title
What is Data Science? | Introduction to Data Science | Data Science for Beginners | Simplilearn

description
🔥 Caltech Post Graduate Program In Data Science: https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Descriptionff&utm_source=youtube 🔥IIT Kanpur Professional Certificate Course In Data Science (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-data-science?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/post-graduate-program-data-science?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Descriptionff&utm_source=youtube 🔥Data Scientist Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Descriptionff&utm_source=youtube This Data Science tutorial will help you in understanding what is Data Science, why we need Data Science, the prerequisites for learning Data Science, what a Data Scientist does, the Data Science lifecycle with an example, and career opportunities in the Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about. This Data Science tutorial for beginners will cover the following topics: Start (0:00) 1. Need for Data Science? ( 00:50 ) 2. What is Data Science? ( 05:55 ) 3. Data Science vs Business intelligence ( 11:44 ) 4. Prerequisites for learning Data Science ( 16:36 ) 5. What does a Data scientist do? ( 24:31 ) 6. Data Science life cycle with use case ( 30:17 ) 7. Demand for Data scientists ( 47:17 ) To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 📚Data Science Article: https://bit.ly/30cYbpI Download the Data Science career guide to explore and step into the exciting world of data, and follow the path towards your dream career: https://www.simplilearn.com/data-science-career-guide-pdf?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=description&utm_source=youtube You can also go through the Slide here: https://goo.gl/3d2pNv Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Description&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceForBeginners #WhatIsDataScience #IntroductionToDataScience #DataScienceTutorial #DataScientist #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning ➡️ About Caltech Post Graduate Program In Data Science ✅ 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 - Masterclasses delivered by distinguished Caltech faculty and IBM experts - Caltech CTME Circle membership - Online convocation by Caltech CTME Program Director - IBM certificates for IBM courses - Access to hackathons and Ask Me Anything sessions from IBM - 25+ hands-on projects from the likes of Amazon, Walmart, Uber, and many more - Seamless access to integrated labs - Capstone projects in 3 domains - Simplilearn’s Career Assistance to help you get noticed by top hiring companies - 8X higher interaction in live online classes by industry experts ✅ 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/pgp-data-science-certification-bootcamp-program?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Description&utm_source=youtube 🔥 Enroll for FREE Data Science Course & Get your Completion Certificate: https://www.simplilearn.com/getting-started-data-science-with-python-skillup?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Description&utm_source=youtube Learn More at: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=What-is-Data-Science-KxryzSO1Fjs&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

detail
{'title': 'What is Data Science? | Introduction to Data Science | Data Science for Beginners | Simplilearn', 'heatmap': [{'end': 1136.463, 'start': 1073.288, 'weight': 0.71}, {'end': 1618.056, 'start': 1575.319, 'weight': 0.71}, {'end': 1883.199, 'start': 1787.167, 'weight': 0.75}], 'summary': 'Introduces data science, its applications in various industries like airlines, logistics, cricket analytics, and project lifecycle, emphasizing the demand for data scientists and essential skills. it also demonstrates the impact of data science in reducing flight delays, optimizing transport, and predictive maintenance, with examples projected to minimize car accidents and reduce deaths by 2 million annually.', 'chapters': [{'end': 127.971, 'segs': [{'end': 127.971, 'src': 'embed', 'start': 79.591, 'weight': 0, 'content': [{'end': 81.153, 'text': 'right turn or slow down.', 'start': 79.591, 'duration': 1.562}, {'end': 86.135, 'text': 'So all these decisions are basically a part of data science.', 'start': 81.633, 'duration': 4.502}, {'end': 93.217, 'text': 'And there is a study that says that self-driving cars will minimize accidents.', 'start': 86.935, 'duration': 6.282}, {'end': 100.5, 'text': 'And in fact, it will root out more than 2 million deaths caused by car accidents annually.', 'start': 93.537, 'duration': 6.963}, {'end': 106.262, 'text': "Self-driving cars, right now, there's a lot of research and there is a lot of testing going on.", 'start': 100.76, 'duration': 5.502}, {'end': 114.285, 'text': "Not a lot of cars are yet in production in terms of usage, but it's going to happen.", 'start': 106.822, 'duration': 7.463}, {'end': 120.068, 'text': 'Every automotive company worth its name is investing in self-driving cars.', 'start': 114.666, 'duration': 5.402}, {'end': 127.971, 'text': 'So in about 10 to 15 years, some of the studies say that most of the cars will be autonomous or self-driving cars.', 'start': 120.548, 'duration': 7.423}], 'summary': 'Self-driving cars to reduce 2 million annual deaths in 10-15 years.', 'duration': 48.38, 'max_score': 79.591, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs79591.jpg'}], 'start': 3.697, 'title': 'Introduction to data science', 'summary': 'Introduces data science, its importance, the activities performed by a data scientist, and the increasing demand for data scientists, with an example of the use of data science in self-driving cars projected to minimize car accidents and reduce deaths by 2 million annually.', 'chapters': [{'end': 127.971, 'start': 3.697, 'title': 'Introduction to data science', 'summary': 'Introduces data science, its importance, the activities performed by a data scientist, and the increasing demand for data scientists, with an example of the use of data science in self-driving cars which is projected to minimize car accidents and reduce deaths by 2 million annually.', 'duration': 124.274, 'highlights': ['Self-driving cars are projected to minimize accidents and reduce deaths by 2 million annually, and it is estimated that in 10 to 15 years, most cars will be autonomous.', 'The session covers the need for data science, definitions, differences between data science and business intelligence, prerequisites for learning data science, the activities performed by a data scientist, and the data science lifecycle with a quick example.', 'Automotive companies are investing in self-driving cars, and it is mentioned that not many cars are currently in production for usage, but it is projected to happen in the future.']}], 'duration': 124.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs3697.jpg', 'highlights': ['Self-driving cars projected to minimize accidents and reduce deaths by 2 million annually', 'In 10 to 15 years, most cars will be autonomous', 'Automotive companies investing in self-driving cars']}, {'end': 467.371, 'segs': [{'end': 280.986, 'src': 'embed', 'start': 225.527, 'weight': 0, 'content': [{'end': 232.092, 'text': 'so that some flights can be rescheduled ahead of time and there are no last minute changes.', 'start': 225.527, 'duration': 6.565}, {'end': 236.237, 'text': 'Data science can also be used to make promotional offers.', 'start': 232.532, 'duration': 3.705}, {'end': 241.625, 'text': 'And the last but not least, is what kind of planes should be used,', 'start': 237.079, 'duration': 4.546}, {'end': 246.372, 'text': 'or the different classes of planes that should be used in different routes for better performance.', 'start': 241.625, 'duration': 4.747}, {'end': 251.655, 'text': 'So these are some examples of how data science can be used in airlines.', 'start': 246.612, 'duration': 5.043}, {'end': 257.877, 'text': 'And another example or another industry where data science can be used and benefited would be in logistics.', 'start': 251.715, 'duration': 6.162}, {'end': 269.68, 'text': 'So companies like FedEx, they use data science models to increase their efficiency drastically to optimize the routes and cut costs and so on.', 'start': 258.317, 'duration': 11.363}, {'end': 280.986, 'text': 'So, before their delivery truck actually sets out, they determine which is the best possible route to ship their items to the customers and,', 'start': 270.001, 'duration': 10.985}], 'summary': 'Data science optimizes flight schedules, offers, and logistics for airlines and companies like fedex.', 'duration': 55.459, 'max_score': 225.527, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs225527.jpg'}], 'start': 128.351, 'title': 'Data science in airlines and logistics', 'summary': 'Discusses the significant impact of data science on the airlines industry and logistics, including reducing flight delays, improving route planning, making promotional offers, and optimizing routes and modes of transport for increased efficiency and cost reduction.', 'chapters': [{'end': 467.371, 'start': 128.351, 'title': 'Data science in airlines and logistics', 'summary': 'Discusses the significant impact of data science on the airlines industry, including reducing flight delays, improving route planning, and making promotional offers, as well as its role in logistics for companies like fedex to optimize routes and determine the best mode of transport, ultimately leading to increased efficiency and cost reduction.', 'duration': 339.02, 'highlights': ["Data science's impact on reducing flight delays, improving route planning, and making promotional offers in the airlines industry. Data science can help in better route planning to reduce cancellations and frustrated passengers, predict delays for rescheduling, and make promotional offers, ultimately avoiding problems and reducing pain for airlines and passengers.", "Data science's role in logistics for companies like FedEx to optimize routes and determine the best mode of transport, leading to increased efficiency and cost reduction. Companies like FedEx use data science to optimize routes, predict the best time for delivery, and determine the best mode of transport, resulting in increased efficiency and cost reduction."]}], 'duration': 339.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs128351.jpg', 'highlights': ["Data science's impact on reducing flight delays, improving route planning, and making promotional offers in the airlines industry.", "Data science's role in logistics for companies like FedEx to optimize routes and determine the best mode of transport, leading to increased efficiency and cost reduction."]}, {'end': 1466.43, 'segs': [{'end': 496.095, 'src': 'embed', 'start': 467.371, 'weight': 0, 'content': [{'end': 473.475, 'text': "you probably don't follow this always exactly the same way, but just to illustrate, drive home the point,", 'start': 467.371, 'duration': 6.104}, {'end': 478.963, 'text': 'So we can answer a lot of questions using data science.', 'start': 473.979, 'duration': 4.984}, {'end': 482.165, 'text': 'For example, when we take a cab?', 'start': 479.303, 'duration': 2.862}, {'end': 486.768, 'text': 'when we book a cab now to go from location A to location B,', 'start': 482.165, 'duration': 4.603}, {'end': 494.313, 'text': 'what is the best route that the cab can take to reach in the fastest way or in the least amount of time?', 'start': 486.768, 'duration': 7.545}, {'end': 496.095, 'text': 'There could be several factors.', 'start': 494.774, 'duration': 1.321}], 'summary': 'Using data science, we can optimize cab routes for fastest travel time.', 'duration': 28.724, 'max_score': 467.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs467371.jpg'}, {'end': 540.898, 'src': 'embed', 'start': 514.863, 'weight': 1, 'content': [{'end': 525.086, 'text': 'they have to perform this analysis to find out what kind of shows people are viewing, what kind of shows people are liking, and so on and so forth.', 'start': 514.863, 'duration': 10.223}, {'end': 532.211, 'text': 'so that they can then sell this information to advertisers because their main source of revenue is advertising.', 'start': 525.626, 'duration': 6.585}, {'end': 536.535, 'text': 'So this is again, major function of data science.', 'start': 532.712, 'duration': 3.823}, {'end': 537.876, 'text': 'Predictive maintenance.', 'start': 536.955, 'duration': 0.921}, {'end': 539.577, 'text': 'We need to find out.', 'start': 538.396, 'duration': 1.181}, {'end': 540.898, 'text': 'will my car break down?', 'start': 539.577, 'duration': 1.321}], 'summary': 'Data analysis for show preferences; revenue from advertising; predictive maintenance for cars', 'duration': 26.035, 'max_score': 514.863, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs514863.jpg'}, {'end': 586.052, 'src': 'embed', 'start': 562.553, 'weight': 4, 'content': [{'end': 572.979, 'text': 'nowadays everybody is applying data science in elections and trying to capture the votes, or rather the voters, and influence the voters.', 'start': 562.553, 'duration': 10.426}, {'end': 578.544, 'text': 'personalized messages providing personalized messages, and so on and so forth.', 'start': 573.539, 'duration': 5.005}, {'end': 586.052, 'text': 'And that is one not only that people use data science to even predict who is going to win the elections.', 'start': 578.905, 'duration': 7.147}], 'summary': 'Data science is used to influence voters with personalized messages and predict election outcomes.', 'duration': 23.499, 'max_score': 562.553, 'thumbnail': ''}, {'end': 849.166, 'src': 'embed', 'start': 825.524, 'weight': 5, 'content': [{'end': 834.97, 'text': 'Pretty much it was structured data and it had reports and dashboards that was pretty much what was there in business intelligence.', 'start': 825.524, 'duration': 9.446}, {'end': 840.876, 'text': 'Now with data science in addition to data, we also use a lot of unstructured data.', 'start': 835.01, 'duration': 5.866}, {'end': 843.059, 'text': 'Example, web logs or comments.', 'start': 840.936, 'duration': 2.123}, {'end': 846.443, 'text': 'If we are talking about customer feedback, there is a structured part.', 'start': 843.259, 'duration': 3.184}, {'end': 849.166, 'text': 'There is an unstructured part where people write free text.', 'start': 846.463, 'duration': 2.703}], 'summary': 'Data science involves structured and unstructured data, including web logs and customer feedback.', 'duration': 23.642, 'max_score': 825.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs825524.jpg'}, {'end': 894.076, 'src': 'embed', 'start': 873.274, 'weight': 6, 'content': [{'end': 883.125, 'text': 'We go deeper in terms of finding why a certain behavior has occurred and also go beyond just providing a report.', 'start': 873.274, 'duration': 9.851}, {'end': 886.349, 'text': 'There is a deeper statistical analysis that is done.', 'start': 883.245, 'duration': 3.104}, {'end': 888.131, 'text': 'That is what is the scientific part.', 'start': 886.409, 'duration': 1.722}, {'end': 892.355, 'text': 'and deeper insights are gathered, not just reporting.', 'start': 888.691, 'duration': 3.664}, {'end': 894.076, 'text': "So that's from a method perspective.", 'start': 892.575, 'duration': 1.501}], 'summary': 'Deeper statistical analysis conducted, providing scientific insights beyond reporting.', 'duration': 20.802, 'max_score': 873.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs873274.jpg'}, {'end': 985.272, 'src': 'embed', 'start': 955.151, 'weight': 8, 'content': [{'end': 966.579, 'text': 'In data science you take historical data, but you also combine that with maybe some other required information and you also try to predict the future.', 'start': 955.151, 'duration': 11.428}, {'end': 972.584, 'text': 'So we try to extrapolate, maybe the sales and say okay, sales as of now.', 'start': 966.88, 'duration': 5.704}, {'end': 973.465, 'text': 'as of today.', 'start': 972.584, 'duration': 0.881}, {'end': 985.272, 'text': "this is, the sales is 5 million and If we, based on the historical information, we see that sales increase on a maybe I don't know monthly basis, 10%.", 'start': 973.465, 'duration': 11.807}], 'summary': 'Data science predicts future sales based on historical data, projecting 10% monthly increase.', 'duration': 30.121, 'max_score': 955.151, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs955151.jpg'}, {'end': 1116.955, 'src': 'embed', 'start': 1073.288, 'weight': 7, 'content': [{'end': 1082.334, 'text': 'Maybe technically you may be a genius, but then if you are unable to communicate those results in a proper way, once again, that will not help.', 'start': 1073.288, 'duration': 9.046}, {'end': 1087.257, 'text': 'So these are the three main traits, curiosity, common sense, and communication skills.', 'start': 1082.614, 'duration': 4.643}, {'end': 1089.198, 'text': 'In a way, you can say these are the three Cs.', 'start': 1087.317, 'duration': 1.881}, {'end': 1094.601, 'text': 'Okay, so what are the other prerequisites? First one, so machine learning.', 'start': 1089.618, 'duration': 4.983}, {'end': 1097.503, 'text': 'Machine learning is the backbone of data science.', 'start': 1094.801, 'duration': 2.702}, {'end': 1103.248, 'text': 'Data science involves quite a bit of machine learning in addition to the basic statistics that we do.', 'start': 1098.044, 'duration': 5.204}, {'end': 1109.993, 'text': 'So a data scientist needs to have a good hang or need to be very good at data science.', 'start': 1103.608, 'duration': 6.385}, {'end': 1112.314, 'text': 'The second part is modeling.', 'start': 1110.313, 'duration': 2.001}, {'end': 1116.955, 'text': 'So modeling is also a part of machine learning in a way,', 'start': 1112.534, 'duration': 4.421}], 'summary': 'Data scientists need curiosity, common sense, and communication skills. machine learning is the backbone of data science.', 'duration': 43.667, 'max_score': 1073.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1073288.jpg'}, {'end': 1136.463, 'src': 'heatmap', 'start': 1073.288, 'weight': 0.71, 'content': [{'end': 1082.334, 'text': 'Maybe technically you may be a genius, but then if you are unable to communicate those results in a proper way, once again, that will not help.', 'start': 1073.288, 'duration': 9.046}, {'end': 1087.257, 'text': 'So these are the three main traits, curiosity, common sense, and communication skills.', 'start': 1082.614, 'duration': 4.643}, {'end': 1089.198, 'text': 'In a way, you can say these are the three Cs.', 'start': 1087.317, 'duration': 1.881}, {'end': 1094.601, 'text': 'Okay, so what are the other prerequisites? First one, so machine learning.', 'start': 1089.618, 'duration': 4.983}, {'end': 1097.503, 'text': 'Machine learning is the backbone of data science.', 'start': 1094.801, 'duration': 2.702}, {'end': 1103.248, 'text': 'Data science involves quite a bit of machine learning in addition to the basic statistics that we do.', 'start': 1098.044, 'duration': 5.204}, {'end': 1109.993, 'text': 'So a data scientist needs to have a good hang or need to be very good at data science.', 'start': 1103.608, 'duration': 6.385}, {'end': 1112.314, 'text': 'The second part is modeling.', 'start': 1110.313, 'duration': 2.001}, {'end': 1116.955, 'text': 'So modeling is also a part of machine learning in a way,', 'start': 1112.534, 'duration': 4.421}, {'end': 1123.578, 'text': 'but you need to be good at identifying what are the algorithms that are most suitable to solve a given problem.', 'start': 1116.955, 'duration': 6.623}, {'end': 1129.18, 'text': 'What models can we use and how do we train these models and so on and so forth.', 'start': 1123.818, 'duration': 5.362}, {'end': 1131.24, 'text': 'So that is the second component.', 'start': 1129.24, 'duration': 2}, {'end': 1132.601, 'text': 'Then statistics.', 'start': 1131.36, 'duration': 1.241}, {'end': 1136.463, 'text': 'Statistics is like the core foundation of data science.', 'start': 1132.961, 'duration': 3.502}], 'summary': 'Data science requires 3 main traits - curiosity, common sense, and communication skills. also, machine learning, modeling, and statistics are essential prerequisites.', 'duration': 63.175, 'max_score': 1073.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1073288.jpg'}, {'end': 1186.023, 'src': 'embed', 'start': 1160.499, 'weight': 11, 'content': [{'end': 1167.766, 'text': 'python, especially, is becoming a very popular programming language in data science because of its ease of learning,', 'start': 1160.499, 'duration': 7.267}, {'end': 1176.114, 'text': 'because of the multiple libraries that it supports for performing data science and machine learning, and so on.', 'start': 1167.766, 'duration': 8.348}, {'end': 1181.479, 'text': 'so python is by far one of the most popular languages in data science.', 'start': 1176.114, 'duration': 5.365}, {'end': 1186.023, 'text': 'if any one of you is is wanting to learn a new language, That should be Python.', 'start': 1181.479, 'duration': 4.544}], 'summary': 'Python is the most popular language in data science due to its ease of learning and multiple libraries for data science and machine learning.', 'duration': 25.524, 'max_score': 1160.499, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1160499.jpg'}, {'end': 1243.064, 'src': 'embed', 'start': 1212.593, 'weight': 12, 'content': [{'end': 1217.055, 'text': 'And from a skills perspective, in addition to some of the programming languages,', 'start': 1212.593, 'duration': 4.462}, {'end': 1222.037, 'text': 'it would help if you have a good knowledge or good understanding of statistics.', 'start': 1217.055, 'duration': 4.982}, {'end': 1228.2, 'text': 'And what are the tools that are used in data analysis? SAS is one of the most popular tools.', 'start': 1222.317, 'duration': 5.883}, {'end': 1230.121, 'text': "It's been there for a very long time.", 'start': 1228.54, 'duration': 1.581}, {'end': 1233.162, 'text': "And that's the reason it is very popular.", 'start': 1230.681, 'duration': 2.481}, {'end': 1237.243, 'text': 'And however, this is compared to most of the other tools.', 'start': 1233.602, 'duration': 3.641}, {'end': 1243.064, 'text': 'It is a proprietary software, whereas Python and R are mostly open source.', 'start': 1237.323, 'duration': 5.741}], 'summary': 'Data analysts need programming skills; sas, python, and r are popular tools.', 'duration': 30.471, 'max_score': 1212.593, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1212593.jpg'}], 'start': 467.371, 'title': 'Data science applications and skills', 'summary': 'Discusses the diverse applications of data science in optimizing routes for cab rides, suggesting tv shows on platforms like netflix, analyzing viewership for advertising, predictive maintenance for appliances, and politics. it also emphasizes the essential skills and tools in data science, including machine learning, statistics, programming, and data visualization, with an emphasis on python, r, sas, jupyter, rstudio, hadoop, spark, and tableau.', 'chapters': [{'end': 514.863, 'start': 467.371, 'title': 'Data science applications', 'summary': 'Discusses the applications of data science in optimizing routes for cab rides and suggesting tv shows on platforms like netflix, highlighting the use of data to make decisions in various scenarios.', 'duration': 47.492, 'highlights': ['Data science is used to optimize cab routes by considering factors such as traffic, road conditions, and weather to determine the best route for reaching a destination in the fastest way or least amount of time.', 'Data science is employed by platforms like Netflix to suggest TV shows based on user preferences and viewing patterns, demonstrating the use of data for decision-making in entertainment.', 'The application of data science in optimizing cab routes and suggesting TV shows showcases the diverse use of data for decision-making in different fields.']}, {'end': 703.119, 'start': 514.863, 'title': 'Applications of data science', 'summary': 'Discusses the applications of data science in analyzing viewership for advertising, predictive maintenance for appliances, and politics, along with the key steps in the data science process.', 'duration': 188.256, 'highlights': ['Data science applied in analyzing viewership for advertising Data science is used to analyze viewership and preferences to sell information to advertisers, a key source of revenue.', 'Predictive maintenance for appliances using data science Data science is applied to predict breakdowns in appliances like cars and refrigerators, aiding in making purchase decisions and maintenance planning.', 'Application of data science in politics Data science is utilized in political campaigns to capture and influence voters through personalized messages and predictions of election outcomes.', 'Key steps in the data science process The process involves asking the right questions, exploring the data, modeling with machine learning algorithms, and communicating the results effectively.']}, {'end': 1094.601, 'start': 703.635, 'title': 'Bi vs. data science', 'summary': 'Compares business intelligence with data science, highlighting differences in data source, methods, skills, and focus, emphasizing the shift from historical reporting to predicting the future, and outlining essential traits for a data scientist.', 'duration': 390.966, 'highlights': ["Data science combines structured and unstructured data for analysis, including web logs and customer feedback, while business intelligence primarily uses structured data from enterprise applications like ERP and CRM. Data science incorporates unstructured data such as web logs and customer feedback, providing a more comprehensive data source compared to business intelligence's reliance on primarily structured data from enterprise applications like ERP and CRM.", 'Data science delves deeper into statistical analysis and provides insights beyond historical reporting, while business intelligence focuses on presenting the truth and historical data. Data science involves deeper statistical analysis and insights beyond historical reporting, in contrast to business intelligence, which primarily focuses on presenting historical data without delving into deeper statistical analysis.', "Data science requires a broader skill set, including advanced statistics and machine learning, compared to business intelligence's emphasis on visualization and basic statistics. Data science necessitates a broader skill set encompassing advanced statistics and machine learning, distinguishing it from business intelligence, which focuses more on visualization and basic statistics.", "The focus of data science extends to predicting the future by combining historical data with additional information, while business intelligence primarily focuses on historical data for reporting. Data science's focus extends to predicting the future by combining historical data with additional information, whereas business intelligence primarily focuses on historical data for reporting purposes.", 'Essential traits for a data scientist include curiosity, common sense, and communication skills, emphasizing the importance of asking the right questions, utilizing common sense in data analysis, and effective communication of results. Essential traits for a data scientist comprise curiosity, common sense, and communication skills, underlining the significance of asking the right questions, applying common sense in data analysis, and effectively communicating the results.']}, {'end': 1466.43, 'start': 1094.801, 'title': 'Skills and tools in data science', 'summary': 'Emphasizes the importance of machine learning, statistics, programming, and data visualization in data science, highlighting the significance of python, r, and various tools such as sas, jupyter, rstudio, hadoop, spark, and tableau.', 'duration': 371.629, 'highlights': ['The significance of machine learning, statistics, programming, and data visualization in data science. Data science involves machine learning, statistics, programming, and data visualization, which are essential components for success in the field.', 'The importance of Python and R in data science due to their popularity and extensive libraries for data science and machine learning. Python and R are highly popular in data science due to their extensive libraries and ease of learning, making them essential languages for data science.', 'The relevance of tools such as SAS, Jupyter, RStudio, Hadoop, Spark, and Tableau in data science for analysis, visualization, and machine learning. Tools like SAS, Jupyter, RStudio, Hadoop, Spark, and Tableau are crucial for data analysis, visualization, and machine learning in the field of data science.']}], 'duration': 999.059, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs467371.jpg', 'highlights': ['Data science optimizes cab routes considering traffic, road conditions, and weather.', 'Platforms like Netflix use data science to suggest TV shows based on user preferences.', 'Data science analyzes viewership for advertising, aiding in revenue generation.', 'Predictive maintenance for appliances is enabled through data science.', 'Data science influences political campaigns through personalized messages and predictions.', 'Data science combines structured and unstructured data for comprehensive analysis.', 'Data science delves deeper into statistical analysis and provides insights beyond historical reporting.', 'Data science requires a broader skill set, including advanced statistics and machine learning.', 'Data science focuses on predicting the future by combining historical data with additional information.', 'Essential traits for a data scientist include curiosity, common sense, and communication skills.', 'Data science involves machine learning, statistics, programming, and data visualization.', 'Python and R are highly popular in data science due to their extensive libraries and ease of learning.', 'Tools like SAS, Jupyter, RStudio, Hadoop, Spark, and Tableau are crucial for data analysis.']}, {'end': 1801.217, 'segs': [{'end': 1563.936, 'src': 'embed', 'start': 1544.545, 'weight': 3, 'content': [{'end': 1555.212, 'text': 'And then he puts these output in a proper format for presenting it to the stakeholders and communicating those insights or the results to the stakeholders.', 'start': 1544.545, 'duration': 10.667}, {'end': 1562.056, 'text': 'So this is a very high level view of like a day in the life of a data scientist.', 'start': 1555.292, 'duration': 6.764}, {'end': 1563.936, 'text': 'So gathering data, raw data,', 'start': 1562.096, 'duration': 1.84}], 'summary': 'Data scientist presents insights to stakeholders, handles raw data. high-level overview.', 'duration': 19.391, 'max_score': 1544.545, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1544545.jpg'}, {'end': 1618.056, 'src': 'heatmap', 'start': 1575.319, 'weight': 0.71, 'content': [{'end': 1585.161, 'text': 'feeding this into that analysis system that has been designed, be it mathematical models, machine learning models, and then get the results,', 'start': 1575.319, 'duration': 9.842}, {'end': 1590.642, 'text': 'the insights, and then present it in a nice way so that the stakeholders can understand.', 'start': 1585.161, 'duration': 5.481}, {'end': 1599.066, 'text': "How about machine learning algorithms? So let's see what are the various machine learning algorithms that would be required for a data scientist.", 'start': 1590.962, 'duration': 8.104}, {'end': 1602.348, 'text': 'So these are a few of the algorithms.', 'start': 1599.546, 'duration': 2.802}, {'end': 1604.529, 'text': 'Again, this is not an exhaustive list.', 'start': 1602.808, 'duration': 1.721}, {'end': 1611.292, 'text': 'We have regression is one of the supervised learning models or techniques.', 'start': 1605.57, 'duration': 5.722}, {'end': 1618.056, 'text': "So in case of regression, you try to, let's say, come up with a continuous number.", 'start': 1611.633, 'duration': 6.423}], 'summary': 'Utilize analysis system to apply machine learning algorithms for data insights.', 'duration': 42.737, 'max_score': 1575.319, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1575319.jpg'}, {'end': 1618.056, 'src': 'embed', 'start': 1590.962, 'weight': 2, 'content': [{'end': 1599.066, 'text': "How about machine learning algorithms? So let's see what are the various machine learning algorithms that would be required for a data scientist.", 'start': 1590.962, 'duration': 8.104}, {'end': 1602.348, 'text': 'So these are a few of the algorithms.', 'start': 1599.546, 'duration': 2.802}, {'end': 1604.529, 'text': 'Again, this is not an exhaustive list.', 'start': 1602.808, 'duration': 1.721}, {'end': 1611.292, 'text': 'We have regression is one of the supervised learning models or techniques.', 'start': 1605.57, 'duration': 5.722}, {'end': 1618.056, 'text': "So in case of regression, you try to, let's say, come up with a continuous number.", 'start': 1611.633, 'duration': 6.423}], 'summary': 'Overview of machine learning algorithms for data science.', 'duration': 27.094, 'max_score': 1590.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1590962.jpg'}, {'end': 1716.585, 'src': 'embed', 'start': 1685.884, 'weight': 0, 'content': [{'end': 1687.045, 'text': "So I'm talking about cricket.", 'start': 1685.884, 'duration': 1.161}, {'end': 1690.247, 'text': 'Hopefully, most of you are familiar with the game of cricket.', 'start': 1687.345, 'duration': 2.902}, {'end': 1691.628, 'text': 'So how do we find out?', 'start': 1690.367, 'duration': 1.261}, {'end': 1699.133, 'text': 'So then we put this into a clustering mechanism and then the system will say that okay, these are the people who are all,', 'start': 1691.668, 'duration': 7.465}, {'end': 1701.615, 'text': 'who have all scored good amount of runs.', 'start': 1699.133, 'duration': 2.482}, {'end': 1704.037, 'text': 'So they belong to one cluster.', 'start': 1702.035, 'duration': 2.002}, {'end': 1706.879, 'text': 'These are all the people who have taken good amount of wickets.', 'start': 1704.217, 'duration': 2.662}, {'end': 1708.56, 'text': 'So they belong to one cluster.', 'start': 1706.999, 'duration': 1.561}, {'end': 1714.364, 'text': 'And maybe here are some people who have taken good amount of wickets and they have made good amount of runs.', 'start': 1708.88, 'duration': 5.484}, {'end': 1716.585, 'text': 'So they may be belonging to one group.', 'start': 1714.444, 'duration': 2.141}], 'summary': 'Using clustering to identify cricket players by runs and wickets.', 'duration': 30.701, 'max_score': 1685.884, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1685884.jpg'}, {'end': 1801.217, 'src': 'embed', 'start': 1749.989, 'weight': 1, 'content': [{'end': 1755.211, 'text': 'Primarily, it can also be used for regression, but by and large, it is used for classification.', 'start': 1749.989, 'duration': 5.222}, {'end': 1764.254, 'text': "And here again, it's a very logical way in which the algorithm goes about classifying the various inputs.", 'start': 1755.751, 'duration': 8.503}, {'end': 1769.297, 'text': "One of the biggest advantages of decision tree is that it's very easy to understand,", 'start': 1764.515, 'duration': 4.782}, {'end': 1775.099, 'text': "and it's very easy to explain why a certain object has been classified in a certain way.", 'start': 1769.297, 'duration': 5.802}, {'end': 1782.384, 'text': 'compared to maybe some of the other mechanisms like say support vector machines or logistic regression and so on.', 'start': 1775.499, 'duration': 6.885}, {'end': 1786.887, 'text': "So that's the advantage of Dictionary but that is also very popular algorithm.", 'start': 1782.424, 'duration': 4.463}, {'end': 1793.772, 'text': 'Then we have support vector machines primarily for classification purpose and then we have Naive Bayes.', 'start': 1787.167, 'duration': 6.605}, {'end': 1801.217, 'text': 'This is again a statistical probability based classification So these are a few algorithms.', 'start': 1794.012, 'duration': 7.205}], 'summary': 'Decision tree is primarily used for classification, easy to understand and explain, compared to other algorithms like support vector machines and logistic regression.', 'duration': 51.228, 'max_score': 1749.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1749989.jpg'}], 'start': 1466.43, 'title': 'Data science and cricket analytics', 'summary': "Provides an overview of a data scientist's tasks, including gathering and processing raw data, using machine learning algorithms such as regression and clustering. it also discusses using clustering to group cricketers based on runs and wickets, and using decision tree and other algorithms for classification in cricket analytics.", 'chapters': [{'end': 1659.712, 'start': 1466.43, 'title': 'Life of a data scientist', 'summary': "Provides an overview of a data scientist's daily tasks, including gathering and processing raw data, using analytic systems such as machine learning algorithms, and presenting insights to stakeholders. it also covers the key machine learning algorithms like regression and clustering.", 'duration': 193.282, 'highlights': ["Data Scientist's Daily Tasks A data scientist's daily tasks include gathering and processing raw data, using analytic systems like machine learning algorithms, and presenting insights to stakeholders.", "Machine Learning Algorithms Key machine learning algorithms such as regression for continuous value prediction and clustering for unsupervised learning are essential for a data scientist's toolkit."]}, {'end': 1801.217, 'start': 1659.712, 'title': 'Clustering and classification in cricket data', 'summary': 'Discusses using clustering to group cricketers based on runs and wickets, then using decision tree and other algorithms for classification, highlighting the advantages of decision tree over other mechanisms.', 'duration': 141.505, 'highlights': ['Clustering is used to group cricketers based on runs and wickets, then labeled as batsmen, bowlers, or all-rounders, based on the clusters, providing a logical way of classification.', 'Decision tree is used for classification, known for its ease of understanding and explanation of classification, offering advantages over other mechanisms like support vector machines or logistic regression.', 'Support vector machines and Naive Bayes are also mentioned as popular algorithms for classification purposes.']}], 'duration': 334.787, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1466430.jpg', 'highlights': ['Clustering groups cricketers based on runs and wickets', 'Decision tree offers advantages over other classification mechanisms', 'Machine learning algorithms like regression and clustering are essential for data scientists', 'Data scientists daily tasks include gathering, processing raw data, and presenting insights', 'Support vector machines and Naive Bayes are popular algorithms for classification']}, {'end': 2449.594, 'segs': [{'end': 1850.302, 'src': 'embed', 'start': 1825.94, 'weight': 5, 'content': [{'end': 1835.309, 'text': 'it involves understanding the business problem, asking questions, get a good understanding of the business model, meet up with all the stakeholders,', 'start': 1825.94, 'duration': 9.369}, {'end': 1840.934, 'text': 'understand what kind of data is available and all that is a part of the first step.', 'start': 1835.309, 'duration': 5.625}, {'end': 1842.976, 'text': 'so here are a few examples.', 'start': 1840.934, 'duration': 2.042}, {'end': 1848.96, 'text': 'we want to see what are the various specifications and then what is the end goal?', 'start': 1842.976, 'duration': 5.984}, {'end': 1850.302, 'text': 'what is the budget?', 'start': 1848.96, 'duration': 1.342}], 'summary': 'First step in data analysis involves understanding business problem, stakeholders, available data, and specific goals and budget.', 'duration': 24.362, 'max_score': 1825.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1825940.jpg'}, {'end': 1905.35, 'src': 'embed', 'start': 1875.817, 'weight': 4, 'content': [{'end': 1883.199, 'text': 'Data gathering and data preparation also known as data munging or sometimes it is also known as data manipulation.', 'start': 1875.817, 'duration': 7.382}, {'end': 1892.083, 'text': 'So what happens here is the raw data that is available may not be usable in its current format for various reasons.', 'start': 1883.459, 'duration': 8.624}, {'end': 1897.186, 'text': 'So that is why in this step, a data scientist would explore the data.', 'start': 1892.184, 'duration': 5.002}, {'end': 1905.35, 'text': 'He will take a look at some sample data, maybe pick, there are millions of records, pick a few thousand records and see how the data is looking.', 'start': 1897.466, 'duration': 7.884}], 'summary': 'Data gathering and preparation involve exploring and manipulating raw data for usability, examining sample data to assess quality.', 'duration': 29.533, 'max_score': 1875.817, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1875817.jpg'}, {'end': 1968.752, 'src': 'embed', 'start': 1940.072, 'weight': 3, 'content': [{'end': 1943.495, 'text': 'There are several blank records or blank columns.', 'start': 1940.072, 'duration': 3.423}, {'end': 1949.72, 'text': "So if you use that data directly, you'll get errors or you will get inaccurate results.", 'start': 1943.855, 'duration': 5.865}, {'end': 1956.465, 'text': 'So how do you either get rid of the data or how do you fill those gaps with something meaningful?', 'start': 1950.02, 'duration': 6.445}, {'end': 1962.189, 'text': 'So all that is a part of data munging or data manipulation.', 'start': 1956.665, 'duration': 5.524}, {'end': 1966.131, 'text': 'So these are some additional sub topics within that.', 'start': 1962.39, 'duration': 3.741}, {'end': 1968.752, 'text': 'So data integration is one of them.', 'start': 1966.431, 'duration': 2.321}], 'summary': 'Data cleaning is crucial for accurate analysis; data munging and integration are key subtopics.', 'duration': 28.68, 'max_score': 1940.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1940072.jpg'}, {'end': 2111.609, 'src': 'embed', 'start': 2079.237, 'weight': 2, 'content': [{'end': 2083.02, 'text': 'Somebody would have tried out something and it worked and will continue to use that mechanism.', 'start': 2079.237, 'duration': 3.783}, {'end': 2086.262, 'text': "So that's how we need to take care of data cleaning.", 'start': 2083.199, 'duration': 3.063}, {'end': 2088.744, 'text': 'Now, what are the various ways of doing?', 'start': 2086.302, 'duration': 2.442}, {'end': 2091.565, 'text': 'you know, if values are missing, how do you take care of that?', 'start': 2088.744, 'duration': 2.821}, {'end': 2103.193, 'text': 'Now, if the data is too large and only a few records have some missing values, then it is okay to just get rid of those entire rows, for example.', 'start': 2091.706, 'duration': 11.487}, {'end': 2111.609, 'text': "So if you have a million records and out of which 100 records don't have full data, so there are some missing values in about 100 records.", 'start': 2103.253, 'duration': 8.356}], 'summary': 'Effective data cleaning methods are crucial for handling missing values, like discarding rows with missing values in large datasets.', 'duration': 32.372, 'max_score': 2079.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2079237.jpg'}, {'end': 2282.676, 'src': 'embed', 'start': 2252.979, 'weight': 1, 'content': [{'end': 2256.661, 'text': "So you need to decide what kind of models you're going to use.", 'start': 2252.979, 'duration': 3.682}, {'end': 2259.762, 'text': "Again, it depends on what is the problem you're trying to solve.", 'start': 2256.981, 'duration': 2.781}, {'end': 2265.964, 'text': 'If it is a regression problem, you need to think of a regression algorithm and come up with a regression model.', 'start': 2259.822, 'duration': 6.142}, {'end': 2267.525, 'text': 'So it could be linear regression.', 'start': 2266.104, 'duration': 1.421}, {'end': 2274.909, 'text': "Or if you're talking about classification, then you need to pick an appropriate classification algorithm,", 'start': 2267.885, 'duration': 7.024}, {'end': 2282.676, 'text': 'like logistic regression or decision tree or SVM, and then you need to train that particular model.', 'start': 2274.909, 'duration': 7.767}], 'summary': 'Choose appropriate model based on problem type: regression or classification', 'duration': 29.697, 'max_score': 2252.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2252979.jpg'}, {'end': 2391.487, 'src': 'embed', 'start': 2359.448, 'weight': 0, 'content': [{'end': 2363.933, 'text': 'You will also get an idea about what kind of model to be used and so on and so forth.', 'start': 2359.448, 'duration': 4.485}, {'end': 2367.316, 'text': 'What are the various techniques used for exploratory data analysis?', 'start': 2364.195, 'duration': 3.121}, {'end': 2371.178, 'text': 'Typically, these would be visualization techniques.', 'start': 2367.596, 'duration': 3.582}, {'end': 2376.24, 'text': 'like you use histograms, you can use box plots, you can use scatter plots.', 'start': 2371.178, 'duration': 5.062}, {'end': 2382.943, 'text': 'So these are very quick ways of identifying the patterns or a few of the trends of the data and so on.', 'start': 2376.44, 'duration': 6.503}, {'end': 2391.487, 'text': "And then once your data is ready, you decided on the model, what kind of model, what kind of algorithm you're going to use.", 'start': 2383.641, 'duration': 7.846}], 'summary': 'Exploratory data analysis involves visualization techniques like histograms, box plots, and scatter plots to identify patterns and trends in the data.', 'duration': 32.039, 'max_score': 2359.448, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2359448.jpg'}], 'start': 1801.258, 'title': 'Data science project lifecycle and model planning', 'summary': 'Covers the lifecycle of a data science project, including concept study, data preparation, and data munging, and discusses data cleaning techniques, model planning, and training for statistical and machine learning models, emphasizing exploratory data analysis and iterative model training.', 'chapters': [{'end': 2056.679, 'start': 1801.258, 'title': 'Data science project lifecycle', 'summary': 'Discusses the lifecycle of a data science project, encompassing the concept study, data preparation, and various subtopics within data munging such as data integration, data transformation, and data cleaning.', 'duration': 255.421, 'highlights': ['The concept study involves understanding the business problem, meeting stakeholders, and understanding available data, with examples like specifying end goals and budgets, and predicting the price of a 1.35 carat diamond.', 'Data preparation includes exploring raw data, handling gaps and structure issues, and addressing problems like redundant and mismatched data from multiple sources.', 'Data munging subtopics encompass data integration, resolving data redundancy, data transformation to ensure similarity, data reduction, and data cleaning to handle missing or improper values.']}, {'end': 2449.594, 'start': 2056.859, 'title': 'Data preparation and model planning', 'summary': 'Discusses data cleaning techniques, including handling missing values and data preparation for machine learning, and explains the process of model planning and training for statistical and machine learning models, emphasizing the importance of exploratory data analysis and iterative model training.', 'duration': 392.735, 'highlights': ['Explains data cleaning techniques, including handling missing values and data preparation for machine learning Discusses various methods of data cleaning and handling missing values, such as removing entire rows with missing values if the data size allows, or filling missing values with mean or median values, emphasizing the importance of accurate data for machine learning activities.', 'Emphasizes the process of model planning and training for statistical and machine learning models Describes the importance of selecting appropriate models based on the problem being solved, such as regression models for regression problems and classification algorithms for classification problems, and highlights the iterative process of model training and testing for ensuring accuracy before deployment.', 'Stresses the importance of exploratory data analysis in understanding the data and determining the appropriate model Highlights the significance of exploratory data analysis in understanding data types, identifying missing values, and determining the relationship between variables, emphasizing the use of visualization techniques like histograms and scatter plots for quick data analysis.']}], 'duration': 648.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs1801258.jpg', 'highlights': ['Stresses the importance of exploratory data analysis in understanding the data and determining the appropriate model Highlights the significance of exploratory data analysis in understanding data types, identifying missing values, and determining the relationship between variables, emphasizing the use of visualization techniques like histograms and scatter plots for quick data analysis.', 'Emphasizes the process of model planning and training for statistical and machine learning models Describes the importance of selecting appropriate models based on the problem being solved, such as regression models for regression problems and classification algorithms for classification problems, and highlights the iterative process of model training and testing for ensuring accuracy before deployment.', 'Explains data cleaning techniques, including handling missing values and data preparation for machine learning Discusses various methods of data cleaning and handling missing values, such as removing entire rows with missing values if the data size allows, or filling missing values with mean or median values, emphasizing the importance of accurate data for machine learning activities.', 'Data munging subtopics encompass data integration, resolving data redundancy, data transformation to ensure similarity, data reduction, and data cleaning to handle missing or improper values.', 'Data preparation includes exploring raw data, handling gaps and structure issues, and addressing problems like redundant and mismatched data from multiple sources.', 'The concept study involves understanding the business problem, meeting stakeholders, and understanding available data, with examples like specifying end goals and budgets, and predicting the price of a 1.35 carat diamond.']}, {'end': 2985.787, 'segs': [{'end': 2481.518, 'src': 'embed', 'start': 2449.795, 'weight': 1, 'content': [{'end': 2454.996, 'text': 'So what are the various tools that we use for model planning?', 'start': 2449.795, 'duration': 5.201}, {'end': 2457.977, 'text': 'R is an excellent tool in a lot of ways.', 'start': 2455.576, 'duration': 2.401}, {'end': 2463.658, 'text': "whether you're doing regular statistical analysis or machine learning, or any of these activities.", 'start': 2457.977, 'duration': 5.681}, {'end': 2471.247, 'text': 'R in, along with RStudio, provides a very powerful environment to do data analysis, including visualization.', 'start': 2463.658, 'duration': 7.589}, {'end': 2481.518, 'text': 'It has a very good integrated visualization or plot mechanism which can be used for doing exploratory data analysis and then later on to do analysis,', 'start': 2471.347, 'duration': 10.171}], 'summary': 'R and rstudio are powerful tools for data analysis and visualization.', 'duration': 31.723, 'max_score': 2449.795, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2449795.jpg'}, {'end': 2592.669, 'src': 'embed', 'start': 2566.15, 'weight': 2, 'content': [{'end': 2576.478, 'text': 'you pass it through a linear regression model or you create a linear regression model which can then predict your price for 1.35 carat.', 'start': 2566.15, 'duration': 10.328}, {'end': 2580.121, 'text': 'So this is one example of model building.', 'start': 2576.618, 'duration': 3.503}, {'end': 2584.584, 'text': 'And then a little bit details of how linear regression works.', 'start': 2580.661, 'duration': 3.923}, {'end': 2592.669, 'text': 'So linear regression is basically coming up with a relation between an independent variable and a dependent variable.', 'start': 2584.904, 'duration': 7.765}], 'summary': 'Building a linear regression model to predict price for 1.35 carat diamond.', 'duration': 26.519, 'max_score': 2566.15, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2566150.jpg'}, {'end': 2865.218, 'src': 'embed', 'start': 2832.955, 'weight': 0, 'content': [{'end': 2837.238, 'text': "Now, in the end, let's take a quick look at the demand for data scientists.", 'start': 2832.955, 'duration': 4.283}, {'end': 2840.741, 'text': 'Data science is an area of great demand.', 'start': 2837.578, 'duration': 3.163}, {'end': 2846.885, 'text': 'The demand for data scientists is currently huge and the supply is very low.', 'start': 2841.081, 'duration': 5.804}, {'end': 2848.526, 'text': 'So there is a huge gap.', 'start': 2846.985, 'duration': 1.541}, {'end': 2852.329, 'text': 'So what are some of the industries with high demand for data scientists?', 'start': 2848.646, 'duration': 3.683}, {'end': 2865.218, 'text': "I think gaming is definitely one area where it's an industry which is consumer facing industry and a lot of people play games and growing industry and it requires a lot of data science.", 'start': 2852.509, 'duration': 12.709}], 'summary': 'High demand for data scientists in various industries due to low supply.', 'duration': 32.263, 'max_score': 2832.955, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2832955.jpg'}], 'start': 2449.795, 'title': 'Data science tools and model building', 'summary': 'Discusses tools like r, python, matlab, and sas for model planning and highlights the model building process, including linear regression for predicting the price of a 1.35 carat diamond. it also covers the demand for data scientists in industries such as gaming, healthcare, finance, marketing, and technology.', 'chapters': [{'end': 2985.787, 'start': 2449.795, 'title': 'Data science tools and model building', 'summary': 'Discusses various tools for model planning, such as r, python, matlab, and sas, and highlights the model building process, including the example of using linear regression to predict the price of a 1.35 carat diamond, along with the demand for data scientists in industries like gaming, healthcare, finance, marketing, and technology.', 'duration': 535.992, 'highlights': ['The demand for data scientists is currently huge and the supply is very low, creating a significant gap in the industry. The high demand for data scientists is emphasized, with a shortage in the supply, indicating a substantial opportunity in the field.', 'The chapter discusses various tools for model planning, including R, Python, MATLAB, and SAS, highlighting their capabilities for data analysis and machine learning. A detailed overview of tools for model planning, emphasizing the strengths of R, Python, MATLAB, and SAS for data analysis and machine learning.', 'The model building process is exemplified using linear regression to predict the price of a 1.35 carat diamond, demonstrating the application of the technique in data analysis. An example of model building through linear regression to predict the price of a specific diamond size, showcasing the practical application of the technique in data analysis.']}], 'duration': 535.992, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/KxryzSO1Fjs/pics/KxryzSO1Fjs2449795.jpg', 'highlights': ['The demand for data scientists is currently huge and the supply is very low, creating a significant gap in the industry.', 'The chapter discusses various tools for model planning, including R, Python, MATLAB, and SAS, highlighting their capabilities for data analysis and machine learning.', 'The model building process is exemplified using linear regression to predict the price of a 1.35 carat diamond, demonstrating the application of the technique in data analysis.']}], 'highlights': ['Self-driving cars projected to minimize accidents and reduce deaths by 2 million annually', 'In 10 to 15 years, most cars will be autonomous', 'Automotive companies investing in self-driving cars', "Data science's impact on reducing flight delays, improving route planning, and making promotional offers in the airlines industry", "Data science's role in logistics for companies like FedEx to optimize routes and determine the best mode of transport, leading to increased efficiency and cost reduction", 'Data science optimizes cab routes considering traffic, road conditions, and weather', 'Platforms like Netflix use data science to suggest TV shows based on user preferences', 'Data science influences political campaigns through personalized messages and predictions', 'Essential traits for a data scientist include curiosity, common sense, and communication skills', 'Python and R are highly popular in data science due to their extensive libraries and ease of learning', 'Clustering groups cricketers based on runs and wickets', 'Decision tree offers advantages over other classification mechanisms', 'Machine learning algorithms like regression and clustering are essential for data scientists', 'Stresses the importance of exploratory data analysis in understanding the data and determining the appropriate model', 'Emphasizes the process of model planning and training for statistical and machine learning models', 'Explains data cleaning techniques, including handling missing values and data preparation for machine learning', 'The demand for data scientists is currently huge and the supply is very low, creating a significant gap in the industry', 'The chapter discusses various tools for model planning, including R, Python, MATLAB, and SAS, highlighting their capabilities for data analysis and machine learning', 'The model building process is exemplified using linear regression to predict the price of a 1.35 carat diamond, demonstrating the application of the technique in data analysis']}