, 'max_score': 24145.266, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k24145266.jpg'}, {'end': 24271.094, 'src': 'embed', 'start': 24243.493, 'weight': 3, 'content': [{'end': 24245.955, 'text': "So let's execute this and check the shape of our data set.", 'start': 24243.493, 'duration': 2.462}, {'end': 24249.818, 'text': 'So our data set consists of five different column and 150 rows.', 'start': 24246.135, 'duration': 3.683}, {'end': 24253.921, 'text': 'Okay Next, if you want to check the sample data set, then you have a head function over here.', 'start': 24250.078, 'duration': 3.843}, {'end': 24256.743, 'text': 'So this will give you first 20 result of your data set.', 'start': 24254.021, 'duration': 2.722}, {'end': 24258.664, 'text': 'Okay Zero to 90.', 'start': 24257.003, 'duration': 1.661}, {'end': 24261.787, 'text': 'Fine Next is the data set dot describe function.', 'start': 24258.664, 'duration': 3.123}, {'end': 24269.492, 'text': 'So this will give you the various description from your data set, like count, mean, standard deviation, minimum percentage, 25 percentile,', 'start': 24262.047, 'duration': 7.445}, {'end': 24271.094, 'text': '50 percentile, 75 percentile and max.', 'start': 24269.492, 'duration': 1.602}], 'summary': 'Data set has 5 columns, 150 rows; head function shows first 20 results; describe function provides various statistics.', 'duration': 27.601, 'max_score': 24243.493, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k24243493.jpg'}, {'end': 24601.802, 'src': 'embed', 'start': 24573.248, 'weight': 5, 'content': [{'end': 24580.01, 'text': 'so here validation size means it will split my data as 80 in one part and 20 in other.', 'start': 24573.248, 'duration': 6.762}, {'end': 24582.751, 'text': 'okay, so next we are defining a seed value.', 'start': 24580.01, 'duration': 2.741}, {'end': 24586.673, 'text': 'so the seed value is used to initialize a randomization.', 'start': 24582.751, 'duration': 3.922}, {'end': 24593.655, 'text': 'saving or setting it to same number each time guarantees that every time you execute the algorithm it will come up with the same result.', 'start': 24586.673, 'duration': 6.982}, {'end': 24601.802, 'text': 'okay, next we are defining scoring as accuracy and here here we are defining extreme x validation, y train and y validation.', 'start': 24593.655, 'duration': 8.147}], 'summary': 'Data split as 80-20, seed ensures consistent results, scoring based on accuracy.', 'duration': 28.554, 'max_score': 24573.248, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k24573248.jpg'}, {'end': 24835.858, 'src': 'embed', 'start': 24806.811, 'weight': 0, 'content': [{'end': 24811.196, 'text': 'So from this, we can say that support vector machine is having the highest accuracy.', 'start': 24806.811, 'duration': 4.385}, {'end': 24815.3, 'text': 'Okay, now our next step would be to compare the algorithm and select the best model.', 'start': 24811.216, 'duration': 4.084}, {'end': 24820.045, 'text': 'so just by looking at this output, we can say that support vector machine is having the highest accuracy.', 'start': 24815.3, 'duration': 4.745}, {'end': 24824.089, 'text': "but let's compare all these algorithm and see which one fit the best.", 'start': 24820.045, 'duration': 4.044}, {'end': 24829.655, 'text': "so for that i'll be plotting a box and viscous plot for the accuracy versus the name of the algorithm.", 'start': 24824.089, 'duration': 5.566}, {'end': 24831.915, 'text': "okay, So there's my algorithm comparison.", 'start': 24829.655, 'duration': 2.26}, {'end': 24835.858, 'text': 'Here I have the name of my algorithm and this is the accuracy for them.', 'start': 24832.075, 'duration': 3.783}], 'summary': 'Support vector machine has the highest accuracy for algorithm selection.', 'duration': 29.047, 'max_score': 24806.811, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k24806811.jpg'}, {'end': 24992.644, 'src': 'embed', 'start': 24963.057, 'weight': 6, 'content': [{'end': 24967.079, 'text': 'So let me just explain you what is precision, what is recall, what is FN score and support.', 'start': 24963.057, 'duration': 4.022}, {'end': 24974.12, 'text': 'So this precision tells how accurate or precise is a model that is out of those predicted positive.', 'start': 24967.199, 'duration': 6.921}, {'end': 24976.181, 'text': 'How many of them are actually positive.', 'start': 24974.36, 'duration': 1.821}, {'end': 24977.621, 'text': 'So this is precision.', 'start': 24976.361, 'duration': 1.26}, {'end': 24979.321, 'text': 'OK Next is recall.', 'start': 24977.961, 'duration': 1.36}, {'end': 24985.143, 'text': 'It calculates how many of the actual positives our model has captured through labeling it as positive.', 'start': 24979.661, 'duration': 5.482}, {'end': 24986.863, 'text': 'OK Next is F1 score.', 'start': 24985.303, 'duration': 1.56}, {'end': 24992.644, 'text': 'It is used when you want to seek a balance between precision and recall because of a large number of actual negative values.', 'start': 24987.023, 'duration': 5.621}], 'summary': 'Explains precision, recall, f1 score in model evaluation.', 'duration': 29.587, 'max_score': 24963.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k24963057.jpg'}, {'end': 25093.386, 'src': 'embed', 'start': 25067.233, 'weight': 7, 'content': [{'end': 25071.575, 'text': "so now let's take a look at what is classification, because that is the kind of model that we'll be building.", 'start': 25067.233, 'duration': 4.342}, {'end': 25075.516, 'text': "i'd like to help you understand what classification is.", 'start': 25071.575, 'duration': 3.941}, {'end': 25080.538, 'text': 'uh, for those of you who are unaware, classification is a machine learning.', 'start': 25075.516, 'duration': 5.022}, {'end': 25086.444, 'text': 'uh, classification is a machine learning technique that comes under the supervised learning technique.', 'start': 25080.538, 'duration': 5.906}, {'end': 25093.386, 'text': 'in supervised learning technique, you can build um, you can build regression models as well as classification models,', 'start': 25086.444, 'duration': 6.942}], 'summary': 'Classification is a supervised machine learning technique for building regression and classification models.', 'duration': 26.153, 'max_score': 25067.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25067233.jpg'}], 'start': 24125.94, 'title': 'Ml project in python', 'summary': 'Covers probability theory, importing libraries, and ml model building in python. it also includes loading a dataset, visualizing attributes, and achieving a 99% accuracy with the support vector machine model.', 'chapters': [{'end': 24184.54, 'start': 24125.94, 'title': 'Hello world ml project in python', 'summary': "Introduces the probability theory's role in predicting event occurrences and discusses the step-by-step process of starting a hello world machine learning project in python, including the import of required libraries and machine learning models.", 'duration': 58.6, 'highlights': ['Importing required libraries such as pandas, scatter matrix, and matplotlib for the hello world machine learning project.', 'Introducing various machine learning models like KNN classifier, linear discriminant analysis, Gaussian Naive Bayes, and support vector machine from the scikit-learn library.']}, {'end': 24447.946, 'start': 24184.8, 'title': 'Loading and analyzing dataset', 'summary': 'Covers loading a dataset from a url, summarizing its shape and description, and visualizing its attributes through univariate and multivariate plots, revealing insights such as 150 rows and 5 columns, and gaussian distribution in sepal length and width.', 'duration': 263.146, 'highlights': ['The dataset consists of five different columns and 150 rows, indicating the size and structure of the dataset.', 'The univariate box and whisker plot provides a clearer idea about the distribution of input attributes, showing mean values and outliers for sepal length and width.', 'The multivariate scatter plot helps to spot structured relationships between input variables, providing insights into the interaction between attributes.']}, {'end': 25246.413, 'start': 24448.007, 'title': 'Machine learning model building', 'summary': 'Discusses the process of visualizing data dependencies, splitting the dataset into training and testing parts, building and evaluating machine learning models to predict species from flower measurement, and selecting the best model, with the support vector machine demonstrating the highest accuracy of 99%.', 'duration': 798.406, 'highlights': ['The support vector machine demonstrates the highest accuracy of 99% among the models built, followed by KNN with 98% accuracy and linear discriminant analysis with 97% accuracy. The accuracy of various machine learning models, including support vector machine, KNN, and linear discriminant analysis, is highlighted, with the support vector machine demonstrating the highest accuracy of 99%.', 'The process involves splitting the dataset into training and testing parts, with 80% of the data used for training and 20% for validation, ensuring a consistent random split using a seed value. The process of splitting the dataset into training and testing parts, with 80% used for training and 20% for validation, is explained, along with the use of a seed value to ensure a consistent random split.', 'The chapter emphasizes the significance of considering various metrics beyond accuracy for selecting the best model, such as precision, recall, F1 score, and support. The chapter emphasizes the importance of considering metrics beyond accuracy, such as precision, recall, F1 score, and support, for selecting the best model.', 'The concept of classification in machine learning is explained, highlighting its application in categorizing data based on common criteria and the fixed set of outputs for mapping the input data. The explanation of the concept of classification in machine learning, including its application in categorizing data based on common criteria and the fixed set of outputs for mapping the input data, is provided.']}], 'duration': 1120.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k24125940.jpg', 'highlights': ['Achieving 99% accuracy with the support vector machine model.', 'Importing required libraries such as pandas, scatter matrix, and matplotlib for the hello world machine learning project.', 'Introducing various machine learning models like KNN classifier, linear discriminant analysis, Gaussian Naive Bayes, and support vector machine from the scikit-learn library.', 'The dataset consists of five different columns and 150 rows, indicating the size and structure of the dataset.', 'The support vector machine demonstrates the highest accuracy of 99% among the models built, followed by KNN with 98% accuracy and linear discriminant analysis with 97% accuracy.', 'The process involves splitting the dataset into training and testing parts, with 80% of the data used for training and 20% for validation, ensuring a consistent random split using a seed value.', 'The chapter emphasizes the significance of considering various metrics beyond accuracy for selecting the best model, such as precision, recall, F1 score, and support.', 'The concept of classification in machine learning is explained, highlighting its application in categorizing data based on common criteria and the fixed set of outputs for mapping the input data.']}, {'end': 26808.025, 'segs': [{'end': 25296.799, 'src': 'embed', 'start': 25266.837, 'weight': 3, 'content': [{'end': 25272.92, 'text': 'in which it takes a look at an image and figures out what are the things that are written in that image.', 'start': 25266.837, 'duration': 6.083}, {'end': 25280.183, 'text': "So it could be that it figures out what's written on a board, it could be it figure out what's written on a piece of paper, so on and so forth.", 'start': 25273.02, 'duration': 7.163}, {'end': 25281.664, 'text': "That's character recognition.", 'start': 25280.723, 'duration': 0.941}, {'end': 25284.425, 'text': 'Then comes face detection.', 'start': 25283.184, 'duration': 1.241}, {'end': 25290.053, 'text': 'So if you are unaware of face detection is if you ever used a mobile phone with a camera,', 'start': 25285.029, 'duration': 5.024}, {'end': 25296.799, 'text': "you put someone's face in that camera and it creates a bounding box around it to help you.", 'start': 25290.053, 'duration': 6.746}], 'summary': 'Ai identifies text in images & detects faces for mobile camera users.', 'duration': 29.962, 'max_score': 25266.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25266837.jpg'}, {'end': 25373.535, 'src': 'embed', 'start': 25317.696, 'weight': 0, 'content': [{'end': 25331.903, 'text': "in which you upload an image and it easy and it understands whose face is in these images and then gives you the person's name around the bounding box and tags them automatically.", 'start': 25317.696, 'duration': 14.207}, {'end': 25334.104, 'text': "so that's face recognition.", 'start': 25331.903, 'duration': 2.201}, {'end': 25335.685, 'text': 'this is face detection.', 'start': 25334.104, 'duration': 1.581}, {'end': 25337.626, 'text': 'then comes fraud detection.', 'start': 25335.685, 'duration': 1.941}, {'end': 25342.61, 'text': 'so if you work in the banking industry, fraud detection is something that is used.', 'start': 25337.626, 'duration': 4.984}, {'end': 25346.032, 'text': 'fraud detection is something in which machine learning is used quite a lot.', 'start': 25342.61, 'duration': 3.422}, {'end': 25350.613, 'text': "It takes a look at a lot of transaction, user's credit scores, so on and so forth,", 'start': 25346.612, 'duration': 4.001}, {'end': 25354.275, 'text': 'and understands what are the different transactions that could be considered fraudulent.', 'start': 25350.613, 'duration': 3.662}, {'end': 25356.776, 'text': 'Finally, we have intrusion detection.', 'start': 25355.375, 'duration': 1.401}, {'end': 25358.797, 'text': 'This is used in cybersecurity.', 'start': 25357.176, 'duration': 1.621}, {'end': 25369.181, 'text': 'What they do is they take a look at a lot of logs that it has and takes a look at the activity that was performed by particular IP addresses or a range of IP addresses in those logs.', 'start': 25359.417, 'duration': 9.764}, {'end': 25373.535, 'text': 'and understands what are the IP addresses that have tried to intrude in our system.', 'start': 25369.713, 'duration': 3.822}], 'summary': 'Face recognition, fraud detection, and intrusion detection are applications of machine learning in identifying faces, detecting fraudulent transactions, and monitoring cybersecurity.', 'duration': 55.839, 'max_score': 25317.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25317696.jpg'}, {'end': 25463.808, 'src': 'embed', 'start': 25436.145, 'weight': 5, 'content': [{'end': 25442.027, 'text': 'so, for instance, in our digital recognition example, what we do is we have 10 different categories,', 'start': 25436.145, 'duration': 5.882}, {'end': 25452.63, 'text': 'and what logistic regression would do is it would take a look at all the data points that we have given it and figure out the probability that each data point belongs to one of the following.', 'start': 25442.027, 'duration': 10.603}, {'end': 25463.808, 'text': "So, for instance, if I give it an image that contains seven, it will pick a probability of, let's say, around 0.05,, that it's one, 0.4, it's two,", 'start': 25453.366, 'duration': 10.442}], 'summary': 'Logistic regression categorizes data with 10 categories based on probabilities.', 'duration': 27.663, 'max_score': 25436.145, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25436145.jpg'}, {'end': 25544.906, 'src': 'embed', 'start': 25520.878, 'weight': 6, 'content': [{'end': 25528.262, 'text': 'It takes a look at the data and figures out what are the different columns that we have or the features that we have.', 'start': 25520.878, 'duration': 7.384}, {'end': 25532.845, 'text': 'And using those features, how can we split the data so that we can make the decisions better?', 'start': 25528.742, 'duration': 4.103}, {'end': 25540.349, 'text': 'So, for instance, if it takes a look at the temperature previous days and then figures out whether okay,', 'start': 25532.865, 'duration': 7.484}, {'end': 25544.906, 'text': 'In the previous days the temperature was 37 degrees.', 'start': 25541.184, 'duration': 3.722}], 'summary': 'Analyzing data to make better decisions using temperature as a feature.', 'duration': 24.028, 'max_score': 25520.878, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25520878.jpg'}, {'end': 25692.199, 'src': 'embed', 'start': 25645.702, 'weight': 4, 'content': [{'end': 25652.246, 'text': "use these algorithms that have already been built for you and then figure out what's the accuracy and how well they predict your data.", 'start': 25645.702, 'duration': 6.544}, {'end': 25654.787, 'text': 'so now we finally come to scikit-learn.', 'start': 25652.246, 'duration': 2.541}, {'end': 25655.768, 'text': 'so what is scikit-learn?', 'start': 25654.787, 'duration': 0.981}, {'end': 25663.008, 'text': 'Scikit-learn is a free, open source machine learning library that contains generic implementation of common machine learning algorithms.', 'start': 25656.544, 'duration': 6.464}, {'end': 25672.294, 'text': "So as we've already discussed, there are a lot of algorithms that you can use in order to build a machine learning model.", 'start': 25663.849, 'duration': 8.445}, {'end': 25678.978, 'text': 'But writing those algorithms from scratch by yourself is going to be really, really difficult.', 'start': 25672.694, 'duration': 6.284}, {'end': 25684.542, 'text': 'So instead of that, what we do is we use Scikit-learn.', 'start': 25680.399, 'duration': 4.143}, {'end': 25692.199, 'text': 'Scikit-learn allows us to understand, allows us to import the algorithms ourselves after understanding the data,', 'start': 25685.578, 'duration': 6.621}], 'summary': 'Scikit-learn is a free machine learning library with generic algorithms, helping to predict data accurately.', 'duration': 46.497, 'max_score': 25645.702, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25645702.jpg'}, {'end': 25984.269, 'src': 'embed', 'start': 25962.355, 'weight': 9, 'content': [{'end': 25972.564, 'text': "it's got an extensive documentation that contains information about how to use every available feature in the scikit-learn library and using that,", 'start': 25962.355, 'duration': 10.209}, {'end': 25975.225, 'text': 'we can take a look at that.', 'start': 25972.564, 'duration': 2.661}, {'end': 25978.086, 'text': "then the it's been created by experts,", 'start': 25975.225, 'duration': 2.861}, {'end': 25984.269, 'text': 'so scikit-learn is a library that has been created by expert data scientists who have been inside the data science business for a long time.', 'start': 25978.086, 'duration': 6.183}], 'summary': "Scikit-learn's extensive documentation by expert data scientists aids in feature utilization.", 'duration': 21.914, 'max_score': 25962.355, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25962355.jpg'}, {'end': 26089.463, 'src': 'embed', 'start': 26047.571, 'weight': 7, 'content': [{'end': 26050.012, 'text': 'This is an important concept.', 'start': 26047.571, 'duration': 2.441}, {'end': 26052.493, 'text': "Now let's take a look at steps in building a classifier.", 'start': 26050.352, 'duration': 2.141}, {'end': 26063.303, 'text': 'there are five steps in building a classifier, um, but there are several steps in building a classic, uh, five to be exact,', 'start': 26054.318, 'duration': 8.985}, {'end': 26066.824, 'text': "but depending on how you're training your model, it could be more and could be less.", 'start': 26063.303, 'duration': 3.521}, {'end': 26069.045, 'text': 'usually, these are the five generic steps that are used.', 'start': 26066.824, 'duration': 2.221}, {'end': 26070.786, 'text': 'the first thing is we import the library.', 'start': 26069.045, 'duration': 1.741}, {'end': 26075.769, 'text': "so if you're using sql and numpy panda to import it, many people import it throughout their code.", 'start': 26070.786, 'duration': 4.983}, {'end': 26079.251, 'text': "so as soon as they need something, only then they'll import it.", 'start': 26075.769, 'duration': 3.482}, {'end': 26085.961, 'text': "i like to import everything at the top of my file so that everyone who's using my application knows, looking at the code that i've written,", 'start': 26079.251, 'duration': 6.71}, {'end': 26089.463, 'text': 'knows what are the libraries that my code depends on.', 'start': 26085.961, 'duration': 3.502}], 'summary': 'Building a classifier involves five generic steps, including importing libraries at the top of the file.', 'duration': 41.892, 'max_score': 26047.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k26047571.jpg'}, {'end': 26178.889, 'src': 'embed', 'start': 26151.694, 'weight': 12, 'content': [{'end': 26157.579, 'text': "And using that, we understand what is the how the model has learned and what is the accuracy that it's giving us.", 'start': 26151.694, 'duration': 5.885}, {'end': 26162.901, 'text': 'Then comes training the model, which is the task of the scikit-learn library.', 'start': 26158.038, 'duration': 4.863}, {'end': 26166.783, 'text': 'It creates an algorithm and allows us to feed it data so that it can learn from the data.', 'start': 26162.921, 'duration': 3.862}, {'end': 26173.606, 'text': 'And after learning from the data, it can do the classification or regression tasks that are needed.', 'start': 26167.463, 'duration': 6.143}, {'end': 26175.347, 'text': 'Finally, we test the model.', 'start': 26173.966, 'duration': 1.381}, {'end': 26178.889, 'text': 'We give it the data that we had set aside for testing.', 'start': 26175.507, 'duration': 3.382}], 'summary': 'Model training with scikit-learn library, achieving accurate classification and regression tasks.', 'duration': 27.195, 'max_score': 26151.694, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k26151694.jpg'}, {'end': 26345.588, 'src': 'embed', 'start': 26319.945, 'weight': 11, 'content': [{'end': 26324.868, 'text': 'So for training and testing, you can split the data into depending on how large your data set is.', 'start': 26319.945, 'duration': 4.923}, {'end': 26329.83, 'text': "If it's really large, like you have millions or billions of rows, then you can just take 1% or 10% of your data.", 'start': 26324.928, 'duration': 4.902}, {'end': 26334.525, 'text': "If it's a small data set, then 70-30 is a good ratio to split your data.", 'start': 26331.164, 'duration': 3.361}, {'end': 26337.266, 'text': "It's testing set, which is the correct answer.", 'start': 26335.025, 'duration': 2.241}, {'end': 26339.146, 'text': 'The training set is used to train the model.', 'start': 26337.466, 'duration': 1.68}, {'end': 26345.588, 'text': 'The testing set is used to figure out whether or not the answer is, whether or not the model is performing well, which is why we use it.', 'start': 26339.166, 'duration': 6.422}], 'summary': 'For large datasets, consider using 1% or 10% for testing. small datasets are split 70-30 for testing and training purposes.', 'duration': 25.643, 'max_score': 26319.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k26319945.jpg'}], 'start': 25248.613, 'title': 'Common classification problems and popular algorithms', 'summary': 'Discusses common classification problems such as character recognition, face detection, fraud detection, and intrusion detection, and popular classification algorithms like logistic regression, decision tree, random forest, and naive bayes, emphasizing their applications and significance in various industries, along with the use of scikit-learn for model building and training process.', 'chapters': [{'end': 25410.759, 'start': 25248.613, 'title': 'Common classification problems', 'summary': 'Discusses common classification problems such as character recognition, face detection, fraud detection, and intrusion detection, highlighting their applications and significance in various industries.', 'duration': 162.146, 'highlights': ['Fraud detection in the banking industry utilizes machine learning to analyze transactions and credit scores to identify potentially fraudulent activities.', "Face recognition, exemplified by Facebook's feature, automatically identifies individuals in uploaded images and tags them accordingly, showcasing the practical application of machine learning in this area.", 'Intrusion detection in cybersecurity involves analyzing activity logs and identifying and blacklisting IP addresses attempting to intrude into the system, demonstrating the importance of machine learning in maintaining system security.', 'Character recognition, such as that used in Google Lens, involves interpreting images to identify and decipher written characters, showcasing the practical application of machine learning in this field.']}, {'end': 25749.959, 'start': 25411.199, 'title': 'Popular classification algorithms and scikit-learn', 'summary': 'Discusses popular classification algorithms such as logistic regression, decision tree, random forest, and naive bayes, along with the use of scikit-learn, a free, open-source machine learning library containing generic implementations of common machine learning algorithms.', 'duration': 338.76, 'highlights': ['Scikit-learn is a free, open-source machine learning library that contains generic implementation of common machine learning algorithms. Scikit-learn is a free, open-source machine learning library that provides generic implementations of common machine learning algorithms, making it easier to implement machine learning models without having to write the algorithms from scratch.', 'Logistic regression learns from data using a log loss function to make predictions about the probability of data points belonging to specific categories, such as in a digital recognition example with 10 different categories. Logistic regression learns from data using a log loss function to make predictions about the probability of data points belonging to specific categories, such as in a digital recognition example with 10 different categories, allowing for the selection of the category with the highest probability.', 'Decision tree algorithm splits data based on features to make decisions, making it easy to conceptualize and use for classification tasks. The decision tree algorithm splits data based on features to make decisions, offering a simple and intuitive way to conceptualize and use for classification tasks, such as predicting weather based on temperature.']}, {'end': 26070.786, 'start': 25801.664, 'title': 'Scikit-learn for machine learning', 'summary': 'Discusses the use of scikit-learn for building machine learning models, revealing that it is possible to build a machine learning model using only scikit-learn, and highlights the advantages of scikit-learn including its ease of use, extensive documentation, and reliability due to being created by expert data scientists and being well tested through its open-source nature.', 'duration': 269.122, 'highlights': ['Building a Classifier The chapter outlines the five generic steps in building a classifier, including importing the library, preparing the data, selecting the model, training the model, and making predictions, emphasizing that these steps may vary based on how the model is trained.', 'Using Scikit-learn for Machine Learning The chapter addresses the question of whether it is possible to build a machine learning model using only scikit-learn, revealing that it is indeed possible, albeit challenging, without using other libraries like NumPy or Pandas, and emphasizes the difficulty of coding without these libraries.', 'Advantages of Scikit-learn The chapter highlights the advantages of scikit-learn, including its ease of use, extensive documentation, expertise-driven creation, and reliability due to being well tested through its open-source nature, indicating that it contains all the implementation of mathematical algorithms and is created by expert data scientists who have been in the data science business for a long time, while also being well tested through contributions from developers across the world.']}, {'end': 26808.025, 'start': 26070.786, 'title': 'Machine learning model training process', 'summary': 'Covers the process of training a machine learning model, including importing libraries, dataset import and manipulation, data splitting, model training, and testing, with a focus on using scikit-learn for algorithm creation and accuracy assessment.', 'duration': 737.239, 'highlights': ['Importing Libraries Importing libraries at the top of the file ensures clarity for code dependencies, aiding in understanding and usage, promoting transparency and efficient code development.', 'Data Splitting Splitting data into training and testing sets, typically using a 70-30 ratio, aids in training the model on a subset and evaluating accuracy on another subset, with the testing set used to assess model performance.', "Model Training and Testing Using scikit-learn for model training involves creating an algorithm, feeding it data for learning, and subsequently testing the model's accuracy, with 100% accuracy being unattainable and variances impacting accuracy."]}], 'duration': 1559.412, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k25248613.jpg', 'highlights': ['Fraud detection in banking uses ML to analyze transactions and credit scores for identifying fraudulent activities.', "Face recognition, like Facebook's feature, showcases practical ML applications in identifying individuals in images.", "Intrusion detection in cybersecurity involves analyzing activity logs and blacklisting IP addresses, demonstrating ML's role in system security.", 'Character recognition, as in Google Lens, demonstrates practical ML applications in interpreting images to identify written characters.', 'Scikit-learn is an open-source ML library with generic implementations of common algorithms, simplifying model building.', "Logistic regression uses a log loss function to predict data points' category probabilities, aiding in digital recognition tasks.", 'Decision tree algorithm splits data based on features, offering a simple and intuitive way for classification tasks.', 'Building a Classifier outlines generic steps including importing the library, preparing data, selecting the model, training, and making predictions.', 'Using Scikit-learn for Machine Learning reveals the possibility of building ML models solely with scikit-learn, albeit challenging.', 'Advantages of Scikit-learn include ease of use, extensive documentation, expertise-driven creation, and reliability due to being well tested.', 'Importing Libraries at the top of the file ensures clarity for code dependencies, aiding in understanding and usage.', 'Data Splitting into training and testing sets aids in model training and evaluating accuracy, typically using a 70-30 ratio.', "Model Training and Testing using scikit-learn involves creating an algorithm, feeding it data for learning, and subsequently testing the model's accuracy."]}, {'end': 28838.344, 'segs': [{'end': 26830.805, 'src': 'embed', 'start': 26808.065, 'weight': 0, 'content': [{'end': 26816.009, 'text': 'Phase recognition is a system that uses classification algorithms along with other algorithms as well.', 'start': 26808.065, 'duration': 7.944}, {'end': 26819.278, 'text': 'Is computer vision in area of ML? Yes, it is.', 'start': 26817.017, 'duration': 2.261}, {'end': 26825.842, 'text': 'So what is NumPy? Well, a NumPy is a most widely used Python library for linear algebra.', 'start': 26820.159, 'duration': 5.683}, {'end': 26830.805, 'text': 'It is used for performing mathematical and logical operations on multidimensional array.', 'start': 26826.122, 'duration': 4.683}], 'summary': 'Phase recognition system uses classification algorithms and numpy for mathematical operations on multidimensional arrays.', 'duration': 22.74, 'max_score': 26808.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k26808065.jpg'}, {'end': 26913.499, 'src': 'embed', 'start': 26855.348, 'weight': 1, 'content': [{'end': 26857.729, 'text': 'This would make things more easier for you to understand.', 'start': 26855.348, 'duration': 2.381}, {'end': 26860.389, 'text': 'So this is my Jupyter Notebook.', 'start': 26858.629, 'duration': 1.76}, {'end': 26868.031, 'text': "So the very first thing that I'll do up here is define my one dimensional array.", 'start': 26864.43, 'duration': 3.601}, {'end': 26880.979, 'text': 'So how to define a 1D array? So for that, the very first thing that I have to do is import my numpy library.', 'start': 26873.672, 'duration': 7.307}, {'end': 26884.142, 'text': "So I'll be importing numpy as np.", 'start': 26881.419, 'duration': 2.723}, {'end': 26892.25, 'text': "Why I'm using np up here? So it's just because whenever I want to call some function of numpy, I don't want to use numpy every time.", 'start': 26884.843, 'duration': 7.407}, {'end': 26897.194, 'text': 'So apart from it, what I can do, I can just replace the word numpy by np.', 'start': 26892.93, 'duration': 4.264}, {'end': 26906.974, 'text': "So once you have imported NumPy as NP, let's define an array as A equal NP dot array.", 'start': 26898.547, 'duration': 8.427}, {'end': 26912.959, 'text': 'So here instead of writing NumPy dot array, I can just go ahead and write NP dot array.', 'start': 26907.775, 'duration': 5.184}, {'end': 26913.499, 'text': "That's it.", 'start': 26913.139, 'duration': 0.36}], 'summary': 'Using np instead of numpy for defining 1d array makes it easier to understand and write code.', 'duration': 58.151, 'max_score': 26855.348, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k26855348.jpg'}, {'end': 27093.288, 'src': 'embed', 'start': 27058.373, 'weight': 3, 'content': [{'end': 27060.974, 'text': 'import numpy as np.', 'start': 27058.373, 'duration': 2.601}, {'end': 27067.242, 'text': "Now, here I'll be using a function ZEROS.", 'start': 27062.079, 'duration': 5.163}, {'end': 27070.464, 'text': 'Well, this function is predefined in NumPy.', 'start': 27068.083, 'duration': 2.381}, {'end': 27075.408, 'text': 'This is used to initialize all the elements of an array to zero.', 'start': 27071.165, 'duration': 4.243}, {'end': 27079.47, 'text': 'So np.zeros.', 'start': 27076.068, 'duration': 3.402}, {'end': 27082.752, 'text': 'Inside this, it takes a list of list.', 'start': 27080.151, 'duration': 2.601}, {'end': 27087.075, 'text': "So inside this, what I'll pass up here is the size of my array.", 'start': 27083.413, 'duration': 3.662}, {'end': 27090.437, 'text': "Let's take the size of the array as 3x4.", 'start': 27087.615, 'duration': 2.822}, {'end': 27091.558, 'text': "Okay, that's it.", 'start': 27090.797, 'duration': 0.761}, {'end': 27093.288, 'text': "You don't need to do anything else.", 'start': 27091.988, 'duration': 1.3}], 'summary': 'Using np.zeros to initialize array elements to zero, with a size of 3x4.', 'duration': 34.915, 'max_score': 27058.373, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k27058373.jpg'}, {'end': 27243.972, 'src': 'embed', 'start': 27194.309, 'weight': 4, 'content': [{'end': 27198.091, 'text': 'We want to print all the even numbers between 10 and 20.', 'start': 27194.309, 'duration': 3.782}, {'end': 27199.131, 'text': "So let's see how we can do it.", 'start': 27198.091, 'duration': 1.04}, {'end': 27205.453, 'text': 'So here we are printing even numbers between 10 and 20.', 'start': 27199.851, 'duration': 5.602}, {'end': 27211.837, 'text': "Okay to tell you you don't need to import numpy every time you're doing it.", 'start': 27205.453, 'duration': 6.384}, {'end': 27213.938, 'text': "okay, i'll just show up here.", 'start': 27211.837, 'duration': 2.101}, {'end': 27220.221, 'text': "if you're not importing numpy, it won't throw an error, because python is executing every statement line by line, right.", 'start': 27213.938, 'duration': 6.283}, {'end': 27222.522, 'text': 'so here numpy is already executed.', 'start': 27220.221, 'duration': 2.301}, {'end': 27224.763, 'text': 'so i can use np up here.', 'start': 27222.522, 'duration': 2.241}, {'end': 27226.263, 'text': "i'll just write np.", 'start': 27224.763, 'duration': 1.5}, {'end': 27235.806, 'text': 'arrange inside this it should start from 10 right and it should go till 20 and The difference should be 2,', 'start': 27226.263, 'duration': 9.543}, {'end': 27238.388, 'text': 'as we are considering the even numbers right?', 'start': 27235.806, 'duration': 2.582}, {'end': 27240.309, 'text': 'Just a quick info, guys.', 'start': 27239.308, 'duration': 1.001}, {'end': 27243.972, 'text': 'Test your knowledge of data science by answering this question.', 'start': 27241.09, 'duration': 2.882}], 'summary': 'Printing even numbers between 10 and 20 using numpy arrange.', 'duration': 49.663, 'max_score': 27194.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k27194309.jpg'}, {'end': 27340.338, 'src': 'embed', 'start': 27311.632, 'weight': 6, 'content': [{'end': 27313.533, 'text': "okay, let's see how to do it.", 'start': 27311.632, 'duration': 1.901}, {'end': 27318.721, 'text': 'So for arranging Z numbers up here we have a function as line space.', 'start': 27314.739, 'duration': 3.982}, {'end': 27322.263, 'text': 'So you can call NP dot line space.', 'start': 27319.962, 'duration': 2.301}, {'end': 27326.505, 'text': "It's not LA any, okay.", 'start': 27324.824, 'duration': 1.681}, {'end': 27330.006, 'text': "It's LA and don't make this mistake.", 'start': 27327.005, 'duration': 3.001}, {'end': 27333.728, 'text': 'Fine And after that, what do you do? You will specify the first number.', 'start': 27330.346, 'duration': 3.382}, {'end': 27340.338, 'text': 'the ending number, that is, the value of X and Y, and how many numbers you want between them.', 'start': 27334.913, 'duration': 5.425}], 'summary': 'Use np.linspace to arrange z numbers with specified start, end, and step values.', 'duration': 28.706, 'max_score': 27311.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k27311632.jpg'}, {'end': 27462.323, 'src': 'embed', 'start': 27437.116, 'weight': 7, 'content': [{'end': 27442.403, 'text': "so again, i won't be importing numpy up here, i'll just write np dot.", 'start': 27437.116, 'duration': 5.287}, {'end': 27446.728, 'text': 'so for filling the same number, we have a predefined function as full.', 'start': 27442.403, 'duration': 4.325}, {'end': 27452.436, 'text': 'it takes two parameters First the dimension of the array and next the element you want to fill in.', 'start': 27446.728, 'duration': 5.708}, {'end': 27457.7, 'text': 'So, suppose my dimension of the array is 2 x 3.', 'start': 27452.956, 'duration': 4.744}, {'end': 27462.323, 'text': "Okay? And what value I want to fill in? Let's say I want to fill all the values with 6.", 'start': 27457.7, 'duration': 4.623}], 'summary': 'Using the full function, fill a 2x3 array with the value 6.', 'duration': 25.207, 'max_score': 27437.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k27437116.jpg'}, {'end': 27524.843, 'src': 'embed', 'start': 27486.252, 'weight': 8, 'content': [{'end': 27493.357, 'text': 'This course is of very high quality and cost effective as it is taught by IIT professors and industry experts.', 'start': 27486.252, 'duration': 7.105}, {'end': 27501.882, 'text': 'So now what if I want to initialize my array with some random numbers? How can I do that? Well, for that, you have a random function in NumPy.', 'start': 27493.697, 'duration': 8.185}, {'end': 27503.443, 'text': "Let's see how you can do it.", 'start': 27502.202, 'duration': 1.241}, {'end': 27524.843, 'text': "We are filling some random number Array dimension X cross Y Okay, let's see how we can do it.", 'start': 27509.351, 'duration': 15.492}], 'summary': 'High-quality, cost-effective course taught by iit professors and industry experts. utilizes numpy random function for array initialization.', 'duration': 38.591, 'max_score': 27486.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k27486252.jpg'}, {'end': 27582.607, 'src': 'embed', 'start': 27553.57, 'weight': 9, 'content': [{'end': 27555.191, 'text': "correct, let's move ahead.", 'start': 27553.57, 'duration': 1.621}, {'end': 27559.112, 'text': 'so this was all about how you can initialize a numpy array.', 'start': 27555.191, 'duration': 3.921}, {'end': 27564.593, 'text': "okay, numpy array inspection and we'll see how we can inspect various aspect of a numpy array.", 'start': 27559.112, 'duration': 5.481}, {'end': 27567.017, 'text': "Okay, so let's start it.", 'start': 27565.276, 'duration': 1.741}, {'end': 27572.06, 'text': 'So the very first thing we got up here is any array dot shape.', 'start': 27568.798, 'duration': 3.262}, {'end': 27577.484, 'text': 'Well, this shape function is used to return a tuple consisting of array dimension.', 'start': 27572.761, 'duration': 4.723}, {'end': 27579.985, 'text': 'It can also be used to resize the array.', 'start': 27578.004, 'duration': 1.981}, {'end': 27582.607, 'text': "Let's check the shape of our array.", 'start': 27580.746, 'duration': 1.861}], 'summary': 'Introduction to numpy array initialization and inspection, including the use of the shape function to return array dimensions.', 'duration': 29.037, 'max_score': 27553.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k27553570.jpg'}, {'end': 28433.529, 'src': 'embed', 'start': 28401.265, 'weight': 11, 'content': [{'end': 28403.708, 'text': "I'm assuming all these things are clear to you right?", 'start': 28401.265, 'duration': 2.443}, {'end': 28410.544, 'text': "In case you have any doubt, feel free to reach out to us and we'll make sure that the doubt is clear to you within 24 hours.", 'start': 28404.383, 'duration': 6.161}, {'end': 28414.845, 'text': 'Okay So next we have is we want to find the data type of the array.', 'start': 28411.024, 'duration': 3.821}, {'end': 28416.646, 'text': "So let's see how to do it.", 'start': 28415.485, 'duration': 1.161}, {'end': 28426.508, 'text': 'So here we have is find the data type of the array.', 'start': 28420.226, 'duration': 6.282}, {'end': 28433.529, 'text': "Right So let's take an example up here.", 'start': 28427.848, 'duration': 5.681}], 'summary': 'Clarify doubts within 24 hours; find data type of the array.', 'duration': 32.264, 'max_score': 28401.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k28401265.jpg'}, {'end': 28532.771, 'src': 'embed', 'start': 28497.884, 'weight': 12, 'content': [{'end': 28500.545, 'text': 'So this was all about NumPy array inspection.', 'start': 28497.884, 'duration': 2.661}, {'end': 28502.305, 'text': 'NumPy array mathematics.', 'start': 28500.925, 'duration': 1.38}, {'end': 28506.686, 'text': "We'll see what are the various mathematical function that we can perform using NumPy.", 'start': 28502.825, 'duration': 3.861}, {'end': 28508.367, 'text': "Okay So let's see.", 'start': 28507.167, 'duration': 1.2}, {'end': 28514.709, 'text': 'So first we have is addition using NumPy.', 'start': 28511.568, 'duration': 3.141}, {'end': 28518.51, 'text': 'So for performing some addition, we have a sum function up here.', 'start': 28515.129, 'duration': 3.381}, {'end': 28519.99, 'text': "Let's see how does it work.", 'start': 28518.99, 'duration': 1}, {'end': 28532.771, 'text': 'numpy array mathematics, and in this we are learning about addition.', 'start': 28526.866, 'duration': 5.905}], 'summary': 'Learning about numpy array mathematics and addition.', 'duration': 34.887, 'max_score': 28497.884, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k28497884.jpg'}, {'end': 28788.308, 'src': 'embed', 'start': 28730.216, 'weight': 13, 'content': [{'end': 28735.839, 'text': 'now, to perform subtraction, we have a method as so we have to call the function subtract.', 'start': 28730.216, 'duration': 5.623}, {'end': 28741.787, 'text': "let's see an example like np dot subtract, what do you want to subtract?", 'start': 28735.839, 'duration': 5.948}, {'end': 28745.79, 'text': 'like any value, like 10, comma 20.', 'start': 28741.787, 'duration': 4.003}, {'end': 28749.873, 'text': "so we'll get the output as minus 10, 10, minus 20.", 'start': 28745.79, 'duration': 4.083}, {'end': 28751.935, 'text': 'so you got the value as minus 10.', 'start': 28749.873, 'duration': 2.062}, {'end': 28761.242, 'text': "okay, similarly, what if i want to find the multiplication of two number, i'll just write np dot, multiply two comma three.", 'start': 28751.935, 'duration': 9.307}, {'end': 28768.637, 'text': "let's take two number, multiplication of two number, we'll get the output as 6.", 'start': 28761.242, 'duration': 7.395}, {'end': 28774.22, 'text': 'let me just add one more heading up here.', 'start': 28768.637, 'duration': 5.583}, {'end': 28783.005, 'text': 'all other numpy mathematics function.', 'start': 28774.22, 'duration': 8.785}, {'end': 28786.167, 'text': 'so inside this we saw for multiply.', 'start': 28783.005, 'duration': 3.162}, {'end': 28788.308, 'text': "now let's check out for division.", 'start': 28786.167, 'duration': 2.141}], 'summary': 'The transcript covers examples of subtraction, multiplication, and division using numpy functions, demonstrating results like -10, -20, and 6.', 'duration': 58.092, 'max_score': 28730.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k28730216.jpg'}], 'start': 26808.065, 'title': 'Numpy fundamentals', 'summary': 'Covers the fundamentals of numpy, including creating arrays, initializing arrays with specific values, array operations, array dimensions, data types, and mathematical operations. it explains the use of numpy for linear algebra and provides examples in a jupyter notebook.', 'chapters': [{'end': 26991.581, 'start': 26808.065, 'title': 'Understanding numpy and creating arrays', 'summary': 'Introduces numpy as the most widely used python library for linear algebra, detailing its use for performing mathematical and logical operations on multidimensional arrays and demonstrating the creation of 1d and 2d arrays in a jupyter notebook.', 'duration': 183.516, 'highlights': ['Introduction to NumPy as the most widely used Python library for linear algebra Most widely used Python library for linear algebra', 'Demonstration of creating 1D and 2D arrays in a Jupyter Notebook Creation of 1D and 2D arrays', "Explanation of using 'np' as a shorthand for 'numpy' when calling functions Use of 'np' as shorthand for 'numpy'"]}, {'end': 27275.578, 'start': 26992.081, 'title': 'Initializing numpy arrays and creating sequences', 'summary': 'Covers initializing a numpy array with all values as zero, creating a sequence of numbers with a specified interval, and printing even numbers between 10 and 20 using numpy.arange function.', 'duration': 283.497, 'highlights': ['Creating a 3x4 matrix with all elements initialized to 0 using numpy.zeros function. The speaker demonstrates initializing a 3x4 matrix with all elements set to 0 using the np.zeros function, showcasing the process and the resulting matrix.', 'Creating a sequence of numbers between 1 and 10 with an interval of 2 using numpy.arange function. The tutorial presents the creation of a sequence of numbers between 1 and 10 with an interval of 2 using the np.arange function, providing the specific parameters and the resulting sequence.', 'Printing all even numbers between 10 and 20 using numpy.arange function. The speaker explains how to print all even numbers between 10 and 20 using the np.arange function, emphasizing the specific parameters required and the output sequence.']}, {'end': 27917.8, 'start': 27275.918, 'title': 'Numpy array operations', 'summary': 'Covers array operations in numpy, including arranging numbers between fixed values, filling arrays with the same and random numbers, and inspecting array dimensions, with examples and explanations provided for each operation.', 'duration': 641.882, 'highlights': ['NumPy line space function is used to arrange Z numbers between fixed values X and Y, with examples demonstrating the output for different number arrangements.', "The 'full' function in NumPy is utilized to fill an array of dimensions X cross Y with the same specified number, demonstrated with an example output of a 2 x 3 array filled with the number 6.", "The 'random' function in NumPy is employed to fill an array of dimensions X cross Y with random values, exemplified by a 2 x 3 array containing various random values.", "The 'shape' function is utilized to inspect the dimensions of an array, with examples demonstrating the process of checking and resizing the array dimensions, including explanations and outputs for different array sizes and reshaping operations."]}, {'end': 28497.364, 'start': 27917.8, 'title': 'Array dimension and data type', 'summary': "Covers how to determine the dimension of an array using numpy's 'ndim' function, including reshaping the array and calculating the number of elements using the 'size' function. it also demonstrates how to find the data type of the elements in the array using the 'dtype' attribute of numpy arrays.", 'duration': 579.564, 'highlights': ["The chapter covers how to determine the dimension of an array using numpy's 'ndim' function, including reshaping the array and calculating the number of elements using the 'size' function. Demonstrates the usage of numpy's 'ndim' function to find the dimension of an array. Illustrates the process of reshaping an array using numpy. Discusses calculating the number of elements in an array using the 'size' function.", "It also demonstrates how to find the data type of the elements in the array using the 'dtype' attribute of numpy arrays. Highlights the process of determining the data type of array elements using the 'dtype' attribute of numpy arrays. Discusses the impact of changing data types on the array elements."]}, {'end': 28838.344, 'start': 28497.884, 'title': 'Numpy array mathematics', 'summary': 'Covers numpy array inspection, addition, subtraction, multiplication, division, and other mathematical functions. it explains how to perform these operations using numpy arrays, including examples and potential errors to avoid.', 'duration': 340.46, 'highlights': ['Performing addition using NumPy Explains how to perform addition using NumPy, including using the sum function and dealing with potential errors.', 'Performing subtraction using NumPy Demonstrates the method for performing subtraction using NumPy, with an example of subtracting two numbers.', 'Performing multiplication using NumPy Illustrates how to use the multiply function to perform multiplication of two numbers using NumPy.', 'Performing division using NumPy Explains the process of division using the divide function, including an example of dividing two numbers and obtaining the result.', 'Other NumPy mathematics functions Mentions the availability of various other mathematical functions like exponent, square root, sine, cosine, and logarithm, encouraging further exploration.']}], 'duration': 2030.279, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k26808065.jpg', 'highlights': ['Introduction to NumPy as the most widely used Python library for linear algebra', 'Demonstration of creating 1D and 2D arrays in a Jupyter Notebook', "Explanation of using 'np' as a shorthand for 'numpy' when calling functions", 'Creating a 3x4 matrix with all elements initialized to 0 using numpy.zeros function', 'Creating a sequence of numbers between 1 and 10 with an interval of 2 using numpy.arange function', 'Printing all even numbers between 10 and 20 using numpy.arange function', 'NumPy line space function is used to arrange Z numbers between fixed values X and Y', "The 'full' function in NumPy is utilized to fill an array of dimensions X cross Y with the same specified number", "The 'random' function in NumPy is employed to fill an array of dimensions X cross Y with random values", "The 'shape' function is utilized to inspect the dimensions of an array", "The chapter covers how to determine the dimension of an array using numpy's 'ndim' function", "It also demonstrates how to find the data type of the elements in the array using the 'dtype' attribute of numpy arrays", 'Performing addition using NumPy', 'Performing subtraction using NumPy', 'Performing multiplication using NumPy', 'Performing division using NumPy', 'Other NumPy mathematics functions']}, {'end': 30982.618, 'segs': [{'end': 28863.816, 'src': 'embed', 'start': 28840.256, 'weight': 0, 'content': [{'end': 28847.182, 'text': 'So here all the operation that we perform with sum can also be performed with subtract, multiply, divide or anything.', 'start': 28840.256, 'duration': 6.926}, {'end': 28850.666, 'text': "Okay It's like here we use a variable.", 'start': 28847.443, 'duration': 3.223}, {'end': 28853.568, 'text': 'So here we are summing up two array with axis zero.', 'start': 28850.786, 'duration': 2.782}, {'end': 28855.23, 'text': 'Here we are summing up axis one.', 'start': 28853.668, 'duration': 1.562}, {'end': 28861.794, 'text': 'So all those functions can even be done with np.subtract, np.multiply or np.divide.', 'start': 28855.748, 'duration': 6.046}, {'end': 28863.816, 'text': 'Let me just show you with one example.', 'start': 28862.194, 'duration': 1.622}], 'summary': 'Operations like sum, subtract, multiply, and divide can be performed using np functions and examples will be demonstrated.', 'duration': 23.56, 'max_score': 28840.256, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k28840256.jpg'}, {'end': 28977.375, 'src': 'embed', 'start': 28948.633, 'weight': 1, 'content': [{'end': 28952.635, 'text': 'and similarly, log function to find out the log value of the array.', 'start': 28948.633, 'duration': 4.002}, {'end': 28954.055, 'text': "Let's see them one by one again.", 'start': 28952.635, 'duration': 1.42}, {'end': 28955.276, 'text': "So there's our array.", 'start': 28954.315, 'duration': 0.961}, {'end': 28957.677, 'text': 'We have already defined it.', 'start': 28955.496, 'duration': 2.181}, {'end': 28960.418, 'text': "So let's use all these functions one by one.", 'start': 28957.677, 'duration': 2.741}, {'end': 28963.199, 'text': 'print NP dot, exponent of.', 'start': 28960.418, 'duration': 2.781}, {'end': 28965.81, 'text': 'okay, execute it.', 'start': 28964.269, 'duration': 1.541}, {'end': 28968.891, 'text': 'so this is the exponent of a.', 'start': 28965.81, 'duration': 3.081}, {'end': 28970.432, 'text': 'that is, e to the power a.', 'start': 28968.891, 'duration': 1.541}, {'end': 28972.112, 'text': 'okay, e to the power 2 is 7.38.', 'start': 28970.432, 'duration': 1.68}, {'end': 28975.294, 'text': 'e to the power 4 is 54.5.', 'start': 28972.112, 'duration': 3.182}, {'end': 28977.375, 'text': 'e to the power 6 is 403.42.', 'start': 28975.294, 'duration': 2.081}], 'summary': 'Using numpy to calculate exponent values of an array.', 'duration': 28.742, 'max_score': 28948.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k28948633.jpg'}, {'end': 29056.35, 'src': 'embed', 'start': 29023.718, 'weight': 2, 'content': [{'end': 29027.281, 'text': 'So, we have log as np.log.', 'start': 29023.718, 'duration': 3.563}, {'end': 29028.802, 'text': 'Okay Execute it.', 'start': 29027.701, 'duration': 1.101}, {'end': 29030.963, 'text': 'So, we got all the values of here.', 'start': 29029.522, 'duration': 1.441}, {'end': 29031.903, 'text': 'Exponent is this.', 'start': 29031.003, 'duration': 0.9}, {'end': 29032.724, 'text': 'Square root is this.', 'start': 29031.963, 'duration': 0.761}, {'end': 29033.745, 'text': 'Sine of 246 is this.', 'start': 29032.744, 'duration': 1.001}, {'end': 29034.685, 'text': 'Cos of 246.', 'start': 29033.765, 'duration': 0.92}, {'end': 29035.286, 'text': 'Log of 246.', 'start': 29034.685, 'duration': 0.601}, {'end': 29035.766, 'text': 'Okay Okay.', 'start': 29035.286, 'duration': 0.48}, {'end': 29045.545, 'text': 'these are some of the mathematical function that you can perform using numpy.', 'start': 29041.403, 'duration': 4.142}, {'end': 29046.706, 'text': "so let's move ahead.", 'start': 29045.545, 'duration': 1.161}, {'end': 29048.867, 'text': "let's go back to our ppt.", 'start': 29046.706, 'duration': 2.161}, {'end': 29056.35, 'text': 'so we already discussed this part and we also learned about all these different types of other function that you can perform,', 'start': 29048.867, 'duration': 7.483}], 'summary': 'Using numpy, various mathematical functions were executed, including log, exponent, square root, sine, and cosine.', 'duration': 32.632, 'max_score': 29023.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k29023718.jpg'}, {'end': 29280.64, 'src': 'embed', 'start': 29251.153, 'weight': 3, 'content': [{'end': 29253.073, 'text': 'Okay, let me show you with an example.', 'start': 29251.153, 'duration': 1.92}, {'end': 29259.515, 'text': 'Okay, function.', 'start': 29256.674, 'duration': 2.841}, {'end': 29264.256, 'text': "So here let's again copy paste the value of a B and C.", 'start': 29259.515, 'duration': 4.741}, {'end': 29267.577, 'text': 'So, in aggregate function, model things we can do.', 'start': 29264.256, 'duration': 3.321}, {'end': 29273.078, 'text': 'We can perform an array wise sum, or we can find the minimum value of an array or the mean of an array,', 'start': 29267.577, 'duration': 5.501}, {'end': 29278.219, 'text': 'The median or correlation coefficient of an array, or even the standard deviation of the array.', 'start': 29273.078, 'duration': 5.141}, {'end': 29280.64, 'text': "Alright, let's print all of them one by one.", 'start': 29278.219, 'duration': 2.421}], 'summary': 'Demonstrating aggregate functions: sum, min, mean, median, correlation, and standard deviation.', 'duration': 29.487, 'max_score': 29251.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k29251153.jpg'}, {'end': 29642.211, 'src': 'embed', 'start': 29587.706, 'weight': 4, 'content': [{'end': 29590.868, 'text': 'So here we got the output as first array is this, second array is this.', 'start': 29587.706, 'duration': 3.162}, {'end': 29593.729, 'text': 'And first array plus second array, you can see that.', 'start': 29591.308, 'duration': 2.421}, {'end': 29597.982, 'text': '0, 1, 2 is added to all these numbers.', 'start': 29594.9, 'duration': 3.082}, {'end': 29601.624, 'text': 'Fine? So this was the concept of broadcasting.', 'start': 29598.402, 'duration': 3.222}, {'end': 29603.645, 'text': 'Array manipulation in Python.', 'start': 29602.044, 'duration': 1.601}, {'end': 29613.029, 'text': 'So, as a part of array manipulation, so we have concatenating two arrays together stack array row-wise vertically,', 'start': 29605.345, 'duration': 7.684}, {'end': 29617.471, 'text': 'stack array column-wise horizontally and combining column-wise stacked array.', 'start': 29613.029, 'duration': 4.442}, {'end': 29621.093, 'text': "Let's take an example in Jupyter Notebook and learn them one by one.", 'start': 29617.631, 'duration': 3.462}, {'end': 29627.086, 'text': 'So here we want to start about array manipulation.', 'start': 29621.533, 'duration': 5.553}, {'end': 29630.927, 'text': "python. okay, so first of all let's define two arrays.", 'start': 29627.086, 'duration': 3.841}, {'end': 29635.529, 'text': "let's take a equal np dot array inside this.", 'start': 29630.927, 'duration': 4.602}, {'end': 29642.211, 'text': "let's say one, two, three, okay, so this is my array a and b equal np dot array.", 'start': 29635.529, 'duration': 6.682}], 'summary': 'Concept of broadcasting and array manipulation in python demonstrated with examples in jupyter notebook.', 'duration': 54.505, 'max_score': 29587.706, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k29587706.jpg'}, {'end': 29970.971, 'src': 'embed', 'start': 29939.637, 'weight': 6, 'content': [{'end': 29944.223, 'text': 'Okay So guys, this was all about splitting arrays and array manipulation in Python.', 'start': 29939.637, 'duration': 4.586}, {'end': 29946.085, 'text': 'Indexing and slicing in Python.', 'start': 29944.343, 'duration': 1.742}, {'end': 29949.47, 'text': "And we'll see how to select certain elements from the array.", 'start': 29946.286, 'duration': 3.184}, {'end': 29952.454, 'text': "So let's start with what exactly is indexing and slicing.", 'start': 29949.63, 'duration': 2.824}, {'end': 29955.733, 'text': 'Well, indexing just refers to a position.', 'start': 29953.51, 'duration': 2.223}, {'end': 29964.883, 'text': 'For example, if you are storing some numbers or a string in an array, so each cell of an array has some index number associated to it.', 'start': 29955.913, 'duration': 8.97}, {'end': 29970.971, 'text': 'For example, the image shown on your screen represents an array in which each cell has a different character.', 'start': 29965.084, 'duration': 5.887}], 'summary': 'Introduction to array manipulation and indexing in python.', 'duration': 31.334, 'max_score': 29939.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k29939637.jpg'}, {'end': 30538.327, 'src': 'embed', 'start': 30503.113, 'weight': 7, 'content': [{'end': 30514.88, 'text': "let's modify our code a bit here size of our list and here size of an array.", 'start': 30503.113, 'duration': 11.767}, {'end': 30522.159, 'text': 'So we got the output as 28, 000, as the size of the list and size of the array is 4000..', 'start': 30516.696, 'duration': 5.463}, {'end': 30530.563, 'text': 'Okay, so from here I can say that storing a NumPy array occupies less memory when compared to a list.', 'start': 30522.159, 'duration': 8.404}, {'end': 30538.327, 'text': "Okay, now that we have compared their memory size, now let's compare NumPy and list on the basis of their speed.", 'start': 30530.863, 'duration': 7.464}], 'summary': 'Numpy array occupies less memory than list. speed comparison next.', 'duration': 35.214, 'max_score': 30503.113, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k30503113.jpg'}, {'end': 30797.337, 'src': 'embed', 'start': 30761.262, 'weight': 8, 'content': [{'end': 30766.224, 'text': 'this many times faster than a list.', 'start': 30761.262, 'duration': 4.962}, {'end': 30771.227, 'text': "okay, fine, it's executed the name using underscore list.", 'start': 30766.224, 'duration': 5.003}, {'end': 30783.793, 'text': 'so for this example, We got the processing time for list is 0.00199, while for NumPy we got 0.000099..', 'start': 30771.227, 'duration': 12.566}, {'end': 30790.295, 'text': 'So in this example NumPy is 1.99 times or almost 2x time faster than a list.', 'start': 30783.793, 'duration': 6.502}, {'end': 30793.756, 'text': 'Okay So that is how a NumPy is faster than a list.', 'start': 30790.615, 'duration': 3.141}, {'end': 30797.337, 'text': 'And here I showed you how it is more convenient than a list.', 'start': 30793.976, 'duration': 3.361}], 'summary': 'Numpy is almost 2x faster than a list and more convenient', 'duration': 36.075, 'max_score': 30761.262, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k30761262.jpg'}, {'end': 30864.664, 'src': 'embed', 'start': 30840.007, 'weight': 9, 'content': [{'end': 30845.51, 'text': "But before we actually move ahead, there's few special characteristics of SciPy that you should be familiar with.", 'start': 30840.007, 'duration': 5.503}, {'end': 30850.373, 'text': "So let's move ahead and discuss about some of the characteristics of SciPy.", 'start': 30846.25, 'duration': 4.123}, {'end': 30855.756, 'text': 'So first of all, it contains toolboxes dedicated to common issues in scientific computing.', 'start': 30851.193, 'duration': 4.563}, {'end': 30860.919, 'text': 'Secondly, SciPy offered different submodules for different applications.', 'start': 30857.056, 'duration': 3.863}, {'end': 30864.664, 'text': 'Third is, it operates very efficiently on NumPy.', 'start': 30861.701, 'duration': 2.963}], 'summary': 'Scipy has toolboxes for scientific computing, submodules for applications, and operates efficiently on numpy.', 'duration': 24.657, 'max_score': 30840.007, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k30840007.jpg'}], 'start': 28840.256, 'title': 'Numpy and scipy functions and advantages', 'summary': 'Covers numpy mathematical operations, array comparison, aggregate functions, array manipulation, indexing, slicing, and the advantages of numpy and scipy, showcasing examples and results, demonstrating array-wise comparisons and aggregate functions, broadcasting, and memory efficiency of numpy arrays.', 'chapters': [{'end': 29048.867, 'start': 28840.256, 'title': 'Numpy mathematical functions', 'summary': 'Covers performing mathematical operations like addition, subtraction, multiplication, division, exponentiation, square root, sine, cosine, and logarithm using numpy, showcasing examples and results.', 'duration': 208.611, 'highlights': ['Numpy provides functions for mathematical operations like addition, subtraction, multiplication, and division, exemplified by dividing two arrays, resulting in 2, 8, 6.', 'The chapter demonstrates using numpy for exponentiation, showing examples and results of e to the power of 2, 4, and 6 as 7.38, 54.5, and 403.42 respectively.', 'The chapter illustrates using numpy for finding the square root, sine value, cosine value, and logarithm of an array, showcasing examples and results for each function.']}, {'end': 29399.956, 'start': 29048.867, 'title': 'Numpy array comparison and aggregate function', 'summary': 'Covers array comparison in numpy, including element-wise and array-wise comparisons, with examples resulting in false and true outputs. it also demonstrates aggregate functions such as sum, minimum, mean, median, correlation coefficient, and standard deviation on arrays.', 'duration': 351.089, 'highlights': ['Performing array-wise comparison using NumPy The chapter covers array-wise comparison in NumPy, demonstrating that if all the elements of array A are equal to all the elements of array B, the output will be true; otherwise, it will be false.', 'Aggregate functions in NumPy The chapter demonstrates various aggregate functions in NumPy, including array-wise sum, finding the minimum value, mean, median, correlation coefficient, and standard deviation of an array, with specific examples and their corresponding calculated values.', 'Element-wise comparison using NumPy The chapter explains element-wise comparison in NumPy, showcasing comparisons between arrays A and B, and A and C, yielding false and true values, respectively, based on the comparison results.']}, {'end': 29939.417, 'start': 29400.256, 'title': 'Array manipulation in python', 'summary': 'Covers the concept of broadcasting in python, which involves adding the elements of two arrays using the concept of broadcasting, and then delves into array manipulation techniques such as concatenation, stacking row-wise and column-wise, and splitting arrays horizontally and vertically.', 'duration': 539.161, 'highlights': ['The concept of broadcasting in Python involves adding the elements of two arrays using the concept of broadcasting. It demonstrates how the concept of broadcasting adds the elements of two arrays and shows the result of the addition.', 'Array manipulation techniques such as concatenation, stacking row-wise and column-wise, and splitting arrays horizontally and vertically are explained. It explains the techniques of concatenation, stacking row-wise and column-wise, and splitting arrays horizontally and vertically, illustrating each technique with examples.']}, {'end': 30386.599, 'start': 29939.637, 'title': 'Indexing and slicing in python', 'summary': 'Covers indexing and slicing in python, including examples of extracting specific elements and slices from arrays using index numbers and colon notation, with a brief overview of a three cross three dimensional array. key points include indexing and slicing concepts, array manipulation, and selecting specific elements and slices from arrays.', 'duration': 446.962, 'highlights': ['The chapter explains the concepts of indexing and slicing in Python, demonstrating how to extract specific elements and slices from arrays using index numbers and colon notation, with a focus on array manipulation. It also provides examples of using negative index numbers for extracting elements from the end of the array, and highlights the importance of understanding index number ranges for precise extraction. (e.g. M is at index 0, n is at index 11, negative index starts from -1 and goes till -12, 0:5 selects elements from index 0 to 4)', "The chapter showcases the extraction of elements and slices from arrays, including examples of extracting 'Monty' and 'ntypty' from the array, demonstrating the use of index ranges and colon notation for precise extraction. (e.g. 'Monty' is extracted using 0:5, 'ntypty' is extracted using 2:9)", "The chapter provides detailed examples of extracting specific elements and slices from a three cross three dimensional array, demonstrating the use of index numbers and colon notation for precise extraction. It also covers the extraction of specific rows and columns using colon notation. (e.g. extracting the first row using A of 0, extracting 'two three' using 1:3, extracting '2, 3, 5, 6' using 0:2 and 1:3)"]}, {'end': 30982.618, 'start': 30386.819, 'title': 'Advantages of numpy and scipy', 'summary': 'Highlights the benefits of using numpy arrays over python lists, demonstrating that numpy arrays consume less memory and are faster, as well as the characteristics and submodules of scipy for scientific and technical computing.', 'duration': 595.799, 'highlights': ['NumPy consumes less memory than a list Storing a NumPy array occupies less memory compared to a list as demonstrated by the size of the list being 28,000 and the size of the array being 4000.', 'NumPy is faster than a list NumPy is shown to be almost 2x faster than a list, with a processing time for the list being 0.00199 and for NumPy being 0.000099.', 'Characteristics of SciPy SciPy is an open source Python library used for scientific and technical computing, containing modules for optimization, linear algebra, integration, interpolation, special functions, Fourier transformation, and more, while offering toolboxes dedicated to common scientific computing issues, different submodules for various applications, and efficient operation on NumPy.']}], 'duration': 2142.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k28840256.jpg', 'highlights': ['NumPy provides functions for mathematical operations like addition, subtraction, multiplication, and division, exemplified by dividing two arrays, resulting in 2, 8, 6.', 'The chapter demonstrates using numpy for exponentiation, showing examples and results of e to the power of 2, 4, and 6 as 7.38, 54.5, and 403.42 respectively.', 'The chapter illustrates using numpy for finding the square root, sine value, cosine value, and logarithm of an array, showcasing examples and results for each function.', 'The chapter demonstrates various aggregate functions in NumPy, including array-wise sum, finding the minimum value, mean, median, correlation coefficient, and standard deviation of an array, with specific examples and their corresponding calculated values.', 'The concept of broadcasting in Python involves adding the elements of two arrays using the concept of broadcasting. It demonstrates how the concept of broadcasting adds the elements of two arrays and shows the result of the addition.', 'Array manipulation techniques such as concatenation, stacking row-wise and column-wise, and splitting arrays horizontally and vertically are explained. It explains the techniques of concatenation, stacking row-wise and column-wise, and splitting arrays horizontally and vertically, illustrating each technique with examples.', 'The chapter explains the concepts of indexing and slicing in Python, demonstrating how to extract specific elements and slices from arrays using index numbers and colon notation, with a focus on array manipulation.', 'NumPy consumes less memory than a list Storing a NumPy array occupies less memory compared to a list as demonstrated by the size of the list being 28,000 and the size of the array being 4000.', 'NumPy is faster than a list NumPy is shown to be almost 2x faster than a list, with a processing time for the list being 0.00199 and for NumPy being 0.000099.', 'Characteristics of SciPy SciPy is an open source Python library used for scientific and technical computing, containing modules for optimization, linear algebra, integration, interpolation, special functions, Fourier transformation, and more, while offering toolboxes dedicated to common scientific computing issues, different submodules for various applications, and efficient operation on NumPy.']}, {'end': 32565.99, 'segs': [{'end': 31122.779, 'src': 'embed', 'start': 31091.704, 'weight': 0, 'content': [{'end': 31095.946, 'text': 'so first of all, uh, we randomly pick two different cluster centers.', 'start': 31091.704, 'duration': 4.242}, {'end': 31098.528, 'text': "okay, then, as a step two, what we'll do?", 'start': 31095.946, 'duration': 2.582}, {'end': 31103.251, 'text': "we'll assign our data set into two cluster, based on minimum distance, to the cluster centers.", 'start': 31098.528, 'duration': 4.723}, {'end': 31108.513, 'text': "fine, then we'll calculate the mean distance of each member in the cluster.", 'start': 31103.891, 'duration': 4.622}, {'end': 31117.277, 'text': "then, as a step forward, we'll do we'll shift the cluster center towards the mean and we'll consider that two mean values are the new cluster centers.", 'start': 31108.513, 'duration': 8.764}, {'end': 31122.779, 'text': "okay, then again we'll repeat step two and step three until no member changes the group.", 'start': 31117.277, 'duration': 5.502}], 'summary': 'A clustering algorithm assigns data to clusters based on distance and iteratively updates cluster centers until convergence.', 'duration': 31.075, 'max_score': 31091.704, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k31091704.jpg'}, {'end': 31382.272, 'src': 'embed', 'start': 31355.049, 'weight': 1, 'content': [{'end': 31360.153, 'text': 'So as you can see from the array of cluster index, our data set is divided into three different clusters.', 'start': 31355.049, 'duration': 5.104}, {'end': 31366.738, 'text': "It was simple, wasn't it? But there's a common preprocessing step for machine learning algorithm called the data whitening.", 'start': 31360.653, 'duration': 6.085}, {'end': 31374.263, 'text': 'We need to perform data whitening when we use features that represents different characteristics and are on very different scales.', 'start': 31367.178, 'duration': 7.085}, {'end': 31377.826, 'text': 'So which we are gonna see in our data set, okay.', 'start': 31374.784, 'duration': 3.042}, {'end': 31382.272, 'text': "So we'll now see if there is any change in the cluster after we perform whitening.", 'start': 31378.191, 'duration': 4.081}], 'summary': 'Data set divided into three clusters, data whitening for different scale features, checking cluster change post-whitening.', 'duration': 27.223, 'max_score': 31355.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k31355049.jpg'}, {'end': 31633.6, 'src': 'embed', 'start': 31589.172, 'weight': 3, 'content': [{'end': 31598.501, 'text': 'That is the value of Z score as 0.9 means about 90% of drinks have a higher sugar content than this drink.', 'start': 31589.172, 'duration': 9.329}, {'end': 31603.767, 'text': 'So this was about how you can use scipy.stats to calculate Z score for your data.', 'start': 31598.862, 'duration': 4.905}, {'end': 31606.009, 'text': "Now let's take another example over here.", 'start': 31604.147, 'duration': 1.862}, {'end': 31612.764, 'text': 'So this data set consists of frequency of people going to gym and frequency of smokers.', 'start': 31606.482, 'duration': 6.282}, {'end': 31620.187, 'text': 'So in this example, we are trying to find out whether frequency of going to gym is related to frequency of smoking for our data or not.', 'start': 31613.124, 'duration': 7.063}, {'end': 31627.054, 'text': 'Okay, so how will we do that? So for doing this we have another function offered by this sub package that is chi-square.', 'start': 31620.748, 'duration': 6.306}, {'end': 31633.6, 'text': 'So this chi-square test is used to determine whether the output variable is dependent or independent of the input variable.', 'start': 31627.174, 'duration': 6.426}], 'summary': 'Z score of 0.9 indicates 90% of drinks have higher sugar content. using scipy.stats to calculate z score and chi-square test for dependency.', 'duration': 44.428, 'max_score': 31589.172, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k31589172.jpg'}, {'end': 31751.133, 'src': 'embed', 'start': 31723.286, 'weight': 5, 'content': [{'end': 31726.627, 'text': 'You can process your one-dimensional or periodic signals in that.', 'start': 31723.286, 'duration': 3.341}, {'end': 31731.749, 'text': "Let's say you need to resample a signal with n data points to x data point.", 'start': 31727.407, 'duration': 4.342}, {'end': 31734.51, 'text': "So in that case, you'll be using scipy.signal.", 'start': 31732.029, 'duration': 2.481}, {'end': 31742.734, 'text': 'So what scipy.signal is basically doing, it is resampling the data point using FFT, that is Fast Fourier Transformation.', 'start': 31735.21, 'duration': 7.524}, {'end': 31748.876, 'text': "Okay, so now that you know why do we use scipy.signal, let's move ahead and see how do we use it.", 'start': 31743.534, 'duration': 5.342}, {'end': 31751.133, 'text': 'So here we are performing the resampling.', 'start': 31749.332, 'duration': 1.801}], 'summary': 'Resample signals using scipy.signal with fft', 'duration': 27.847, 'max_score': 31723.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k31723286.jpg'}, {'end': 31894.062, 'src': 'embed', 'start': 31865.667, 'weight': 6, 'content': [{'end': 31868.608, 'text': 'either it can be a scalar or multi-dimensional.', 'start': 31865.667, 'duration': 2.941}, {'end': 31871.809, 'text': 'you can also use it for curve fitting or root finding.', 'start': 31868.608, 'duration': 3.201}, {'end': 31875.731, 'text': 'so let us discuss how can we do that with the help of an example.', 'start': 31871.809, 'duration': 3.922}, {'end': 31879.093, 'text': "so here i'm importing matplotlib library and numpy library.", 'start': 31875.731, 'duration': 3.362}, {'end': 31885.596, 'text': "i'm using numpy library to create my data set and matplotlib to plot this particular graph.", 'start': 31879.093, 'duration': 6.503}, {'end': 31894.062, 'text': 'okay, So here I have generated one function, f of x, where x varies from 0 to 1, evenly with a step size of 0.1..', 'start': 31885.596, 'duration': 8.466}], 'summary': 'Using numpy and matplotlib for data visualization and analysis.', 'duration': 28.395, 'max_score': 31865.667, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k31865667.jpg'}, {'end': 31962.52, 'src': 'embed', 'start': 31933.947, 'weight': 7, 'content': [{'end': 31939.089, 'text': 'Then with the help of result dot x we can easily find the value of x at which the function is minimum.', 'start': 31933.947, 'duration': 5.142}, {'end': 31941.97, 'text': 'So as you can see we got the output as 0.69.', 'start': 31939.529, 'duration': 2.441}, {'end': 31948.968, 'text': 'So this means that At x equals 0.699, we get a minimum out of our function.', 'start': 31941.97, 'duration': 6.998}, {'end': 31953.532, 'text': "scipy.integrate Well, there's another very important sub package in scipy.", 'start': 31948.988, 'duration': 4.544}, {'end': 31962.52, 'text': "So in this session, we'll learn about why do we use scipy.integrate and how do we use it.", 'start': 31956.434, 'duration': 6.086}], 'summary': 'Using scipy.integrate, we find a minimum function value at x=0.69.', 'duration': 28.573, 'max_score': 31933.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k31933947.jpg'}, {'end': 32262.233, 'src': 'embed', 'start': 32234.534, 'weight': 9, 'content': [{'end': 32237.997, 'text': 'So, in order to work with our fast Fourier transformation sub package,', 'start': 32234.534, 'duration': 3.463}, {'end': 32242.501, 'text': 'the very first thing that we are going to do is generate one digital signal which has some noise in it.', 'start': 32237.997, 'duration': 4.504}, {'end': 32246.464, 'text': 'Okay, so we can easily do that using NumPy library.', 'start': 32243.122, 'duration': 3.342}, {'end': 32249.087, 'text': "Okay, so let's see what we have done here.", 'start': 32247.045, 'duration': 2.042}, {'end': 32253.371, 'text': "So here the very first thing that I'm doing up is importing my NumPy library.", 'start': 32249.367, 'duration': 4.004}, {'end': 32259.536, 'text': "I'm defining a time step as 0.02, as I need to have equal number of spaces over here right.", 'start': 32253.371, 'duration': 6.165}, {'end': 32262.233, 'text': 'You can directly put this value over here.', 'start': 32260.031, 'duration': 2.202}], 'summary': 'Creating a digital signal using numpy with a time step of 0.02.', 'duration': 27.699, 'max_score': 32234.534, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k32234534.jpg'}], 'start': 30983.038, 'title': 'Using scipy for data clustering, statistical analysis, resampling, and optimization', 'summary': 'Discusses the usage of scipy libraries including scipy.cluster for data clustering, scipy.stats for statistical analysis, scipy.signal for resampling, and optimization functions. it covers concepts such as clustering, z-scores, chi-square tests, resampling signals, function minimization, curve fitting, numerical integration, and fast fourier transformations, with specific examples and impacts.', 'chapters': [{'end': 31416.476, 'start': 30983.038, 'title': 'Using scipy cluster for data clustering', 'summary': 'Discusses the use of scipy.cluster for data clustering, explaining the concept of clustering, the process of finding clusters, and a demonstration of using k-means clustering on a dataset, with an example and the impact of data whitening.', 'duration': 433.438, 'highlights': ['The process of clustering is explained as the grouping of similar items into single groups, with data points within a cluster being similar to each other but completely different from data points in different clusters.', 'The process of finding clusters is outlined, involving randomly picking cluster centers, assigning data points to clusters based on minimum distance, calculating mean distances, and iteratively shifting cluster centers until no member changes group.', 'A demonstration of using k-means clustering on a dataset is provided, showing the division of the dataset into three different clusters and the impact of data whitening on the clustering results.', 'The need for data whitening in machine learning algorithms, especially when using features representing different characteristics and on different scales, is highlighted.']}, {'end': 31696.257, 'start': 31416.917, 'title': 'Using scipy.stats for statistical analysis', 'summary': 'Discusses the usage of scipy.stats for statistical analysis, including calculating z-scores for sugar content in drinks and performing a chi-square test to determine the relationship between frequency of going to the gym and frequency of smoking.', 'duration': 279.34, 'highlights': ['The chapter explains how to calculate the z-score for sugar content in drinks using scipy.stats, with a specific example showing that a drink with a z-score of 0.9 means about 90% of drinks have a higher sugar content than this drink. The z-score for a 14-gram sugar coffee is calculated as -0.93, indicating that about 90% of drinks have a higher sugar content than this drink.', 'The chapter demonstrates the use of the chi-square test from scipy.stats to determine the relationship between the frequency of going to the gym and the frequency of smoking, with the resulting p-value being 0.482, indicating that the variables are not related. A chi-square test is performed to determine the relationship between the frequency of going to the gym and the frequency of smoking, resulting in a p-value of 0.482, indicating that the variables are not related.', 'The chapter provides an overview of the functions and capabilities offered by scipy.stats, including the availability of various statistical functions and probability distributions within the package. scipy.stats is highlighted for its collection of statistical functions and probability distributions, demonstrating its capabilities for statistical analysis.']}, {'end': 31885.596, 'start': 31696.257, 'title': 'Using scipy.signal for resampling data', 'summary': 'Covers the usage of scipy.signal for resampling one-dimensional signals, with an example demonstrating the resampling of a sine graph from 200 data points to 100 data points using fft, and also delves into the usage of scipy.optimize for function minimization and curve fitting.', 'duration': 189.339, 'highlights': ['The chapter covers the usage of scipy.signal for resampling one-dimensional signals, with an example demonstrating the resampling of a sine graph from 200 data points to 100 data points using FFT. usage of scipy.signal for resampling one-dimensional signals, example of resampling a sine graph from 200 data points to 100 data points, usage of FFT for resampling', 'The usage of scipy.optimize for function minimization and curve fitting is also discussed, providing algorithms for scalar or multi-dimensional function minimization, curve fitting, and root finding. usage of scipy.optimize for function minimization, algorithms for scalar or multi-dimensional function minimization, curve fitting, and root finding']}, {'end': 32565.99, 'start': 31885.596, 'title': 'Optimization functions and fast fourier transformations', 'summary': 'Covers the use of optimization functions in scipy to find the minimum value of a function, with an example showing the function output as 0.69. it also explains the use of scipy.integrate for numerical integration, demonstrating the calculation of the integration of x squared from 0 to 1 as 0.33. additionally, it delves into the application of scipy.fftpack for fast fourier transformations, showcasing the generation of a digital signal with noise and the process of applying fft and ifft to obtain a noiseless signal.', 'duration': 680.394, 'highlights': ['The chapter covers the use of optimization functions in scipy to find the minimum value of a function. It explains the use of scipy.optimize.minimize to directly find the minimum value of a function, with the example output as 0.69.', 'It also explains the use of scipy.integrate for numerical integration, demonstrating the calculation of the integration of x squared from 0 to 1 as 0.33. It details the application of scipy.integrate.quad for single integration and the mathematical calculation of the integration of x squared from 0 to 1 as 0.33.', 'Additionally, it delves into the application of scipy.fftpack for fast Fourier transformations, showcasing the generation of a digital signal with noise and the process of applying FFT and IFFT to obtain a noiseless signal. It highlights the generation of a digital signal with noise, the application of scipy.fftpack.fft for fast Fourier transformation, and the process of applying IFFT to obtain a noiseless signal.']}], 'duration': 1582.952, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k30983038.jpg', 'highlights': ['The process of finding clusters involves randomly picking cluster centers, assigning data points to clusters based on minimum distance, calculating mean distances, and iteratively shifting cluster centers until no member changes group.', 'A demonstration of using k-means clustering on a dataset is provided, showing the division of the dataset into three different clusters and the impact of data whitening on the clustering results.', 'The need for data whitening in machine learning algorithms, especially when using features representing different characteristics and on different scales, is highlighted.', 'The chapter explains how to calculate the z-score for sugar content in drinks using scipy.stats, with a specific example showing that a drink with a z-score of 0.9 means about 90% of drinks have a higher sugar content than this drink.', 'The chapter demonstrates the use of the chi-square test from scipy.stats to determine the relationship between the frequency of going to the gym and the frequency of smoking, with the resulting p-value being 0.482, indicating that the variables are not related.', 'The chapter covers the usage of scipy.signal for resampling one-dimensional signals, with an example demonstrating the resampling of a sine graph from 200 data points to 100 data points using FFT.', 'The usage of scipy.optimize for function minimization and curve fitting is also discussed, providing algorithms for scalar or multi-dimensional function minimization, curve fitting, and root finding.', 'The chapter covers the use of optimization functions in scipy to find the minimum value of a function, explaining the use of scipy.optimize.minimize to directly find the minimum value of a function, with the example output as 0.69.', 'It also explains the use of scipy.integrate for numerical integration, demonstrating the calculation of the integration of x squared from 0 to 1 as 0.33.', 'Additionally, it delves into the application of scipy.fftpack for fast Fourier transformations, showcasing the generation of a digital signal with noise and the process of applying FFT and IFFT to obtain a noiseless signal.']}, {'end': 33973.827, 'segs': [{'end': 32620.087, 'src': 'embed', 'start': 32589.946, 'weight': 0, 'content': [{'end': 32594.729, 'text': "So as the name suggests, ratings.csv contains all users' ratings of the books.", 'start': 32589.946, 'duration': 4.783}, {'end': 32597.611, 'text': 'So there are a total of 980, 000 ratings for 10, 000 books from 53, 424 users.', 'start': 32595.33, 'duration': 2.281}, {'end': 32610.883, 'text': "So the books.csv contains more information on the books such as the author's name, publication year, book id and so on.", 'start': 32603.84, 'duration': 7.043}, {'end': 32613.484, 'text': 'Then we have the booktags.csv file.', 'start': 32611.603, 'duration': 1.881}, {'end': 32620.087, 'text': 'So this file comprises of all tag ids users have assigned to the books and the corresponding tag counts.', 'start': 32614.085, 'duration': 6.002}], 'summary': 'Ratings.csv has 980,000 ratings for 10,000 books from 53,424 users, while books.csv contains additional book details, and booktags.csv includes tag ids and counts.', 'duration': 30.141, 'max_score': 32589.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k32589946.jpg'}, {'end': 33126.695, 'src': 'embed', 'start': 33095.949, 'weight': 2, 'content': [{'end': 33096.991, 'text': 'So this is the command for that.', 'start': 33095.949, 'duration': 1.042}, {'end': 33107.078, 'text': 'I have again given ratings over here and I am filtering out only those records where the ratings given by each user is greater than 2.', 'start': 33097.391, 'duration': 9.687}, {'end': 33115.343, 'text': 'That is each user has at least rated 3 books or more and I am storing the result back to ratings.', 'start': 33107.078, 'duration': 8.265}, {'end': 33121.667, 'text': 'So view of ratings.', 'start': 33118.706, 'duration': 2.961}, {'end': 33124.372, 'text': 'So this is our final data set.', 'start': 33122.389, 'duration': 1.983}, {'end': 33126.695, 'text': 'So we see that there are 960, 595 entries in total.', 'start': 33124.972, 'duration': 1.723}], 'summary': 'Filtered records where each user rated at least 3 books, resulting in 960,595 total entries.', 'duration': 30.746, 'max_score': 33095.949, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33095949.jpg'}, {'end': 33326.759, 'src': 'embed', 'start': 33289.139, 'weight': 3, 'content': [{'end': 33294.241, 'text': 'So we see that there are 45, 016 unique user IDs in total.', 'start': 33289.139, 'duration': 5.102}, {'end': 33299.061, 'text': 'So we need 2% of these unique user IDs.', 'start': 33294.86, 'duration': 4.201}, {'end': 33309.328, 'text': 'So 2% of this would be 0.02 into 45016.', 'start': 33299.521, 'duration': 9.807}, {'end': 33311.67, 'text': 'So this would give us 900 users.', 'start': 33309.328, 'duration': 2.342}, {'end': 33317.953, 'text': 'So from 45, 016 users in total, we would need 900 users.', 'start': 33312.25, 'duration': 5.703}, {'end': 33324.517, 'text': "Right So we'll do a random sampling of 900 users from the entire user base.", 'start': 33318.553, 'duration': 5.964}, {'end': 33326.759, 'text': 'so this is the command for that.', 'start': 33325.038, 'duration': 1.721}], 'summary': 'There are 45,016 unique user ids, and 2% of them would be 900 users for random sampling.', 'duration': 37.62, 'max_score': 33289.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33289139.jpg'}, {'end': 33443.538, 'src': 'embed', 'start': 33398.826, 'weight': 4, 'content': [{'end': 33402.228, 'text': 'So now we see that the number of ratings has reduced to 18, 832.', 'start': 33398.826, 'duration': 3.402}, {'end': 33407.129, 'text': 'So initially we had more than 9 lakh ratings.', 'start': 33402.228, 'duration': 4.901}, {'end': 33412.332, 'text': 'So now after filtering the data set, we have just 18, 832 ratings.', 'start': 33407.61, 'duration': 4.722}, {'end': 33414.232, 'text': 'All right.', 'start': 33412.352, 'duration': 1.88}, {'end': 33417.572, 'text': 'So our second task was to make a distribution of these ratings.', 'start': 33414.792, 'duration': 2.78}, {'end': 33419.134, 'text': 'So let me go ahead and do that.', 'start': 33418.034, 'duration': 1.1}, {'end': 33422.379, 'text': 'So guys, this is the command for that.', 'start': 33420.939, 'duration': 1.44}, {'end': 33428.226, 'text': 'So again, I am using the ratings dataset and on top of this, I am building the ggplot.', 'start': 33422.981, 'duration': 5.245}, {'end': 33433.01, 'text': 'So here I am mapping the rating column onto the X axis.', 'start': 33428.886, 'duration': 4.124}, {'end': 33436.913, 'text': 'So this column over here, so we have different ratings, one, two, three, four, and five.', 'start': 33433.39, 'duration': 3.523}, {'end': 33439.956, 'text': 'So I am mapping this column onto the X axis.', 'start': 33437.334, 'duration': 2.622}, {'end': 33443.538, 'text': 'So the fill color would also be determined by the rating column.', 'start': 33440.576, 'duration': 2.962}], 'summary': 'Filtered dataset has 18,832 ratings, with distribution plotted using ggplot.', 'duration': 44.712, 'max_score': 33398.826, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33398826.jpg'}, {'end': 33637.411, 'src': 'embed', 'start': 33610.055, 'weight': 6, 'content': [{'end': 33616.399, 'text': 'So this is for those instances where a single book was rated by two users or in other words a single book was rated two times.', 'start': 33610.055, 'duration': 6.344}, {'end': 33624.585, 'text': 'This is for those instances which tells us that a single book was rated by three users, or in other words, a single book was rated three times,', 'start': 33617, 'duration': 7.585}, {'end': 33626.366, 'text': 'and the count for this is around 1500 times.', 'start': 33624.585, 'duration': 1.781}, {'end': 33630.929, 'text': 'Right, so this plot was for the number of ratings for each book.', 'start': 33628.208, 'duration': 2.721}, {'end': 33637.411, 'text': 'Then we had to get the percentage distribution of the different genres.', 'start': 33633.53, 'duration': 3.881}], 'summary': 'Analyzing book ratings with 1500 instances of books rated multiple times.', 'duration': 27.356, 'max_score': 33610.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33610055.jpg'}, {'end': 33738.898, 'src': 'embed', 'start': 33711.788, 'weight': 7, 'content': [{'end': 33717.995, 'text': 'So there are 27 genres in total and these are Christian, Business, Poetry, Philosophy, Science and so on.', 'start': 33711.788, 'duration': 6.207}, {'end': 33724.364, 'text': 'Now similarly I will extract all of the corresponding tag IDs with respect to the tag names.', 'start': 33718.015, 'duration': 6.349}, {'end': 33729.552, 'text': 'So let me find out which are the available tags.', 'start': 33727.15, 'duration': 2.402}, {'end': 33738.898, 'text': 'So over here, I am basically extracting all of those tag IDs, if the tag name is present in one of the available genres, right.', 'start': 33730.132, 'duration': 8.766}], 'summary': '27 genres, including christian, business, poetry, philosophy, science. extracting corresponding tag ids.', 'duration': 27.11, 'max_score': 33711.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33711788.jpg'}, {'end': 33839.332, 'src': 'embed', 'start': 33809.724, 'weight': 8, 'content': [{'end': 33814.626, 'text': 'Similarly, this is the tag ID 4605 and the count is 1109.', 'start': 33809.724, 'duration': 4.902}, {'end': 33819.768, 'text': 'So this means that there are 1109 books present for this particular genre over here.', 'start': 33814.626, 'duration': 5.142}, {'end': 33821.588, 'text': "And let's take this over here.", 'start': 33820.368, 'duration': 1.22}, {'end': 33826.11, 'text': 'So the tag ID is 7778 and the count is 469.', 'start': 33821.989, 'duration': 4.121}, {'end': 33831.892, 'text': 'So this means that there are 469 books present with respect to this genre.', 'start': 33826.11, 'duration': 5.782}, {'end': 33836.494, 'text': 'Now let me go ahead and also find the percentage.', 'start': 33834.033, 'duration': 2.461}, {'end': 33839.332, 'text': 'So let me select all of this code over here.', 'start': 33837.571, 'duration': 1.761}], 'summary': 'Tag id 4605 has 1109 books, while tag id 7778 has 469 books. generating percentage next.', 'duration': 29.608, 'max_score': 33809.724, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33809724.jpg'}, {'end': 33896.123, 'src': 'embed', 'start': 33866.006, 'weight': 9, 'content': [{'end': 33871.91, 'text': 'So this percentage over here I am dividing n upon sum of n.', 'start': 33866.006, 'duration': 5.904}, {'end': 33876.453, 'text': 'That is this would give me the percentage of each of the genre.', 'start': 33871.91, 'duration': 4.543}, {'end': 33883.657, 'text': 'and after getting the percentage of each of the genre, i will arrange the data set in descending order.', 'start': 33877.213, 'duration': 6.444}, {'end': 33887.359, 'text': 'and after arranging the data set in descending order,', 'start': 33883.657, 'duration': 3.702}, {'end': 33896.123, 'text': 'i will also left join the tags data set to the book tags data set and the joining would be done by the tag id column over here.', 'start': 33887.359, 'duration': 8.764}], 'summary': 'Calculate genre percentages and arrange in descending order.', 'duration': 30.117, 'max_score': 33866.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k33866006.jpg'}], 'start': 32566.33, 'title': 'Book recommendations with data', 'summary': 'Discusses making book recommendations using a dataset with 980,000 ratings for 10,000 books from 53,424 users, data cleaning resulting in 960,595 entries, sampling 2% of unique users, obtaining 18,832 ratings, and analyzing the percentage distribution of 27 genres, with the highest count being 1109 books belonging to a particular genre.', 'chapters': [{'end': 32635.75, 'start': 32566.33, 'title': 'Book recommendations with data', 'summary': 'Discusses the process of making book recommendations using a dataset comprising 980,000 ratings for 10,000 books from 53,424 users, along with details about the files ratings.csv, books.csv, booktags.csv, and tags.csv.', 'duration': 69.42, 'highlights': ['The dataset comprises 980,000 ratings for 10,000 books from 53,424 users The ratings.csv file contains a total of 980,000 ratings for 10,000 books from 53,424 users.', 'Details about the files ratings.csv, books.csv, booktags.csv, and tags.csv are provided The chapter provides details about the files ratings.csv, books.csv, booktags.csv, and tags.csv, which are essential for making book recommendations.']}, {'end': 33176.629, 'start': 32636.271, 'title': 'Data cleaning and data exploration', 'summary': 'Covers the process of data cleaning, including removing duplicate ratings and users who have rated fewer than three books, resulting in 960,595 entries, and outlines the tasks for data exploration in the second phase.', 'duration': 540.358, 'highlights': ['Removing duplicate ratings The process involves identifying and removing instances where a user has rated the same book multiple times, resulting in 4487 entries with duplicate ratings.', 'Filtering out users with fewer than three ratings The step involves filtering out users who have rated less than three books, resulting in a final dataset with 960,595 entries.', 'Data exploration tasks in the second phase Tasks include extracting a sample set of 2% records, creating a bar plot for rating distribution, analyzing the count of book ratings, plotting the percentage distribution of genres, finding the top 10 books with highest ratings, and identifying the 10 most popular books.']}, {'end': 33630.929, 'start': 33228.614, 'title': 'Sampling and analyzing user and rating data', 'summary': 'Details the process of sampling 2% of unique users, resulting in 900 sample users, and filtering the dataset to obtain 18,832 ratings, followed by visualizing the distribution of ratings and the number of ratings per book.', 'duration': 402.315, 'highlights': ['Sampling 2% of unique user IDs to obtain 900 sample users from a total of 45,016 users. The process involves setting a user fraction of 0.02, finding unique user IDs, calculating the number of sample users, and performing random sampling.', 'Filtering the dataset to obtain 18,832 ratings from the initial 960,595 ratings. The filtering process involves selecting only the user IDs present in the sample users object, resulting in a significant reduction in the number of ratings.', 'Visualizing the distribution of ratings using ggplot, showcasing the frequency of different star ratings. The plot reveals the frequency of different star ratings, with a notable number of 4 and 5-star ratings and relatively fewer 1-star ratings.', 'Analyzing the number of ratings per book, depicting the frequency of books receiving various numbers of ratings. The analysis indicates that most books were rated by only one user, with a small number of books receiving multiple ratings.']}, {'end': 33973.827, 'start': 33633.53, 'title': 'Percentage distribution of genres', 'summary': 'Explains the process of extracting and analyzing the percentage distribution of different genres, resulting in 27 total genres, with the highest count being 1109 books belonging to a particular genre, and a detailed process of creating a plot for the percentage of each genre.', 'duration': 340.297, 'highlights': ['Extracting all of the corresponding tag IDs with respect to the tag names and finding 27 total genres. 27 total genres', 'The highest count of books belonging to a particular genre is 1109. 1109 books belonging to a particular genre', 'Creating a plot for the percentage of each genre and arranging the dataset in descending order based on percentage. Arranging data set in descending order and creating a plot for the percentage of each genre']}], 'duration': 1407.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/a5KmkeQ714k/pics/a5KmkeQ714k32566330.jpg', 'highlights': ['The dataset comprises 980,000 ratings for 10,000 books from 53,424 users', 'Details about the files ratings.csv, books.csv, booktags.csv, and tags.csv are provided', 'Filtering out users with fewer than three ratings resulting in a final dataset with 960,595 entries', 'Sampling 2% of unique user IDs to obtain 900 sample users from a total of 45,016 users', 'Filtering the dataset to obtain 18,832 ratings from the initial 960,595 ratings', 'Visualizing the distribution of ratings using ggplot, showcasing the frequency of different star ratings', 'Analyzing the number of ratings per book, depicting the frequency of books receiving various numbers of ratings', 'Extracting all of the corresponding tag IDs with respect to the tag names and finding 27 total genres', 'The highest count of books belonging to a particular genre is 1109', 'Creating a plot for the percentage of each genre and arranging the dataset in descending order based on percentage']}], 'highlights': ['Data science is a highly demanding and lucrative job profile in India and globally, with abundant career opportunities and exceptional remuneration, making it a recommended technology to pursue.', 'The exponential growth of unstructured data is emphasized by the comparison of data generated in the last two years to all data stored and generated before 2019, showcasing the immense volume and pace of data generation.', 'Data science has diverse applications across various industries, including marketing, manufacturing, insurance, banking, and healthcare, providing a wide range of use cases.', 'The last step of a data science engagement is knowledge representation, where the model outputs and learnings are presented to non-technical stakeholders in a simplified, consumable manner.', 'Inferential statistics are used to draw conclusions and make decisions based on data, such as analyzing demographic trends for decision-making.', 'The data dictionary can be used to make decisions in machine learning model building, inventory cost predictions, and customer behavior analysis, providing insights for better decision-making.', "R Studio provides a user-friendly interface for writing codes, executing queries, and visualizing outputs, making the coder's life easier.", 'Variables are essential in programming as they act as temporary storage spaces where values can be changed, providing the ability to store and manipulate information across variables.', 'Supervised learning achieves 97% accuracy in identifying an apple in the example.', 'Fraud detection in banking uses ML to analyze transactions and credit scores for identifying fraudulent activities.', 'NumPy provides functions for mathematical operations like addition, subtraction, multiplication, and division, exemplified by dividing two arrays, resulting in 2, 8, 6.', 'The process of finding clusters involves randomly picking cluster centers, assigning data points to clusters based on minimum distance, calculating mean distances, and iteratively shifting cluster centers until no member changes group.', 'The dataset comprises 980,000 ratings for 10,000 books from 53,424 users', 'Filtering out users with fewer than three ratings resulting in a final dataset with 960,595 entries', 'Visualizing the distribution of ratings using ggplot, showcasing the frequency of different star ratings', 'Creating a plot for the percentage of each genre and arranging the dataset in descending order based on percentage']}