title
Data Science Tutorial | Data Science for Beginners | Data Science with Python Tutorial | Simplilearn
description
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This Data Science Tutorial will help you understand what is Data Science, who is a Data Scientist, what does a Data Scientist do and also how Python is used for Data Science. Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This Data Science tutorial will help you establish your skills at analytical techniques using Python. With this Data Science video, you’ll learn the essential concepts of Data Science with Python programming and also understand how data acquisition, data preparation, data mining, model building & testing, data visualization is done. This Data Science tutorial is ideal for beginners who aspire to become a Data Scientist.
This Data Science tutorial will cover the following topics:
1. What is Data Science? ( 00:43 )
2. Who is a Data Scientist? ( 02:02 )
3. What does a Data Scientist do? ( 02:25 )
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#DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
➡️ About Caltech Post Graduate Program In Data Science
This Post Graduation in Data Science leverages the superiority of Caltech's academic eminence. The Data Science program covers critical Data Science topics like Python programming, R programming, Machine Learning, Deep Learning, and Data Visualization tools through an interactive learning model with live sessions by global practitioners and practical labs.
âś… Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Caltech PG program in Data Science completion certificate
- Earn up to 14 CEUs from Caltech CTME
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âś… Skills Covered
- Exploratory Data Analysis
- Descriptive Statistics
- Inferential Statistics
- Model Building and Fine Tuning
- Supervised and Unsupervised Learning
- Ensemble Learning
- Deep Learning
- Data Visualization
Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=Data-Sciene-Tutorial-jNeUBWrrRsQ&utm_medium=Description&utm_source=youtube
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detail
{'title': 'Data Science Tutorial | Data Science for Beginners | Data Science with Python Tutorial | Simplilearn', 'heatmap': [{'end': 316.304, 'start': 210.45, 'weight': 0.736}, {'end': 396.266, 'start': 341.214, 'weight': 0.853}, {'end': 530.784, 'start': 498.072, 'weight': 0.853}, {'end': 740.731, 'start': 710.336, 'weight': 0.828}, {'end': 1265.48, 'start': 1104.537, 'weight': 0.843}, {'end': 1512.466, 'start': 1444.277, 'weight': 0.802}], 'summary': 'Learn the fundamentals of data science, including data acquisition, preparation, mining, modeling, and maintenance, and gain insights into applications like fraud detection and happiness data analysis using python and tableau, with a focus on machine learning and communication of results.', 'chapters': [{'end': 120.846, 'segs': [{'end': 76.958, 'src': 'embed', 'start': 30.313, 'weight': 0, 'content': [{'end': 35.216, 'text': 'and then towards the end, we will also see an example program as well.', 'start': 30.313, 'duration': 4.903}, {'end': 41.539, 'text': "I'll take you through a quick code where we have done some data science activity and then we will conclude.", 'start': 35.216, 'duration': 6.323}, {'end': 43.24, 'text': "so let's get started.", 'start': 41.539, 'duration': 1.701}, {'end': 45.401, 'text': 'so what is data science?', 'start': 43.24, 'duration': 2.161}, {'end': 55.585, 'text': 'as the name suggests, it is nothing but study of using data and trying to find out some insights, or extracting some insights or knowledge,', 'start': 45.401, 'duration': 10.184}, {'end': 58.306, 'text': 'using all the data that is at your disposal.', 'start': 55.585, 'duration': 2.721}, {'end': 61.607, 'text': "so that's pretty much what data science is all about.", 'start': 58.306, 'duration': 3.301}, {'end': 73.154, 'text': 'so you take the data and apply certain methodologies, certain algorithms and your business domain knowledge as well and, of course,', 'start': 61.607, 'duration': 11.547}, {'end': 76.958, 'text': 'a certain amount of creativity to extract some insights.', 'start': 73.154, 'duration': 3.804}], 'summary': 'Data science involves using data to extract insights and knowledge, employing methodologies, algorithms, and creativity.', 'duration': 46.645, 'max_score': 30.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ30313.jpg'}, {'end': 120.846, 'src': 'embed', 'start': 95.792, 'weight': 2, 'content': [{'end': 101.193, 'text': 'there are a lot of fraudulent activities or transactions, primarily on the internet.', 'start': 95.792, 'duration': 5.401}, {'end': 108.576, 'text': "it's very easy to commit fraud and therefore we can use data science to either prevent or detect fraud.", 'start': 101.193, 'duration': 7.383}, {'end': 115.061, 'text': 'there are certain algorithms machine learning algorithms that can be used, like, for example, some outlier techniques,', 'start': 108.576, 'duration': 6.485}, {'end': 120.846, 'text': 'clustering techniques that can be used to detect fraud and prevent fraud as well.', 'start': 115.061, 'duration': 5.785}], 'summary': 'Fraudulent activities are prevalent on the internet. data science can help prevent or detect fraud using algorithms like outlier and clustering techniques.', 'duration': 25.054, 'max_score': 95.792, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ95792.jpg'}], 'start': 3.407, 'title': 'Introduction to data science', 'summary': 'Introduces the concept of data science, the skills of a data scientist, the methodology used for data science, and the common applications such as fraud detection, with a focus on using data to extract actionable insights and prevent fraud.', 'chapters': [{'end': 120.846, 'start': 3.407, 'title': 'Introduction to data science', 'summary': 'Introduces the concept of data science, the skills of a data scientist, the methodology used for data science, and the common applications such as fraud detection, with a focus on using data to extract actionable insights and prevent fraud.', 'duration': 117.439, 'highlights': ['Data science is the study of using data to extract actionable insights or knowledge, using methodologies, algorithms, and business domain knowledge, and can be used for fraud detection or prevention.', 'Data science involves applying methodologies, algorithms, and creativity to extract actionable insights from available data.', 'Fraud detection is a common application of data science, using machine learning algorithms such as outlier and clustering techniques.', 'The chapter covers the skills of a data scientist, the methodology used for data science, and an example program demonstrating data science activity.']}], 'duration': 117.439, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ3407.jpg', 'highlights': ['Data science involves applying methodologies, algorithms, and creativity to extract actionable insights from available data.', 'Data science is the study of using data to extract actionable insights or knowledge, using methodologies, algorithms, and business domain knowledge, and can be used for fraud detection or prevention.', 'Fraud detection is a common application of data science, using machine learning algorithms such as outlier and clustering techniques.', 'The chapter covers the skills of a data scientist, the methodology used for data science, and an example program demonstrating data science activity.']}, {'end': 705.374, 'segs': [{'end': 316.304, 'src': 'heatmap', 'start': 160.921, 'weight': 2, 'content': [{'end': 164.923, 'text': 'the first step, obviously, is to get the raw data, which is known as data acquisition.', 'start': 160.921, 'duration': 4.002}, {'end': 168.845, 'text': 'it can be all kinds of format and it could be multiple sources,', 'start': 164.923, 'duration': 3.922}, {'end': 176.328, 'text': 'but obviously that raw data cannot be used as it is for performing data mining activities or data modeling activities.', 'start': 168.845, 'duration': 7.483}, {'end': 183.333, 'text': 'so the data has to be planned and prepared for using in the data models or in the data mining activity.', 'start': 176.328, 'duration': 7.005}, {'end': 185.095, 'text': 'so that is the data preparation.', 'start': 183.333, 'duration': 1.762}, {'end': 189.918, 'text': 'then we actually do the data mining, which can also include some exploratory activities.', 'start': 185.095, 'duration': 4.823}, {'end': 198.204, 'text': 'and then, if we have to do stuff like machine learning, then you need to build a machine learning model and test the model, get insights out of it,', 'start': 189.918, 'duration': 8.286}, {'end': 201.667, 'text': 'and then, if the model is fine, you deploy it.', 'start': 198.204, 'duration': 3.463}, {'end': 203.508, 'text': 'and then you need to maintain the model,', 'start': 201.667, 'duration': 1.841}, {'end': 210.41, 'text': 'because over a period of time it is possible that you need to tweak the model because of change in the process or change in the data, and so on.', 'start': 203.508, 'duration': 6.902}, {'end': 212.971, 'text': 'So that all comes under the model maintenance.', 'start': 210.45, 'duration': 2.521}, {'end': 216.431, 'text': "So let's take a deeper look at each of these activities.", 'start': 213.231, 'duration': 3.2}, {'end': 218.732, 'text': "Let's start with data acquisition.", 'start': 216.732, 'duration': 2}, {'end': 227.014, 'text': 'So the stage of data acquisition basically the data scientist will collect raw data from all possible sources.', 'start': 218.892, 'duration': 8.122}, {'end': 231.675, 'text': 'So this could be typically an RDBMS, which is a relational database,', 'start': 227.294, 'duration': 4.381}, {'end': 237.943, 'text': 'or it can also be a non-rdbms or could be flat files or unstructured data and so on.', 'start': 231.675, 'duration': 6.268}, {'end': 241.689, 'text': 'so we need to bring all that data from different sources.', 'start': 237.943, 'duration': 3.746}, {'end': 249.559, 'text': 'if required, we need to do some kind of homogeneous formatting so that it all fits into looks, at least format.', 'start': 241.689, 'duration': 7.87}, {'end': 255.041, 'text': 'from a format perspective it looks homogeneous so that may be requiring some kind of transformation.', 'start': 249.559, 'duration': 5.482}, {'end': 264.925, 'text': 'very often this is loaded into what is known as data warehouse, so this can also be sometimes referred to as ETL, or extract, transform and load.', 'start': 255.041, 'duration': 9.884}, {'end': 278.85, 'text': 'so a data warehouse is like a common place where data from different sources is brought together so that people can perform data science activities like reporting or data mining or statistical analysis and so on.', 'start': 264.925, 'duration': 13.925}, {'end': 283.891, 'text': 'so data from various sources is put in a centralized place which is known as a data warehouse.', 'start': 278.85, 'duration': 5.041}, {'end': 286.271, 'text': 'so that is also known as ETL.', 'start': 283.891, 'duration': 2.38}, {'end': 289.212, 'text': 'and in order to do this there can be data.', 'start': 286.271, 'duration': 2.941}, {'end': 292.353, 'text': 'scientists can take help of some ETL tools.', 'start': 289.212, 'duration': 3.141}, {'end': 300.215, 'text': 'there are some existing tools that a data scientist can take help of like, for example data stage or talent or informatica.', 'start': 292.353, 'duration': 7.862}, {'end': 305.618, 'text': 'These are pretty good tools for performing these ETL activities and getting the data.', 'start': 300.495, 'duration': 5.123}, {'end': 306.518, 'text': 'The next stage.', 'start': 305.758, 'duration': 0.76}, {'end': 309.96, 'text': 'now that you have the raw data into a data warehouse,', 'start': 306.518, 'duration': 3.442}, {'end': 316.304, 'text': 'you still probably are not in a position to straight away use this data for performing the data mining activities.', 'start': 309.96, 'duration': 6.344}], 'summary': 'Data science process involves data acquisition, preparation, mining, model building, testing, deployment, and maintenance.', 'duration': 66.093, 'max_score': 160.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ160921.jpg'}, {'end': 289.212, 'src': 'embed', 'start': 264.925, 'weight': 1, 'content': [{'end': 278.85, 'text': 'so a data warehouse is like a common place where data from different sources is brought together so that people can perform data science activities like reporting or data mining or statistical analysis and so on.', 'start': 264.925, 'duration': 13.925}, {'end': 283.891, 'text': 'so data from various sources is put in a centralized place which is known as a data warehouse.', 'start': 278.85, 'duration': 5.041}, {'end': 286.271, 'text': 'so that is also known as ETL.', 'start': 283.891, 'duration': 2.38}, {'end': 289.212, 'text': 'and in order to do this there can be data.', 'start': 286.271, 'duration': 2.941}], 'summary': 'Data warehouse centralizes data for data science activities like reporting, data mining, and statistical analysis.', 'duration': 24.287, 'max_score': 264.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ264925.jpg'}, {'end': 397.647, 'src': 'heatmap', 'start': 332.012, 'weight': 0, 'content': [{'end': 341.214, 'text': 'so a data scientist spends a lot of time almost 60 to 70 percent of the time in this part of the project or the process which is data preparation.', 'start': 332.012, 'duration': 9.202}, {'end': 348.096, 'text': "so there are again within this there can be multiple sub activities, starting from, let's say, data cleaning.", 'start': 341.214, 'duration': 6.882}, {'end': 350.676, 'text': 'you will probably have missing values.', 'start': 348.096, 'duration': 2.58}, {'end': 358.64, 'text': 'the data there is, some columns, the values are missing or the values are incorrect, there are null values and so on and so forth.', 'start': 350.676, 'duration': 7.964}, {'end': 361.402, 'text': 'so that is basically the data cleaning part of it.', 'start': 358.64, 'duration': 2.762}, {'end': 368.106, 'text': 'then you need to perform certain transformations, like, for example, normalizing the data and so on right.', 'start': 361.402, 'duration': 6.704}, {'end': 374.85, 'text': 'so you could probably have to modify a categorical values into numerical values and so on and so forth.', 'start': 368.106, 'duration': 6.744}, {'end': 377.452, 'text': 'so these are transformational activities.', 'start': 374.85, 'duration': 2.602}, {'end': 380.394, 'text': 'then we may have to handle outliers.', 'start': 377.452, 'duration': 2.942}, {'end': 389.22, 'text': 'so the data could be such that there are a few values which are way beyond the normal behavior of the data, for whatever reason.', 'start': 380.394, 'duration': 8.826}, {'end': 396.266, 'text': 'either people have keyed in wrong values or for some reason, some of the values are completely out of range.', 'start': 389.22, 'duration': 7.046}, {'end': 397.647, 'text': 'so those are known as outliers.', 'start': 396.266, 'duration': 1.381}], 'summary': 'Data scientists spend 60-70% of their time on data preparation, including data cleaning, transformations, and handling outliers.', 'duration': 65.635, 'max_score': 332.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ332012.jpg'}, {'end': 530.784, 'src': 'heatmap', 'start': 498.072, 'weight': 0.853, 'content': [{'end': 501.955, 'text': "So let's take an example and see how we go about cleaning the data.", 'start': 498.072, 'duration': 3.883}, {'end': 506.117, 'text': "And in this particular example, we're assuming we are using Python.", 'start': 502.275, 'duration': 3.842}, {'end': 511.439, 'text': "So let's Assume we loaded this data, which is the raw file.csv.", 'start': 506.377, 'duration': 5.062}, {'end': 514.22, 'text': 'This is how the customer data looks like.', 'start': 511.579, 'duration': 2.641}, {'end': 520.361, 'text': 'And we will see, for example, we take a closer look at the geography column.', 'start': 514.6, 'duration': 5.761}, {'end': 523.662, 'text': 'We will see that there are quite a few blank spaces.', 'start': 520.621, 'duration': 3.041}, {'end': 530.784, 'text': 'So how do we go about when we have some blank spaces or if it is a string value,', 'start': 524.121, 'duration': 6.663}], 'summary': 'Using python, clean raw file.csv customer data with blank spaces.', 'duration': 32.712, 'max_score': 498.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ498072.jpg'}], 'start': 120.846, 'title': 'Data science activities', 'summary': "Discusses the activities of a data scientist, including data acquisition, preparation, mining, modeling, and maintenance, with a focus on specific roles. it also explains the etl process, consuming 60-70% of a data scientist's time, covering data cleaning, transformation, handling outliers, integrity, and reduction.", 'chapters': [{'end': 227.014, 'start': 120.846, 'title': 'Data scientist activities', 'summary': 'Discusses the activities of a data scientist, including data acquisition, data preparation, data mining, data modeling, and model maintenance, in detail, with a focus on the specific roles within the area of data science.', 'duration': 106.168, 'highlights': ['Data acquisition involves collecting raw data from multiple sources and in various formats, which is then planned and prepared for data mining or modeling activities.', 'Data preparation is essential in getting the raw data ready for use in data models or mining activities, ensuring it is usable for analysis and modeling.', 'Model maintenance is necessary to tweak the model over time due to changes in processes or data, ensuring its continued accuracy and relevance.', 'Data mining includes exploratory activities and may involve building and testing machine learning models to gain insights and deploy them for use.', 'The role of a data scientist is defined as working with data, encompassing specific activities within the broader area of data science.']}, {'end': 705.374, 'start': 227.294, 'title': 'Etl process and data cleaning', 'summary': "Explains the etl process, involving extracting, transforming, and loading data into a data warehouse, as well as the data preparation stage, which consumes 60-70% of a data scientist's time, covering data cleaning, transformation, handling outliers, data integrity, and data reduction.", 'duration': 478.08, 'highlights': ["Data preparation stage consumes 60-70% of a data scientist's time, covering activities like data cleaning, transformation, handling outliers, data integrity, and data reduction. The data preparation stage, including data cleaning, transformation, handling outliers, data integrity, and data reduction, consumes 60-70% of a data scientist's time.", 'ETL process involves extracting, transforming, and loading data into a data warehouse, also referred to as ETL, where data from different sources is brought together for data science activities like reporting, data mining, and statistical analysis. The ETL process involves extracting, transforming, and loading data into a data warehouse, also referred to as ETL, where data from different sources is brought together for data science activities like reporting, data mining, and statistical analysis.', 'Data cleaning involves ensuring the data is valid, consistent, uniform, and accurate, where techniques like filling missing values with the mean, dropping rows with missing values, and setting thresholds for dropping records are employed. Data cleaning involves ensuring the data is valid, consistent, uniform, and accurate, employing techniques like filling missing values with the mean, dropping rows with missing values, and setting thresholds for dropping records.']}], 'duration': 584.528, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ120846.jpg', 'highlights': ["Data preparation stage consumes 60-70% of a data scientist's time, covering activities like data cleaning, transformation, handling outliers, data integrity, and data reduction.", 'ETL process involves extracting, transforming, and loading data into a data warehouse, also referred to as ETL, where data from different sources is brought together for data science activities like reporting, data mining, and statistical analysis.', 'Data acquisition involves collecting raw data from multiple sources and in various formats, which is then planned and prepared for data mining or modeling activities.', 'Data mining includes exploratory activities and may involve building and testing machine learning models to gain insights and deploy them for use.', 'Model maintenance is necessary to tweak the model over time due to changes in processes or data, ensuring its continued accuracy and relevance.']}, {'end': 1326.815, 'segs': [{'end': 740.731, 'src': 'heatmap', 'start': 710.336, 'weight': 0.828, 'content': [{'end': 712.837, 'text': 'so now we get into the data mining part.', 'start': 710.336, 'duration': 2.501}, {'end': 715.698, 'text': 'so what exactly we do in data mining?', 'start': 712.837, 'duration': 2.861}, {'end': 720.48, 'text': 'primarily we come up with ways to take meaningful decisions.', 'start': 715.698, 'duration': 4.782}, {'end': 725.521, 'text': 'so data mining will give us insights into the data, what is existing there,', 'start': 720.48, 'duration': 5.041}, {'end': 732.184, 'text': 'and then we can do additional stuff like maybe machine learning and so on to get perform advanced analytics and so on.', 'start': 725.521, 'duration': 6.663}, {'end': 740.731, 'text': 'So one of the first steps we do is what is known as data discovery and which is basically like exploratory analysis.', 'start': 732.324, 'duration': 8.407}], 'summary': 'Data mining provides insights for making meaningful decisions and performing advanced analytics through techniques like machine learning.', 'duration': 30.395, 'max_score': 710.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ710336.jpg'}, {'end': 776.437, 'src': 'embed', 'start': 747.476, 'weight': 0, 'content': [{'end': 754.122, 'text': 'So Tableau is excellent data mining or actually more of a reporting or a BI tool.', 'start': 747.476, 'duration': 6.646}, {'end': 759.986, 'text': 'and you can download a trial version of Tableau at tableau.com.', 'start': 754.742, 'duration': 5.244}, {'end': 764.949, 'text': 'or there is also Tableau Public, which is free and you can actually use and play around.', 'start': 759.986, 'duration': 4.963}, {'end': 770.573, 'text': 'However, if you want to use it for enterprise purpose, then it is a commercial software,', 'start': 765.069, 'duration': 5.504}, {'end': 776.437, 'text': 'so you need to purchase license and you can then run some of the data mining activities.', 'start': 770.573, 'duration': 5.864}], 'summary': 'Tableau is a versatile bi tool, offering trial & free versions, but requires purchase for enterprise use.', 'duration': 28.961, 'max_score': 747.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ747476.jpg'}, {'end': 865.538, 'src': 'embed', 'start': 838.393, 'weight': 1, 'content': [{'end': 843.898, 'text': "and we want to, let's assume, consider these three of them, like, let's say, gender,", 'start': 838.393, 'duration': 5.505}, {'end': 856.45, 'text': 'credit card and geography these as a criteria and analyze if these are in any way impacting or have some bearing on the customer exiting or the customer exit behavior.', 'start': 843.898, 'duration': 12.552}, {'end': 856.87, 'text': 'OK.', 'start': 856.67, 'duration': 0.2}, {'end': 864.537, 'text': "So let's use tab loop and very quickly we will be able to find out how these parameters are affecting.", 'start': 857.03, 'duration': 7.507}, {'end': 864.857, 'text': 'All right.', 'start': 864.597, 'duration': 0.26}, {'end': 865.538, 'text': "So let's see.", 'start': 864.937, 'duration': 0.601}], 'summary': 'Analyze gender, credit card, and geography as criteria for customer exit behavior.', 'duration': 27.145, 'max_score': 838.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ838393.jpg'}, {'end': 1011.537, 'src': 'embed', 'start': 983.041, 'weight': 2, 'content': [{'end': 987.865, 'text': "So let's see if having a credit card has any impact on the customer exit behavior.", 'start': 983.041, 'duration': 4.824}, {'end': 993.93, 'text': 'So just like before we drag and drop the credit card has credit card column if we drag and drop here.', 'start': 987.965, 'duration': 5.965}, {'end': 1000.332, 'text': 'And then we will see that there is pretty much no difference between people having credit card and not having credit card.', 'start': 994.17, 'duration': 6.162}, {'end': 1011.537, 'text': '20.81% of people who have no credit card have exited and similarly 20.18% of people who have credit card have also exited.', 'start': 1001.893, 'duration': 9.644}], 'summary': 'Credit card has no significant impact on customer exit behavior; 20.81% without card exited, 20.18% with card exited.', 'duration': 28.496, 'max_score': 983.041, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ983041.jpg'}, {'end': 1104.537, 'src': 'embed', 'start': 1055.967, 'weight': 3, 'content': [{'end': 1058.248, 'text': 'we can keep and do further analysis.', 'start': 1055.967, 'duration': 2.281}, {'end': 1059.849, 'text': 'all right.', 'start': 1059.349, 'duration': 0.5}, {'end': 1063.051, 'text': 'so what are some of the advantages of data mining?', 'start': 1059.849, 'duration': 3.202}, {'end': 1072.795, 'text': 'bit more detailed analysis can help us in predicting the future trends, and it also helps in identifying customer behavior patterns.', 'start': 1063.051, 'duration': 9.744}, {'end': 1078.358, 'text': 'okay, so you can take informed decisions because the data is telling you or providing you with some insights,', 'start': 1072.795, 'duration': 5.563}, {'end': 1080.199, 'text': 'and then you take a decision based on that.', 'start': 1078.358, 'duration': 1.841}, {'end': 1088.945, 'text': 'If there is any fraudulent activity, data mining will help in quickly identifying such a fraud as well and, of course,', 'start': 1080.439, 'duration': 8.506}, {'end': 1097.01, 'text': 'it will also help us in identifying the right algorithm for performing more advanced data mining activities like machine learning and so on.', 'start': 1088.945, 'duration': 8.065}, {'end': 1097.49, 'text': 'all right.', 'start': 1097.21, 'duration': 0.28}, {'end': 1104.537, 'text': 'so the next activity, now that we have the data, we have prepared the data and perform some data mining activity.', 'start': 1097.49, 'duration': 7.047}], 'summary': 'Data mining helps predict trends, identify customer behavior, and detect fraudulent activity, enabling informed decision-making.', 'duration': 48.57, 'max_score': 1055.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1055967.jpg'}, {'end': 1265.48, 'src': 'heatmap', 'start': 1104.537, 'weight': 0.843, 'content': [{'end': 1107, 'text': 'the next step is model building.', 'start': 1104.537, 'duration': 2.463}, {'end': 1109.162, 'text': "let's take a look at model building.", 'start': 1107, 'duration': 2.162}, {'end': 1111.504, 'text': 'so what is a model building?', 'start': 1109.162, 'duration': 2.342}, {'end': 1120.887, 'text': 'if we want to perform a more detailed data mining activity, like maybe perform some machine learning, then you need to build a model.', 'start': 1111.504, 'duration': 9.383}, {'end': 1122.247, 'text': 'and how do you build a model?', 'start': 1120.887, 'duration': 1.36}, {'end': 1131.449, 'text': 'first thing is you need to select which algorithm you want to use to solve the problem on hand and also what kind of data that is available,', 'start': 1122.247, 'duration': 9.202}, {'end': 1132.429, 'text': 'and so on and so forth.', 'start': 1131.449, 'duration': 0.98}, {'end': 1139.51, 'text': 'so you need to make a choice of the algorithm and based on that, you go ahead and create a model, train the model and so on.', 'start': 1132.429, 'duration': 7.081}, {'end': 1146.375, 'text': 'Now, machine learning is kind of at a very high level, classified into supervised and unsupervised.', 'start': 1139.67, 'duration': 6.705}, {'end': 1156.103, 'text': 'So if we want to predict a continuous value could be a price or a temperature or a height or a length or things like that.', 'start': 1146.575, 'duration': 9.528}, {'end': 1162.846, 'text': 'so those are continuous values and if you want to find some of those, then you use techniques like regression, linear regression,', 'start': 1156.443, 'duration': 6.403}, {'end': 1165.687, 'text': 'simple linear regression, multiple linear regression and so on.', 'start': 1162.846, 'duration': 2.841}, {'end': 1167.047, 'text': 'so these are the algorithms.', 'start': 1165.687, 'duration': 1.36}, {'end': 1173.73, 'text': 'on the other hand, there will be situations, or there may be situations where you need to perform unsupervised learning.', 'start': 1167.047, 'duration': 6.683}, {'end': 1179.613, 'text': "case of unsupervised learning, you don't have any historical labeled data so to learn from.", 'start': 1173.73, 'duration': 5.883}, {'end': 1182.336, 'text': 'so that is when you use unsupervised learning.', 'start': 1179.613, 'duration': 2.723}, {'end': 1186.5, 'text': 'and some of the algorithms in unsupervised learning are clustering,', 'start': 1182.336, 'duration': 4.164}, {'end': 1193.766, 'text': 'k-means clustering is the most common algorithm used in unsupervised learning and similarly, in supervised learning,', 'start': 1186.5, 'duration': 7.266}, {'end': 1201.509, 'text': 'if you want to perform some activity on categorical values like, for example, it is not measured but it is counted,', 'start': 1193.766, 'duration': 7.743}, {'end': 1209.491, 'text': 'like you want to classify whether this image is a cat or a dog, whether you want to classify whether this customer will buy the product or not,', 'start': 1201.509, 'duration': 7.982}, {'end': 1213.152, 'text': 'or you want to classify whether this email is spam or not spam.', 'start': 1209.491, 'duration': 3.661}, {'end': 1218.893, 'text': 'so these are examples of categorical values and these are examples of classification.', 'start': 1213.152, 'duration': 5.741}, {'end': 1225.617, 'text': 'then you have algorithms like logistic regression, k, nearest neighbor or knn and support vector machine.', 'start': 1218.893, 'duration': 6.724}, {'end': 1231.804, 'text': 'so these are some of the algorithms that are used in this case and similarly, in case of unsupervised learning,', 'start': 1225.617, 'duration': 6.187}, {'end': 1238.731, 'text': 'if you need to perform on categorical values, you have some algorithms like association analysis and hidden marco model.', 'start': 1231.804, 'duration': 6.927}, {'end': 1246.755, 'text': "Okay, so, in order to understand this better, let's take an example and take you through the whole process.", 'start': 1239.013, 'duration': 7.742}, {'end': 1251.136, 'text': 'And then we will also see how the code can be written to perform this.', 'start': 1246.935, 'duration': 4.201}, {'end': 1259.398, 'text': "Now let's take our example here, where we want to perform a supervised learning, which is basically we want to do a multilinear regression,", 'start': 1251.196, 'duration': 8.202}, {'end': 1261.719, 'text': 'which means there are multiple independent variables.', 'start': 1259.398, 'duration': 2.321}, {'end': 1265.48, 'text': 'And then you want to perform a linear regression to predict certain values.', 'start': 1261.959, 'duration': 3.521}], 'summary': 'Model building involves choosing algorithms for supervised or unsupervised learning, such as regression, clustering, logistic regression, k-nearest neighbor, and support vector machine.', 'duration': 160.943, 'max_score': 1104.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1104537.jpg'}, {'end': 1167.047, 'src': 'embed', 'start': 1122.247, 'weight': 5, 'content': [{'end': 1131.449, 'text': 'first thing is you need to select which algorithm you want to use to solve the problem on hand and also what kind of data that is available,', 'start': 1122.247, 'duration': 9.202}, {'end': 1132.429, 'text': 'and so on and so forth.', 'start': 1131.449, 'duration': 0.98}, {'end': 1139.51, 'text': 'so you need to make a choice of the algorithm and based on that, you go ahead and create a model, train the model and so on.', 'start': 1132.429, 'duration': 7.081}, {'end': 1146.375, 'text': 'Now, machine learning is kind of at a very high level, classified into supervised and unsupervised.', 'start': 1139.67, 'duration': 6.705}, {'end': 1156.103, 'text': 'So if we want to predict a continuous value could be a price or a temperature or a height or a length or things like that.', 'start': 1146.575, 'duration': 9.528}, {'end': 1162.846, 'text': 'so those are continuous values and if you want to find some of those, then you use techniques like regression, linear regression,', 'start': 1156.443, 'duration': 6.403}, {'end': 1165.687, 'text': 'simple linear regression, multiple linear regression and so on.', 'start': 1162.846, 'duration': 2.841}, {'end': 1167.047, 'text': 'so these are the algorithms.', 'start': 1165.687, 'duration': 1.36}], 'summary': 'Select algorithm to solve problem, based on data. use regression for continuous value prediction.', 'duration': 44.8, 'max_score': 1122.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1122247.jpg'}, {'end': 1238.731, 'src': 'embed', 'start': 1209.491, 'weight': 7, 'content': [{'end': 1213.152, 'text': 'or you want to classify whether this email is spam or not spam.', 'start': 1209.491, 'duration': 3.661}, {'end': 1218.893, 'text': 'so these are examples of categorical values and these are examples of classification.', 'start': 1213.152, 'duration': 5.741}, {'end': 1225.617, 'text': 'then you have algorithms like logistic regression, k, nearest neighbor or knn and support vector machine.', 'start': 1218.893, 'duration': 6.724}, {'end': 1231.804, 'text': 'so these are some of the algorithms that are used in this case and similarly, in case of unsupervised learning,', 'start': 1225.617, 'duration': 6.187}, {'end': 1238.731, 'text': 'if you need to perform on categorical values, you have some algorithms like association analysis and hidden marco model.', 'start': 1231.804, 'duration': 6.927}], 'summary': 'Email classification uses categorical values and algorithms like logistic regression, k-nearest neighbor, and support vector machine.', 'duration': 29.24, 'max_score': 1209.491, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1209491.jpg'}], 'start': 705.734, 'title': 'Data mining with tableau and model building', 'summary': 'Covers using tableau in data mining to analyze customer exit behavior based on gender, credit card, and geography and discusses the advantages of data mining for predicting future trends, model building process, and the use of algorithms in supervised and unsupervised learning.', 'chapters': [{'end': 1055.967, 'start': 705.734, 'title': 'Data mining with tableau', 'summary': 'Covers the use of tableau in data mining, analyzing customer exit behavior based on gender, credit card, and geography, revealing that gender and geography have a noticeable impact, while credit card does not.', 'duration': 350.233, 'highlights': ['Tableau is used for data mining and exploratory analysis, with a trial version available for download at tableau.com and a free version called Tableau Public. Tableau is a tool for data mining and exploratory analysis, available for trial download at tableau.com and with a free version called Tableau Public.', 'Gender and geography significantly impact customer exit behavior, with females exhibiting a higher exit rate than males, and different geographies showing varying exit rates. Analysis reveals that gender and geography significantly impact customer exit behavior, with females exhibiting a higher exit rate than males, and different geographies showing varying exit rates.', 'Credit card presence does not have a substantial impact on customer exit behavior, as the exit rates for customers with and without credit cards are similar. The presence of a credit card does not have a substantial impact on customer exit behavior, as the exit rates for customers with and without credit cards are similar.']}, {'end': 1326.815, 'start': 1055.967, 'title': 'Data mining and model building', 'summary': 'Discusses the advantages of data mining for predicting future trends and identifying customer behavior patterns, the process of model building, and the use of algorithms in supervised and unsupervised learning for data analysis and predictions.', 'duration': 270.848, 'highlights': ['Data mining can help in predicting future trends and identifying customer behavior patterns, enabling informed decision making. Predicting future trends, identifying customer behavior patterns', 'Data mining facilitates quick identification of fraudulent activity and selection of the right algorithms for advanced data mining activities like machine learning. Identification of fraudulent activity, selection of algorithms for advanced data mining', 'Model building involves the selection of algorithms and the creation of a model, and machine learning is classified into supervised and unsupervised learning. Selection of algorithms, creation of a model, supervised and unsupervised learning', 'Supervised learning involves techniques like regression for predicting continuous values, while unsupervised learning uses algorithms like clustering. Supervised learning techniques, unsupervised learning algorithms', 'Algorithms used in supervised learning for categorical values include logistic regression, k-nearest neighbor, and support vector machine, while unsupervised learning involves algorithms like association analysis and hidden markov model. Algorithms for categorical values in supervised learning, algorithms in unsupervised learning']}], 'duration': 621.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ705734.jpg', 'highlights': ['Tableau is used for data mining and exploratory analysis, available for trial download at tableau.com and with a free version called Tableau Public.', 'Analysis reveals that gender and geography significantly impact customer exit behavior, with females exhibiting a higher exit rate than males, and different geographies showing varying exit rates.', 'The presence of a credit card does not have a substantial impact on customer exit behavior, as the exit rates for customers with and without credit cards are similar.', 'Data mining can help in predicting future trends and identifying customer behavior patterns, enabling informed decision making.', 'Identification of fraudulent activity, selection of algorithms for advanced data mining.', 'Model building involves the selection of algorithms, creation of a model, supervised and unsupervised learning.', 'Supervised learning involves techniques like regression for predicting continuous values, while unsupervised learning uses algorithms like clustering.', 'Algorithms used in supervised learning for categorical values include logistic regression, k-nearest neighbor, and support vector machine, while unsupervised learning involves algorithms like association analysis and hidden markov model.']}, {'end': 1653.03, 'segs': [{'end': 1412.357, 'src': 'embed', 'start': 1387.807, 'weight': 0, 'content': [{'end': 1395.534, 'text': 'and then scikit-learn or sklearn is the library which we will use actually for this particular machine learning activity which is linear regression.', 'start': 1387.807, 'duration': 7.727}, {'end': 1399.895, 'text': 'So we have NumPy, we have pandas and so on and so forth.', 'start': 1395.774, 'duration': 4.121}, {'end': 1409.877, 'text': 'So all these libraries are imported and then we load our data and the data is in the form of a CSV file and there are different files for each year.', 'start': 1399.915, 'duration': 9.962}, {'end': 1412.357, 'text': 'So we have data for 2015, 16 and 17.', 'start': 1409.937, 'duration': 2.42}], 'summary': 'Using scikit-learn for linear regression, with data for 2015-2017.', 'duration': 24.55, 'max_score': 1387.807, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1387807.jpg'}, {'end': 1512.466, 'src': 'heatmap', 'start': 1433.009, 'weight': 2, 'content': [{'end': 1437.072, 'text': 'so we are reading the file, each of these files for each year,', 'start': 1433.009, 'duration': 4.063}, {'end': 1443.136, 'text': 'and this is basically we are creating a list of all the names of the columns we will be using later on.', 'start': 1437.072, 'duration': 6.064}, {'end': 1444.277, 'text': 'we will see in the code.', 'start': 1443.136, 'duration': 1.141}, {'end': 1449.318, 'text': 'so we have loaded 2015, then 2016 and then also 2017..', 'start': 1444.277, 'duration': 5.041}, {'end': 1455.32, 'text': 'so we have created three data frames and then we concatenate all these three data frames.', 'start': 1449.318, 'duration': 6.002}, {'end': 1456.64, 'text': 'this is what we are doing here.', 'start': 1455.32, 'duration': 1.32}, {'end': 1459.981, 'text': 'then we identify which of these columns are required.', 'start': 1456.64, 'duration': 3.341}, {'end': 1463.542, 'text': 'which, for example, some of the categorical values do we really need?', 'start': 1459.981, 'duration': 3.561}, {'end': 1464.402, 'text': "we probably don't.", 'start': 1463.542, 'duration': 0.86}, {'end': 1471.544, 'text': "then we drop those columns so that we don't unnecessarily use all the columns and make the computation complicated.", 'start': 1464.402, 'duration': 7.142}, {'end': 1482.071, 'text': 'we can then create some plots using plotly library, and it has some powerful features, including creation or creation of maps and so on.', 'start': 1471.764, 'duration': 10.307}, {'end': 1488.076, 'text': 'just to understand the pattern, the happiness quotient, or how the happiness is across all the countries.', 'start': 1482.071, 'duration': 6.005}, {'end': 1489.897, 'text': "so it's a nice visualization.", 'start': 1488.076, 'duration': 1.821}, {'end': 1494.28, 'text': 'we can see each of these countries how they are in terms of their happiness score.', 'start': 1489.897, 'duration': 4.383}, {'end': 1495.86, 'text': 'this is the legend here.', 'start': 1494.62, 'duration': 1.24}, {'end': 1501.162, 'text': 'so the lighter colored countries have lower ranking, and so these are the lower ranking ones.', 'start': 1495.86, 'duration': 5.302}, {'end': 1506.524, 'text': 'and these are higher ranking, which means that the ones with these dark colors are the happiest ones.', 'start': 1501.162, 'duration': 5.362}, {'end': 1512.466, 'text': 'so, as you can see here, australia and maybe this side, uh, us and so on, are the happiest ones.', 'start': 1506.524, 'duration': 5.942}], 'summary': 'Concatenated and analyzed 2015, 2016, and 2017 data frames to visualize global happiness patterns.', 'duration': 49.062, 'max_score': 1433.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1433009.jpg'}, {'end': 1542.152, 'src': 'embed', 'start': 1501.162, 'weight': 4, 'content': [{'end': 1506.524, 'text': 'and these are higher ranking, which means that the ones with these dark colors are the happiest ones.', 'start': 1501.162, 'duration': 5.362}, {'end': 1512.466, 'text': 'so, as you can see here, australia and maybe this side, uh, us and so on, are the happiest ones.', 'start': 1506.524, 'duration': 5.942}, {'end': 1519.767, 'text': 'Okay, the other thing that we need to do is the correlation between the happiness score and happiness rank.', 'start': 1513.306, 'duration': 6.461}, {'end': 1527.409, 'text': 'We can find a correlation using a scatterplot and we find that yes, they are kind of inversely proportionate, which is obvious.', 'start': 1520.107, 'duration': 7.302}, {'end': 1535.211, 'text': 'So if the score is high, happiness score is high, then they are ranked number one, for example, highest is scored as number one.', 'start': 1527.469, 'duration': 7.742}, {'end': 1536.651, 'text': "So that's the idea behind this.", 'start': 1535.231, 'duration': 1.42}, {'end': 1542.152, 'text': 'So the happiness score given here and the happiness rank is actually given here.', 'start': 1536.711, 'duration': 5.441}], 'summary': 'Higher ranking countries have higher happiness score; australia and us are among the happiest.', 'duration': 40.99, 'max_score': 1501.162, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1501162.jpg'}, {'end': 1661.933, 'src': 'embed', 'start': 1633.837, 'weight': 6, 'content': [{'end': 1637.339, 'text': 'The dark blue color indicates pretty much no correlation.', 'start': 1633.837, 'duration': 3.502}, {'end': 1646.225, 'text': 'So from this heat map, we see that happiness and economy and family are probably also health, probably are the most correlated.', 'start': 1637.459, 'duration': 8.766}, {'end': 1653.03, 'text': 'And then it keeps decreasing after freedom kind of keeps decreasing and coming to pretty much 0.', 'start': 1646.506, 'duration': 6.524}, {'end': 1661.933, 'text': 'Alright. so that is a correlation graph, and then we can probably use this to find out which are the columns that need to be dropped,', 'start': 1653.03, 'duration': 8.903}], 'summary': 'Happiness, economy, family, and health show the highest correlation, while freedom demonstrates a decreasing correlation.', 'duration': 28.096, 'max_score': 1633.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1633837.jpg'}], 'start': 1326.815, 'title': 'Python for linear regression analysis and happiness data analysis', 'summary': 'Discusses using python for linear regression analysis with data from 2015, 2016, and 2017, and explores global happiness data, revealing correlations and demonstrating column removal.', 'chapters': [{'end': 1482.071, 'start': 1326.815, 'title': 'Python for linear regression analysis', 'summary': 'Discusses the process of using python to perform linear regression analysis, including importing libraries, loading and combining data from csv files for 2015, 2016, and 2017, and identifying and dropping unnecessary columns for simplifying computation.', 'duration': 155.256, 'highlights': ['The chapter discusses the process of using Python to perform linear regression analysis. Key point: Python used for linear regression analysis.', 'Importing libraries in Python is a crucial step required to perform the analysis, which includes libraries like NumPy, pandas, and scikit-learn. Key point: Importing essential libraries for data manipulation and machine learning.', 'Data for 2015, 2016, and 2017 is loaded and combined from CSV files to prepare a single data frame. Key point: Loading and combining data from multiple CSV files for analysis.', 'Identifying and dropping unnecessary columns is done to simplify the computation process. Key point: Simplifying computation by removing unnecessary columns.']}, {'end': 1653.03, 'start': 1482.071, 'title': 'Happiness data analysis', 'summary': 'Explores the global happiness quotient, revealing the correlation between happiness score and rank and demonstrating the removal of redundant columns, with the heatmap indicating the strongest correlation between happiness, economy, and family.', 'duration': 170.959, 'highlights': ['The darker colored countries indicate higher happiness ranking, with Australia and the US ranking among the happiest ones.', 'There is a strong correlation between happiness rank and happiness score, with the scatterplot confirming an inverse proportionality.', 'The heatmap demonstrates that happiness, economy, and family are highly correlated, while freedom shows a decreasing correlation, with the darkest color indicating no correlation.']}], 'duration': 326.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1326815.jpg', 'highlights': ['Python used for linear regression analysis.', 'Importing essential libraries for data manipulation and machine learning.', 'Loading and combining data from multiple CSV files for analysis.', 'Simplifying computation by removing unnecessary columns.', 'Darker colored countries indicate higher happiness ranking.', 'Strong correlation between happiness rank and happiness score.', 'Happiness, economy, and family are highly correlated.', 'Freedom shows a decreasing correlation.']}, {'end': 2067.777, 'segs': [{'end': 1693.596, 'src': 'embed', 'start': 1653.03, 'weight': 0, 'content': [{'end': 1661.933, 'text': 'Alright. so that is a correlation graph, and then we can probably use this to find out which are the columns that need to be dropped,', 'start': 1653.03, 'duration': 8.903}, {'end': 1667.854, 'text': 'which do not have very high correlation, and we take only those columns that we will need.', 'start': 1661.933, 'duration': 5.921}, {'end': 1670.355, 'text': 'So this is the code for dropping some of the columns.', 'start': 1667.894, 'duration': 2.461}, {'end': 1678.602, 'text': 'once we have prepared the data, when we have the required columns, then we use scikit-learn to actually split the data.', 'start': 1670.715, 'duration': 7.887}, {'end': 1681.405, 'text': 'first of all this is a normal machine learning process.', 'start': 1678.602, 'duration': 2.803}, {'end': 1684.828, 'text': 'you need to split the data into training and test data set.', 'start': 1681.405, 'duration': 3.423}, {'end': 1688.011, 'text': 'in this case, we are splitting into 80, 20.', 'start': 1684.828, 'duration': 3.183}, {'end': 1692.175, 'text': 'so 80 is the training data set and 20 is the test data set.', 'start': 1688.011, 'duration': 4.164}, {'end': 1693.596, 'text': "so that's what we are doing here.", 'start': 1692.175, 'duration': 1.421}], 'summary': 'Using correlation graph, dropping columns with low correlation, then splitting data into 80% training and 20% test data using scikit-learn.', 'duration': 40.566, 'max_score': 1653.03, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1653030.jpg'}, {'end': 1769.25, 'src': 'embed', 'start': 1720.382, 'weight': 1, 'content': [{'end': 1724.764, 'text': 'and then the next is to create a linear regression instance.', 'start': 1720.382, 'duration': 4.382}, {'end': 1725.904, 'text': 'so this is what we are doing.', 'start': 1724.764, 'duration': 1.14}, {'end': 1734.367, 'text': 'we are creating an instance of linear regression and then we train the model using the fit function and we are passing x and y,', 'start': 1725.904, 'duration': 8.463}, {'end': 1740.01, 'text': 'which is the x value and the label data, regular input and the label data, label information.', 'start': 1734.367, 'duration': 5.643}, {'end': 1745.776, 'text': 'then we do the test, we run the or we perform the evaluation on the test data set.', 'start': 1740.01, 'duration': 5.766}, {'end': 1755.366, 'text': 'so this is what we are doing with the test data set and then we will evaluate how accurate the model is and using the scikit-learn functionality itself,', 'start': 1745.776, 'duration': 9.59}, {'end': 1760.207, 'text': 'we can also see what are the various parameters and what are the various coefficients.', 'start': 1755.626, 'duration': 4.581}, {'end': 1769.25, 'text': 'because in linear regression you will get like a equation of like a straight line y is equal to beta 0 plus beta 1 x 1 plus beta 2 x 2.', 'start': 1760.207, 'duration': 9.043}], 'summary': 'Creating and evaluating a linear regression model with scikit-learn.', 'duration': 48.868, 'max_score': 1720.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1720382.jpg'}, {'end': 1874.291, 'src': 'embed', 'start': 1846.189, 'weight': 6, 'content': [{'end': 1850.513, 'text': 'We will create a list of all these column names we will be using later.', 'start': 1846.189, 'duration': 4.324}, {'end': 1855.217, 'text': 'So for now just I will run this code and no need of major explanation at this point.', 'start': 1850.613, 'duration': 4.604}, {'end': 1859.44, 'text': 'We know that some of these columns probably are not required.', 'start': 1855.478, 'duration': 3.962}, {'end': 1866.065, 'text': "so you can use this drop functionality to remove some of the columns which we don't need, like, for example,", 'start': 1859.44, 'duration': 6.625}, {'end': 1870.768, 'text': 'region and standard error will not be contributing to our model.', 'start': 1866.065, 'duration': 4.703}, {'end': 1874.291, 'text': 'so we will basically drop those values out here.', 'start': 1870.768, 'duration': 3.523}], 'summary': 'Creating a list of column names for later use, dropping unnecessary columns such as region and standard error.', 'duration': 28.102, 'max_score': 1846.189, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1846189.jpg'}, {'end': 1978.234, 'src': 'embed', 'start': 1935.951, 'weight': 4, 'content': [{'end': 1943.693, 'text': 'So this is a quick way to see how the data is and initial little bit of exploratory analysis can be done here.', 'start': 1935.951, 'duration': 7.742}, {'end': 1947.474, 'text': "So what is the maximum value, what's the minimum value and so on for each of the columns.", 'start': 1943.833, 'duration': 3.641}, {'end': 1953.424, 'text': 'All right, so then we go ahead and create some visualizations using Plotly.', 'start': 1947.802, 'duration': 5.622}, {'end': 1956.205, 'text': 'So let us go and build a plot.', 'start': 1953.624, 'duration': 2.581}, {'end': 1963.828, 'text': 'So if we see here, now this is the relation correlation between happiness rank and happiness score.', 'start': 1956.605, 'duration': 7.223}, {'end': 1966.409, 'text': 'This is what we have seen in the slides as well.', 'start': 1964.028, 'duration': 2.381}, {'end': 1969.23, 'text': 'We can see that there is a tight correlation between them.', 'start': 1966.609, 'duration': 2.621}, {'end': 1974.312, 'text': 'Only thing is it is inverse correlation, but otherwise they are very tightly correlated,', 'start': 1969.39, 'duration': 4.922}, {'end': 1978.234, 'text': 'which also says that they both probably provide the same information.', 'start': 1974.312, 'duration': 3.922}], 'summary': 'Quick exploratory analysis reveals tight inverse correlation between happiness rank and happiness score.', 'duration': 42.283, 'max_score': 1935.951, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1935951.jpg'}, {'end': 2072.446, 'src': 'embed', 'start': 2046.094, 'weight': 5, 'content': [{'end': 2053.299, 'text': 'but then others are, for example, with higher values, are economy and then health, and then maybe family and freedom.', 'start': 2046.094, 'duration': 7.205}, {'end': 2058.161, 'text': 'so these are generosity and trust are not very highly correlated to happiness score.', 'start': 2053.299, 'duration': 4.862}, {'end': 2067.777, 'text': 'so that is, uh, one quick exploratory analysis we can do and therefore we can drop the country and the happiness rank,', 'start': 2058.422, 'duration': 9.355}, {'end': 2072.446, 'text': "because they also again don't have any major impact on the analysis, on our.", 'start': 2067.777, 'duration': 4.669}], 'summary': 'Economy and health have higher values; generosity and trust are not highly correlated to happiness score.', 'duration': 26.352, 'max_score': 2046.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2046094.jpg'}], 'start': 1653.03, 'title': 'Linear regression, data analysis, and visualization', 'summary': 'Covers data preparation, including dropping columns with low correlation, training a linear regression model, and evaluating its accuracy, along with data analysis and visualization techniques such as correlation visualizations and exploratory analysis, revealing tight correlation between happiness rank and score, and high correlation of happiness score with economy, health, family, and freedom.', 'chapters': [{'end': 1845.869, 'start': 1653.03, 'title': 'Linear regression and data preparation', 'summary': 'Covers data preparation including dropping columns with low correlation, splitting data into 80-20 for training and testing, creating a linear regression instance, training the model, evaluating its accuracy, and analyzing coefficients using scikit-learn.', 'duration': 192.839, 'highlights': ['The chapter covers the process of splitting the data into training and test data sets, with a split of 80-20. Splitting data into 80-20 for training and testing.', 'The chapter discusses the process of creating a linear regression instance and training the model using the fit function. Creating a linear regression instance and training the model.', 'The chapter explains how to evaluate the accuracy of the model and analyze coefficients using scikit-learn functionality. Evaluating the accuracy of the model and analyzing coefficients using scikit-learn.', 'The chapter emphasizes the process of dropping columns with low correlation to prepare the data for analysis. Dropping columns with low correlation to prepare the data for analysis.']}, {'end': 2067.777, 'start': 1846.189, 'title': 'Data analysis and visualization', 'summary': 'Covers data preprocessing, including dropping unnecessary columns and concatenating data from 2016 and 2017, followed by exploratory analysis using describe function and correlation visualizations, indicating the tight correlation between happiness rank and score and the correlation heatmap showing high correlation of happiness score with economy, health, family, and freedom.', 'duration': 221.588, 'highlights': ['The chapter covers data preprocessing, including dropping unnecessary columns and concatenating data from 2016 and 2017, followed by exploratory analysis using describe function and correlation visualizations, indicating the tight correlation between happiness rank and score and the correlation heatmap showing high correlation of happiness score with economy, health, family, and freedom.', 'The correlation heatmap reveals that happiness score is highly correlated with economy, health, family, and freedom, with values close to one, indicating strong correlations.', 'The describe function provides information on the count, mean value, and standard deviation of the numeric columns, allowing for initial exploratory analysis of the data.', 'The chapter demonstrates the removal of unwanted columns such as region and standard error, streamlining the dataset for analysis and visualization.', 'A visualization using Plotly depicts the tight inverse correlation between happiness rank and happiness score, suggesting their redundancy, leading to the decision to drop happiness rank from the dataset.']}], 'duration': 414.747, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ1653030.jpg', 'highlights': ['Splitting data into 80-20 for training and testing.', 'Creating a linear regression instance and training the model.', 'Evaluating the accuracy of the model and analyzing coefficients using scikit-learn.', 'Dropping columns with low correlation to prepare the data for analysis.', 'Exploratory analysis using describe function and correlation visualizations.', 'Correlation heatmap showing high correlation of happiness score with economy, health, family, and freedom.', 'Removal of unwanted columns such as region and standard error, streamlining the dataset for analysis and visualization.', 'Visualization depicting the tight inverse correlation between happiness rank and happiness score.']}, {'end': 2632.459, 'segs': [{'end': 2129.722, 'src': 'embed', 'start': 2067.777, 'weight': 1, 'content': [{'end': 2072.446, 'text': "because they also again don't have any major impact on the analysis, on our.", 'start': 2067.777, 'duration': 4.669}, {'end': 2075.638, 'text': 'So now we have prepared our data.', 'start': 2073.016, 'duration': 2.622}, {'end': 2078.438, 'text': 'there was no need to clean the data because the data was clean.', 'start': 2075.638, 'duration': 2.8}, {'end': 2082.442, 'text': 'but if there were some missing values and so on, as we have discussed in the slides,', 'start': 2078.438, 'duration': 4.004}, {'end': 2085.504, 'text': 'we would have had to perform some of the data cleaning activities as well.', 'start': 2082.442, 'duration': 3.062}, {'end': 2090.266, 'text': 'But in this case, the data was clean, all we needed to do was just the preparation part.', 'start': 2085.704, 'duration': 4.562}, {'end': 2094.911, 'text': 'We removed some unwanted columns and we did some exploratory data analysis.', 'start': 2091.047, 'duration': 3.864}, {'end': 2098.097, 'text': 'Now we are ready to perform the machine learning activity.', 'start': 2095.313, 'duration': 2.784}, {'end': 2102.503, 'text': 'So we use scikit-learn for doing the machine learning.', 'start': 2098.377, 'duration': 4.126}, {'end': 2107.71, 'text': 'scikit-learn is a Python library that is available for performing our machine learning.', 'start': 2102.523, 'duration': 5.187}, {'end': 2114.193, 'text': 'Once again we will import some of these libraries like pandas and numpy and also scikit-learn.', 'start': 2107.89, 'duration': 6.303}, {'end': 2118.636, 'text': 'First step we will do is split the data in 2080 format.', 'start': 2114.454, 'duration': 4.182}, {'end': 2125.079, 'text': 'So you have all the test data which is 20% of the data is test data and 80% is your training data.', 'start': 2118.736, 'duration': 6.343}, {'end': 2129.722, 'text': 'So this test size indicates how much of it is the what is the size of the test data.', 'start': 2125.119, 'duration': 4.603}], 'summary': 'Data was prepared, cleaned, and explored before machine learning with scikit-learn; data split into 20% test and 80% training.', 'duration': 61.945, 'max_score': 2067.777, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2067777.jpg'}, {'end': 2324.706, 'src': 'embed', 'start': 2289.508, 'weight': 0, 'content': [{'end': 2296.232, 'text': 'which is a good sign and which is one of the measures of how well our model is performing.', 'start': 2289.508, 'duration': 6.724}, {'end': 2304.956, 'text': 'we can do one more quick plot to just see how the actual values and the predicted values are looking, and once again you can see that,', 'start': 2296.232, 'duration': 8.724}, {'end': 2308.978, 'text': 'as we have seen from the root mean square error, root mean square error is very, very low.', 'start': 2304.956, 'duration': 4.022}, {'end': 2317.261, 'text': 'that means that the actual values and the predicted values are pretty much matching up, almost matching up, and this plot also shows the same.', 'start': 2308.978, 'duration': 8.283}, {'end': 2322.884, 'text': 'so this line is going through the predicted values and the actual values and the difference is very, very low.', 'start': 2317.261, 'duration': 5.623}, {'end': 2324.706, 'text': 'so again, this is actual data.', 'start': 2323.044, 'duration': 1.662}], 'summary': 'Model performance is indicated by low root mean square error, showing close match between actual and predicted values.', 'duration': 35.198, 'max_score': 2289.508, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2289508.jpg'}, {'end': 2462.5, 'src': 'embed', 'start': 2433.168, 'weight': 4, 'content': [{'end': 2435.488, 'text': 'so that is what we will see here now.', 'start': 2433.168, 'duration': 2.32}, {'end': 2436.849, 'text': 'so how do we communicate?', 'start': 2435.488, 'duration': 1.361}, {'end': 2447.872, 'text': 'usually you take these results and then either prepare a presentation or put it in a document and then show them these actionable results or actionable insights,', 'start': 2436.849, 'duration': 11.023}, {'end': 2455.676, 'text': 'and you need to find out who are your target audience and put all the results in context.', 'start': 2448.292, 'duration': 7.384}, {'end': 2462.5, 'text': 'and maybe, if there was a problem statement, you need to put this results in the context of the problem statement.', 'start': 2455.676, 'duration': 6.824}], 'summary': 'Communicate actionable insights to target audience with context and problem statement.', 'duration': 29.332, 'max_score': 2433.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2433168.jpg'}, {'end': 2552.359, 'src': 'embed', 'start': 2521.864, 'weight': 7, 'content': [{'end': 2524.246, 'text': 'there can be various factors which can cause that.', 'start': 2521.864, 'duration': 2.382}, {'end': 2529.468, 'text': 'So from time to time we need to check whether the model is performing well or not.', 'start': 2524.366, 'duration': 5.102}, {'end': 2532.009, 'text': 'The accuracy needs to be tested once in a while.', 'start': 2529.548, 'duration': 2.461}, {'end': 2536.151, 'text': 'And if required, you may have to rebuild or retrain the model.', 'start': 2532.149, 'duration': 4.002}, {'end': 2542.674, 'text': 'So you do the assessment, you see if it needs any tweaks, changes, and then, if it is required,', 'start': 2536.291, 'duration': 6.383}, {'end': 2549.377, 'text': 'you need to probably retrain the model with the latest data that you have, and then you deploy it.', 'start': 2542.674, 'duration': 6.703}, {'end': 2552.359, 'text': 'you build the model, train it and then you deploy it.', 'start': 2549.377, 'duration': 2.982}], 'summary': 'Regularly check model accuracy, retrain if needed, then deploy.', 'duration': 30.495, 'max_score': 2521.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2521864.jpg'}, {'end': 2586.847, 'src': 'embed', 'start': 2561.085, 'weight': 6, 'content': [{'end': 2568.472, 'text': 'So what did we learn in this tutorial? We talked about what is data science and who is a data scientist.', 'start': 2561.085, 'duration': 7.387}, {'end': 2574.799, 'text': 'Then we talked about what a data scientist performs or does, a day in the life of a data scientist.', 'start': 2568.672, 'duration': 6.127}, {'end': 2580.845, 'text': 'Some of the activities or the methodologies like the processes, data acquisition, data preparation.', 'start': 2574.879, 'duration': 5.966}, {'end': 2583.966, 'text': 'mining and model building and so on.', 'start': 2581.565, 'duration': 2.401}, {'end': 2586.847, 'text': 'and then, last but not least, the model maintenance,', 'start': 2583.966, 'duration': 2.881}], 'summary': 'Tutorial covers data science, data scientist roles, activities, and model maintenance.', 'duration': 25.762, 'max_score': 2561.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2561085.jpg'}], 'start': 2067.777, 'title': 'Data science and machine learning', 'summary': 'Covers data preparation and machine learning with scikit-learn, demonstrating linear regression model performance with low rmse and accurate predictions, and emphasizes communication of data science results and model maintenance.', 'chapters': [{'end': 2107.71, 'start': 2067.777, 'title': 'Data preparation and machine learning with scikit-learn', 'summary': 'Discusses the data preparation process, including no need for data cleaning due to clean data, removal of unwanted columns, and exploratory data analysis, before moving on to perform machine learning using the scikit-learn python library.', 'duration': 39.933, 'highlights': ['Data preparation involved no data cleaning as the data was clean, and focused on removing unwanted columns and performing exploratory data analysis.', 'The chapter emphasizes the use of scikit-learn, a Python library, for machine learning activities.', 'The process involved no major impact on the analysis due to clean data.']}, {'end': 2410.378, 'start': 2107.89, 'title': 'Linear regression model performance', 'summary': 'Demonstrates the process of training and evaluating a linear regression model, achieving a low root mean square error, and obtaining coefficients for various independent variables, resulting in a highly accurate model with almost matching predicted and actual values.', 'duration': 302.488, 'highlights': ['The model achieved a low root mean square error, indicating high performance, with coefficients for independent variables such as economy, family, and health being determined. The root mean square error is significantly low, demonstrating high model performance, while coefficients for independent variables such as economy, family, and health are identified.', 'The process involved training the model using 80% of the data as training data and 20% as test data, followed by predicting values for the test data and comparing predicted and actual values to assess error. The model is trained using 80% of the data as training data and 20% as test data, then predicts values for the test data and compares them with actual values to determine error.', "The model's performance is evaluated by plotting the actual values against predicted values, showing a very low difference and high accuracy, although real-life scenarios may exhibit more scattered values and higher error. A plot of the actual values against predicted values illustrates very low differences and high accuracy, acknowledging that real-life scenarios may have more scattered values and higher error."]}, {'end': 2632.459, 'start': 2410.378, 'title': 'Data science communication and model maintenance', 'summary': 'Discusses the communication of data science results to stakeholders, emphasizing the importance of contextualizing actionable insights and maintaining machine learning models through periodic assessment, potential retraining, and deployment.', 'duration': 222.081, 'highlights': ['The chapter emphasizes the importance of contextualizing actionable insights and communicating results to appropriate stakeholders in business terms. N/A', 'It discusses the necessity of maintaining machine learning models through periodic assessment, potential retraining, and deployment to ensure continued accuracy and relevance. N/A', 'The chapter also covers the activities and methodologies performed by a data scientist, including data acquisition, data preparation, mining, and model building. N/A']}], 'duration': 564.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/jNeUBWrrRsQ/pics/jNeUBWrrRsQ2067777.jpg', 'highlights': ['The model achieved a low root mean square error, indicating high performance, with coefficients for independent variables such as economy, family, and health being determined.', 'The process involved training the model using 80% of the data as training data and 20% as test data, followed by predicting values for the test data and comparing predicted and actual values to assess error.', "The model's performance is evaluated by plotting the actual values against predicted values, showing a very low difference and high accuracy, although real-life scenarios may exhibit more scattered values and higher error.", 'The chapter emphasizes the use of scikit-learn, a Python library, for machine learning activities.', 'The chapter emphasizes the importance of contextualizing actionable insights and communicating results to appropriate stakeholders in business terms.', 'The process involved no major impact on the analysis due to clean data.', 'The chapter also covers the activities and methodologies performed by a data scientist, including data acquisition, data preparation, mining, and model building.', 'It discusses the necessity of maintaining machine learning models through periodic assessment, potential retraining, and deployment to ensure continued accuracy and relevance.']}], 'highlights': ['Data science involves applying methodologies, algorithms, and creativity to extract actionable insights from available data.', 'Data science is the study of using data to extract actionable insights or knowledge, using methodologies, algorithms, and business domain knowledge, and can be used for fraud detection or prevention.', 'Fraud detection is a common application of data science, using machine learning algorithms such as outlier and clustering techniques.', 'The chapter covers the skills of a data scientist, the methodology used for data science, and an example program demonstrating data science activity.', "Data preparation stage consumes 60-70% of a data scientist's time, covering activities like data cleaning, transformation, handling outliers, data integrity, and data reduction.", 'ETL process involves extracting, transforming, and loading data into a data warehouse, also referred to as ETL, where data from different sources is brought together for data science activities like reporting, data mining, and statistical analysis.', 'Data acquisition involves collecting raw data from multiple sources and in various formats, which is then planned and prepared for data mining or modeling activities.', 'Data mining includes exploratory activities and may involve building and testing machine learning models to gain insights and deploy them for use.', 'Model maintenance is necessary to tweak the model over time due to changes in processes or data, ensuring its continued accuracy and relevance.', 'Tableau is used for data mining and exploratory analysis, available for trial download at tableau.com and with a free version called Tableau Public.', 'Analysis reveals that gender and geography significantly impact customer exit behavior, with females exhibiting a higher exit rate than males, and different geographies showing varying exit rates.', 'The presence of a credit card does not have a substantial impact on customer exit behavior, as the exit rates for customers with and without credit cards are similar.', 'Data mining can help in predicting future trends and identifying customer behavior patterns, enabling informed decision making.', 'Identification of fraudulent activity, selection of algorithms for advanced data mining.', 'Model building involves the selection of algorithms, creation of a model, supervised and unsupervised learning.', 'Algorithms used in supervised learning for categorical values include logistic regression, k-nearest neighbor, and support vector machine, while unsupervised learning involves algorithms like association analysis and hidden markov model.', 'Python used for linear regression analysis.', 'Importing essential libraries for data manipulation and machine learning.', 'Loading and combining data from multiple CSV files for analysis.', 'Simplifying computation by removing unnecessary columns.', 'Darker colored countries indicate higher happiness ranking.', 'Strong correlation between happiness rank and happiness score.', 'Happiness, economy, and family are highly correlated.', 'Freedom shows a decreasing correlation.', 'Splitting data into 80-20 for training and testing.', 'Creating a linear regression instance and training the model.', 'Evaluating the accuracy of the model and analyzing coefficients using scikit-learn.', 'Dropping columns with low correlation to prepare the data for analysis.', 'Exploratory analysis using describe function and correlation visualizations.', 'Correlation heatmap showing high correlation of happiness score with economy, health, family, and freedom.', 'Removal of unwanted columns such as region and standard error, streamlining the dataset for analysis and visualization.', 'Visualization depicting the tight inverse correlation between happiness rank and happiness score.', 'The model achieved a low root mean square error, indicating high performance, with coefficients for independent variables such as economy, family, and health being determined.', 'The process involved training the model using 80% of the data as training data and 20% as test data, followed by predicting values for the test data and comparing predicted and actual values to assess error.', "The model's performance is evaluated by plotting the actual values against predicted values, showing a very low difference and high accuracy, although real-life scenarios may exhibit more scattered values and higher error.", 'The chapter emphasizes the use of scikit-learn, a Python library, for machine learning activities.', 'The chapter emphasizes the importance of contextualizing actionable insights and communicating results to appropriate stakeholders in business terms.', 'The process involved no major impact on the analysis due to clean data.', 'The chapter also covers the activities and methodologies performed by a data scientist, including data acquisition, data preparation, mining, and model building.', 'It discusses the necessity of maintaining machine learning models through periodic assessment, potential retraining, and deployment to ensure continued accuracy and relevance.']}