title
Live Day 1- Exploratory Data Analysis And Stock Analysis With Time series Data

description
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detail
{'title': 'Live Day 1- Exploratory Data Analysis And Stock Analysis With Time series Data', 'heatmap': [{'end': 730.613, 'start': 634.008, 'weight': 0.869}, {'end': 863.672, 'start': 813.582, 'weight': 0.719}, {'end': 1406.348, 'start': 1351.337, 'weight': 0.833}, {'end': 1769.151, 'start': 1719.115, 'weight': 0.712}, {'end': 1860.476, 'start': 1814.915, 'weight': 0.751}, {'end': 2584.431, 'start': 2399.536, 'weight': 0.768}, {'end': 3307.912, 'start': 3215.453, 'weight': 0.965}, {'end': 4216.094, 'start': 4165.671, 'weight': 0.855}], 'summary': "Covers a live session on youtube with extensive topics including eda, ets, ewma, arima, sarimax, acf, psef, fb profit, and machine learning project with time series data. it also delves into stock price data analysis, data visualization in russia-ukraine conflict, working with date time index, python's date and time module, aggregate functions in data analysis, and data visualization for time series analysis, providing comprehensive insights for tasks and concepts related to time series data.", 'chapters': [{'end': 311.929, 'segs': [{'end': 100.176, 'src': 'embed', 'start': 30.916, 'weight': 2, 'content': [{'end': 36.018, 'text': 'hello, guys, how are you all amazing?', 'start': 30.916, 'duration': 5.102}, {'end': 37.358, 'text': 'am i audible?', 'start': 36.018, 'duration': 1.34}, {'end': 43.46, 'text': "i've actually done some setup changes, so just let me know is my voice audible as such?", 'start': 37.358, 'duration': 6.102}, {'end': 54.648, 'text': 'So, guys, is my voice audible everybody?', 'start': 52.847, 'duration': 1.801}, {'end': 62.89, 'text': 'I think it is audible.', 'start': 62.05, 'duration': 0.84}, {'end': 66.792, 'text': 'Perfect Great.', 'start': 63.01, 'duration': 3.782}, {'end': 68.953, 'text': 'So how are you all?', 'start': 67.572, 'duration': 1.381}, {'end': 72.714, 'text': 'I hope you are doing absolutely fine right?', 'start': 69.233, 'duration': 3.481}, {'end': 74.981, 'text': 'time series, EDM.', 'start': 73.82, 'duration': 1.161}, {'end': 84.246, 'text': 'so this will probably be the best and this kind of videos I have not uploaded in time series kind of thing in my YouTube channel, dedicated videos.', 'start': 74.981, 'duration': 9.265}, {'end': 86.407, 'text': 'so I also had.', 'start': 84.246, 'duration': 2.161}, {'end': 90.59, 'text': 'I was pretty much excited to basically take up this live sessions.', 'start': 86.407, 'duration': 4.183}, {'end': 93.772, 'text': 'so it will be pretty much amazing, okay.', 'start': 90.59, 'duration': 3.182}, {'end': 100.176, 'text': 'so first of all, hit like share with all your friends wherever you can.', 'start': 93.772, 'duration': 6.404}], 'summary': 'Live session on time series and edm, asking for feedback and engagement.', 'duration': 69.26, 'max_score': 30.916, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE30916.jpg'}, {'end': 191.311, 'src': 'embed', 'start': 165.21, 'weight': 1, 'content': [{'end': 169.453, 'text': 'you really need to follow some of the steps and that specific steps I will be doing.', 'start': 165.21, 'duration': 4.243}, {'end': 179.081, 'text': 'Tomorrow we will be discussing about ETS, EWMA, ARIMA, SARIMAX, ACF, PSEF, SARIMAX and FB profit.', 'start': 170.714, 'duration': 8.367}, {'end': 181.283, 'text': 'So this will probably happen in tomorrow.', 'start': 179.101, 'duration': 2.182}, {'end': 187.908, 'text': 'Today why I am specifically taking EDA with time series data because it will take a lot of time.', 'start': 181.783, 'duration': 6.125}, {'end': 191.311, 'text': 'Because here we are going to discuss about lot of visualization also.', 'start': 187.968, 'duration': 3.343}], 'summary': "Tomorrow's discussion will cover ets, ewma, arima, sarimax, acf, psef, sarimax, and fb profit.", 'duration': 26.101, 'max_score': 165.21, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE165210.jpg'}, {'end': 231.979, 'src': 'embed', 'start': 208.364, 'weight': 4, 'content': [{'end': 217.25, 'text': 'so this is what we are actually going to focus on where, uh, by using arimax or serimax, we will try to solve some problems.', 'start': 208.364, 'duration': 8.886}, {'end': 224.037, 'text': "uh, one stock stock forecasting project we can take over here and we'll try to solve it.", 'start': 217.25, 'duration': 6.787}, {'end': 227.838, 'text': 'uh, and there are also some, like some projects like sale forecasting and all.', 'start': 224.037, 'duration': 3.801}, {'end': 229.939, 'text': "we'll try to do it in the third day.", 'start': 227.838, 'duration': 2.101}, {'end': 231.979, 'text': 'in the fourth day, we will try to cover this.', 'start': 229.939, 'duration': 2.04}], 'summary': 'Using arimax or serimax for stock and sale forecasting projects in 3-4 days.', 'duration': 23.615, 'max_score': 208.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE208364.jpg'}, {'end': 276.04, 'src': 'embed', 'start': 248.785, 'weight': 0, 'content': [{'end': 256.029, 'text': 'otherwise, after this session, we are also going to start deep learning, deep learning live sessions.', 'start': 248.785, 'duration': 7.244}, {'end': 259.69, 'text': 'now, in deep learning live sessions, it will be for seven days.', 'start': 256.029, 'duration': 3.661}, {'end': 267.074, 'text': 'uh, that will probably be my next live session and we will try to understand many, many topics as we go ahead.', 'start': 259.69, 'duration': 7.384}, {'end': 270.856, 'text': "okay, uh, so what are the today's topic that we are going to focus on?", 'start': 267.074, 'duration': 3.782}, {'end': 276.04, 'text': 'The first topic that we are going to see is Pandas Data Reader.', 'start': 272.357, 'duration': 3.683}], 'summary': 'Next live session: deep learning for 7 days, starting with pandas data reader', 'duration': 27.255, 'max_score': 248.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE248785.jpg'}], 'start': 30.916, 'title': 'Time series data analysis', 'summary': 'Covers a time series edm live session on youtube and extensive topics including eda, ets, ewma, arima, sarimax, acf, psef, fb profit, and a machine learning project with time series data, leading to a potential deep learning live session for seven days.', 'chapters': [{'end': 100.176, 'start': 30.916, 'title': 'Time series edm live session', 'summary': 'Introduces a time series edm live session on youtube, expressing excitement and seeking audience interaction for the upcoming videos.', 'duration': 69.26, 'highlights': ['The speaker expresses excitement for the upcoming time series EDM live sessions, indicating a new content direction for the YouTube channel.', 'The speaker seeks audience engagement by encouraging viewers to like and share the upcoming videos with friends, emphasizing the desire for wider reach and interaction.', 'The speaker checks for the audibility of their voice, reflecting a concern for audio quality and ensuring effective communication with the audience.']}, {'end': 311.929, 'start': 101.303, 'title': 'Time series data eda & forecasting', 'summary': 'Will cover extensive topics including eda, ets, ewma, arima, sarimax, acf, psef, fb profit, and machine learning project with time series data, leading to a potential deep learning live session for seven days.', 'duration': 210.626, 'highlights': ["The chapter will cover EDA, ETS, EWMA, ARIMA, SARIMAX, ACF, PSEF, and FB profit in tomorrow's session, which will involve extensive visualization and parameter exploration.", 'The third day will focus on a machine learning project with time series data, utilizing ARIMAX or SARIMAX to solve stock forecasting and sales forecasting problems.', 'The potential deep learning live session, spanning seven days, will provide in-depth understanding of implementing time series with the help of deep learning.']}], 'duration': 281.013, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE30916.jpg', 'highlights': ['The potential deep learning live session, spanning seven days, will provide in-depth understanding of implementing time series with the help of deep learning.', "The chapter will cover EDA, ETS, EWMA, ARIMA, SARIMAX, ACF, PSEF, and FB profit in tomorrow's session, which will involve extensive visualization and parameter exploration.", 'The speaker seeks audience engagement by encouraging viewers to like and share the upcoming videos with friends, emphasizing the desire for wider reach and interaction.', 'The speaker expresses excitement for the upcoming time series EDM live sessions, indicating a new content direction for the YouTube channel.', 'The third day will focus on a machine learning project with time series data, utilizing ARIMAX or SARIMAX to solve stock forecasting and sales forecasting problems.', 'The speaker checks for the audibility of their voice, reflecting a concern for audio quality and ensuring effective communication with the audience.']}, {'end': 607.262, 'segs': [{'end': 428.307, 'src': 'embed', 'start': 311.929, 'weight': 0, 'content': [{'end': 319.894, 'text': "okay, so this is what we are going to do tomorrow and day after tomorrow, we'll focus on machine learning projects related to time series data.", 'start': 311.929, 'duration': 7.965}, {'end': 327.5, 'text': 'overall, excited, yes, so shall we start, everyone.', 'start': 319.894, 'duration': 7.606}, {'end': 332.623, 'text': 'shall we start and please hit like before we start, definitely, okay.', 'start': 327.5, 'duration': 5.123}, {'end': 335.665, 'text': 'so all these things we are going to cover today.', 'start': 332.623, 'duration': 3.042}, {'end': 342.57, 'text': "so to start with, uh, i really want to show you one library which is called as panda's data reader.", 'start': 335.665, 'duration': 6.905}, {'end': 346.616, 'text': 'Okay, so Pandas data reader.', 'start': 344.514, 'duration': 2.102}, {'end': 351.36, 'text': 'it is an amazing library which will actually help you to provide some kind of financial data.', 'start': 346.616, 'duration': 4.744}, {'end': 357.926, 'text': 'So here you can see up to date remote data access for Pandas works for multiple versions of Pandas.', 'start': 351.38, 'duration': 6.546}, {'end': 362.67, 'text': 'Okay, so in order to do it, first of all, you need to install Pandas data reader.', 'start': 358.006, 'duration': 4.664}, {'end': 364.312, 'text': 'And internally.', 'start': 363.431, 'duration': 0.881}, {'end': 367.256, 'text': 'if I go and probably see in remote data access.', 'start': 364.312, 'duration': 2.944}, {'end': 372.722, 'text': "so here you'll be able to see, you'll be able to get data from this many different, different platforms.", 'start': 367.256, 'duration': 5.466}, {'end': 377.868, 'text': 'Okay Like Tingo, IEX, Alpha Vantage, Econbee, Enigma, Quandl.', 'start': 372.882, 'duration': 4.986}, {'end': 381.409, 'text': 'World Bank, OECD, Eurostates and all.', 'start': 378.648, 'duration': 2.761}, {'end': 384.491, 'text': 'And for this, you also require some kind of API key.', 'start': 381.79, 'duration': 2.701}, {'end': 390.874, 'text': "If probably you don't have an API key, one thing you can do is that you can basically go and login into the account and create an API key.", 'start': 385.051, 'duration': 5.823}, {'end': 396.036, 'text': "But if you don't have an API key also, then also we can actually read that specific data.", 'start': 391.314, 'duration': 4.722}, {'end': 400.618, 'text': "Okay So I'll try to show you how we can read that data and it'll be quite interesting.", 'start': 396.396, 'duration': 4.222}, {'end': 401.819, 'text': "So let's start.", 'start': 401.138, 'duration': 0.681}, {'end': 404.463, 'text': "And let's start with the first thing.", 'start': 403.121, 'duration': 1.342}, {'end': 408.249, 'text': "What I'm actually going to do install Pandas data reader.", 'start': 404.904, 'duration': 3.345}, {'end': 414.318, 'text': "The first step I'm going to basically install Pandas data reader.", 'start': 408.749, 'duration': 5.569}, {'end': 417.701, 'text': "Okay, panda's data data.", 'start': 414.898, 'duration': 2.803}, {'end': 425.305, 'text': 'because whenever we are working with a kind of time series data always remember that that data deals with a lot of data and time values.', 'start': 417.701, 'duration': 7.604}, {'end': 428.307, 'text': 'okay, so that is first of all very much important.', 'start': 425.305, 'duration': 3.002}], 'summary': 'Focus on machine learning projects related to time series data using pandas data reader for financial data access.', 'duration': 116.378, 'max_score': 311.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE311929.jpg'}, {'end': 557.951, 'src': 'embed', 'start': 532.318, 'weight': 4, 'content': [{'end': 541.886, 'text': 'and apart from that, let me import one very important library, which is called as from okay, from, uh, date time.', 'start': 532.318, 'duration': 9.568}, {'end': 545.187, 'text': "i'm going to use this date time, and date time is a library.", 'start': 541.886, 'duration': 3.301}, {'end': 548.228, 'text': "if you're probably working with time series data, you really need to have this.", 'start': 545.187, 'duration': 3.041}, {'end': 551.549, 'text': "okay, so i'm going to import date time.", 'start': 548.228, 'duration': 3.321}, {'end': 557.951, 'text': 'okay, so this three are the libraries that we will probably require for doing most of our work.', 'start': 551.549, 'duration': 6.402}], 'summary': "Imported the 'datetime' library for working with time series data.", 'duration': 25.633, 'max_score': 532.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE532318.jpg'}], 'start': 311.929, 'title': 'Machine learning projects for time series data', 'summary': 'Focuses on machine learning projects related to time series data, utilizing pandas data reader to access financial data from various platforms such as tingo, iex, alpha vantage, and more. it also demonstrates how to read financial time series data without an api key, providing guidance on the required process and libraries.', 'chapters': [{'end': 381.409, 'start': 311.929, 'title': 'Machine learning projects for time series data', 'summary': 'Focuses on machine learning projects related to time series data, using pandas data reader to access financial data from multiple platforms, including tingo, iex, alpha vantage, and more.', 'duration': 69.48, 'highlights': ['Pandas data reader provides remote data access for financial data from multiple platforms The library Pandas data reader offers remote data access for financial data from various platforms like Tingo, IEX, Alpha Vantage, and others.', 'Focus on machine learning projects related to time series data The chapter will concentrate on machine learning projects related to time series data over the next two days.', 'Installation of Pandas data reader is necessary for accessing financial data To utilize Pandas data reader for accessing financial data, the installation of the library is required.']}, {'end': 607.262, 'start': 381.79, 'title': 'Reading financial data using pandas', 'summary': 'Demonstrates how to install pandas data reader and import libraries to read financial time series data without the need for an api key, providing guidance on the process and libraries required.', 'duration': 225.472, 'highlights': ['The process of installing pandas data reader and importing necessary libraries for reading financial time series data is described, emphasizing the importance of these steps in working with such data.', 'The necessity of an API key for accessing financial data is highlighted, with an explanation of how to read the data without requiring an API key, demonstrating the accessibility of the process.', 'The different data sources available through pandas data reader, including Bank of Canada, Yahoo, Quandl, and others, are mentioned, showcasing the variety of data accessible through the tool.', 'The significance of the datetime library in working with time series data and the requirement for its import is discussed, emphasizing its importance in the context of the tutorial.']}], 'duration': 295.333, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE311929.jpg', 'highlights': ['Pandas data reader provides remote data access for financial data from multiple platforms', 'The process of installing pandas data reader and importing necessary libraries for reading financial time series data is described', 'The necessity of an API key for accessing financial data is highlighted, with an explanation of how to read the data without requiring an API key', 'The different data sources available through pandas data reader, including Bank of Canada, Yahoo, Quandl, and others, are mentioned', 'The significance of the datetime library in working with time series data and the requirement for its import is discussed', 'Focus on machine learning projects related to time series data', 'Installation of Pandas data reader is necessary for accessing financial data']}, {'end': 910.14, 'segs': [{'end': 730.613, 'src': 'heatmap', 'start': 609.468, 'weight': 0, 'content': [{'end': 615.713, 'text': 'Okay, how do I read something? Okay guys, is my screen visible? Because I can see probably in 1080 pixels.', 'start': 609.468, 'duration': 6.245}, {'end': 616.614, 'text': "I don't think so.", 'start': 616.134, 'duration': 0.48}, {'end': 617.235, 'text': 'It is blurred.', 'start': 616.654, 'duration': 0.581}, {'end': 620.077, 'text': 'Your internet speed is very slow.', 'start': 617.715, 'duration': 2.362}, {'end': 625.061, 'text': 'Okay, so use Geo 5G and try to increase your speed.', 'start': 620.638, 'duration': 4.423}, {'end': 629.104, 'text': "Okay, so let's go ahead and try to read some data set.", 'start': 625.221, 'duration': 3.883}, {'end': 634.008, 'text': 'and right now we are basically going to read the data set.', 'start': 629.104, 'duration': 4.904}, {'end': 643.557, 'text': 'uh, there is, with respect to one thing like iex, and this iex will actually actually help you to get data, also through google finance, if you want,', 'start': 634.008, 'duration': 9.549}, {'end': 644.758, 'text': 'from yahoo finance.', 'start': 643.557, 'duration': 1.201}, {'end': 646.099, 'text': 'you can basically take this.', 'start': 644.758, 'duration': 1.341}, {'end': 651.822, 'text': "okay, so here i'm going to basically take get data underscore yahoo.", 'start': 646.099, 'duration': 5.723}, {'end': 656.126, 'text': "okay, so yahoo finance, i'm going to basically see and i'm going to pick up the data.", 'start': 651.822, 'duration': 4.304}, {'end': 661.491, 'text': 'so which stock price, which stock data you want to really take up?', 'start': 656.126, 'duration': 5.365}, {'end': 662.772, 'text': 'okay, it is up to you.', 'start': 661.491, 'duration': 1.281}, {'end': 663.092, 'text': 'you can.', 'start': 662.772, 'duration': 0.32}, {'end': 665.234, 'text': 'you can take any kind of data that you want.', 'start': 663.092, 'duration': 2.142}, {'end': 668.657, 'text': "let's say that most famous data right now will be tesla.", 'start': 665.234, 'duration': 3.423}, {'end': 676.302, 'text': 'right. so if i probably see tesla, share price, okay, so tesla is basically written as tsla.', 'start': 668.657, 'duration': 7.645}, {'end': 678.743, 'text': "okay, so i'm just going to, uh, copy this.", 'start': 676.302, 'duration': 2.441}, {'end': 685.507, 'text': "let's see whether i'll be able to read tesla data set or not, and if i execute it, you will be able to see something.", 'start': 678.743, 'duration': 6.764}, {'end': 686.627, 'text': 'data from 24 to 2022.', 'start': 685.507, 'duration': 1.12}, {'end': 689.489, 'text': 'the starting date is 2017 and the ending that is 20.', 'start': 686.627, 'duration': 2.862}, {'end': 696.192, 'text': 'uh, 24, 0 to 2020..', 'start': 689.489, 'duration': 6.703}, {'end': 702.279, 'text': 'to. so that basically means that here you will be able to get the data of the past five years.', 'start': 696.192, 'duration': 6.087}, {'end': 708.066, 'text': "from today till the past five years, you'll be able to see all the data set available over here.", 'start': 702.279, 'duration': 5.787}, {'end': 716.591, 'text': 'okay, and here you can basically see that how you can by default any, any, any stocks you want to basically see, go with respect to facebook,', 'start': 708.066, 'duration': 8.525}, {'end': 719.571, 'text': 'with respect to microsoft, with respect tesla.', 'start': 716.591, 'duration': 2.98}, {'end': 723.792, 'text': 'so all this data you will be able to see just in one line of code.', 'start': 719.571, 'duration': 4.221}, {'end': 730.613, 'text': 'okay, and here the data that you are actually able to see is like high, low, open, close, volume, adjacent, close, okay.', 'start': 723.792, 'duration': 6.821}], 'summary': 'Using geo 5g to increase internet speed, accessing stock data including tesla, with data available from the past five years.', 'duration': 46.658, 'max_score': 609.468, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE609468.jpg'}, {'end': 746.559, 'src': 'embed', 'start': 702.279, 'weight': 1, 'content': [{'end': 708.066, 'text': "from today till the past five years, you'll be able to see all the data set available over here.", 'start': 702.279, 'duration': 5.787}, {'end': 716.591, 'text': 'okay, and here you can basically see that how you can by default any, any, any stocks you want to basically see, go with respect to facebook,', 'start': 708.066, 'duration': 8.525}, {'end': 719.571, 'text': 'with respect to microsoft, with respect tesla.', 'start': 716.591, 'duration': 2.98}, {'end': 723.792, 'text': 'so all this data you will be able to see just in one line of code.', 'start': 719.571, 'duration': 4.221}, {'end': 730.613, 'text': 'okay, and here the data that you are actually able to see is like high, low, open, close, volume, adjacent, close, okay.', 'start': 723.792, 'duration': 6.821}, {'end': 736.594, 'text': 'so these are some of the information with respect to any stock share price that you can actually see over here.', 'start': 730.613, 'duration': 5.981}, {'end': 744.197, 'text': 'okay, with respect to indian stocks, yes, definitely, there are some of the uh websites, third-party websites,', 'start': 736.594, 'duration': 7.603}, {'end': 746.559, 'text': 'that can actually provide you that specific data.', 'start': 744.197, 'duration': 2.362}], 'summary': 'Access 5 years of stock data for facebook, microsoft, and tesla using one line of code, including high, low, open, close, volume, and adjacent close information.', 'duration': 44.28, 'max_score': 702.279, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE702279.jpg'}, {'end': 863.672, 'src': 'heatmap', 'start': 813.582, 'weight': 0.719, 'content': [{'end': 821.651, 'text': "And obviously, you know, in this kind of data, volume will be seen in much way because here you can see that it's quite huge.", 'start': 813.582, 'duration': 8.069}, {'end': 826.637, 'text': 'Volume basically means how many number of shares are basically being traded on that specific day.', 'start': 821.992, 'duration': 4.645}, {'end': 836.661, 'text': 'Okay, So here you can see df underscore, tesla dot plot and if I probably execute this here, you can see all very cubersome plotting.', 'start': 826.917, 'duration': 9.744}, {'end': 837.481, 'text': 'you can see over here.', 'start': 836.661, 'duration': 0.82}, {'end': 842.143, 'text': 'Obviously volume will be lot of this because you have a very huge value over here.', 'start': 837.541, 'duration': 4.602}, {'end': 845.584, 'text': "Right But we'll not try to plot it in this specific way.", 'start': 842.723, 'duration': 2.861}, {'end': 849.745, 'text': 'Okay So we will try to see it in a different way over here.', 'start': 845.964, 'duration': 3.781}, {'end': 857.648, 'text': "So what I'm actually going to do, I'm just going to write DF Tesla with respect to let's say I want with respect to high.", 'start': 850.165, 'duration': 7.483}, {'end': 863.672, 'text': "and i'm just going to plot like this now here you can see that the plotting looks quite good.", 'start': 858.528, 'duration': 5.144}], 'summary': 'Analyzing data volume and plotting for tesla stock.', 'duration': 50.09, 'max_score': 813.582, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE813582.jpg'}, {'end': 910.14, 'src': 'embed', 'start': 882.877, 'weight': 3, 'content': [{'end': 888.159, 'text': 'Okay, so here you can basically see from, probably from the start of 2020, you know,', 'start': 882.877, 'duration': 5.282}, {'end': 892.92, 'text': 'the Tesla stock price has increased tremendously and currently it is going down.', 'start': 888.159, 'duration': 4.761}, {'end': 895.081, 'text': 'Obviously why it is going down recently?', 'start': 893.04, 'duration': 2.041}, {'end': 896.942, 'text': 'Can anybody tell me why?', 'start': 895.921, 'duration': 1.021}, {'end': 901.797, 'text': 'it is going down?', 'start': 900.717, 'duration': 1.08}, {'end': 905.258, 'text': 'come on, tell me, okay, why it is going down recently.', 'start': 901.797, 'duration': 3.461}, {'end': 907.739, 'text': 'okay, you know the answer, right.', 'start': 905.258, 'duration': 2.481}, {'end': 910.14, 'text': 'so obviously the price is going down.', 'start': 907.739, 'duration': 2.401}], 'summary': 'Tesla stock price increased in 2020 but is currently decreasing.', 'duration': 27.263, 'max_score': 882.877, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE882877.jpg'}], 'start': 609.468, 'title': 'Analyzing stock price data', 'summary': 'Covers analyzing stock price data using geo 5g, accessing stock data from sources like iex, google finance, and yahoo finance, and utilizing a single line of code to retrieve stock data for companies like tesla from the past five years. it also includes analyzing high, low, open, close, and volume, and plotting time series data to visualize trends and fluctuations, ultimately showcasing the increase and recent decrease in tesla stock price.', 'chapters': [{'end': 723.792, 'start': 609.468, 'title': 'Reading data sets and stock prices', 'summary': 'Discusses reading data sets and stock prices using geo 5g to improve internet speed, accessing stock data from sources like iex, google finance, and yahoo finance, and utilizing a single line of code to retrieve stock data for companies like tesla from the past five years.', 'duration': 114.324, 'highlights': ["Accessing stock data from sources like iex, google finance, and yahoo finance The chapter discusses using iex, google finance, and yahoo finance to retrieve stock data, offering flexibility to choose any stock data, and demonstrating the process with the example of retrieving Tesla's stock price data.", 'Utilizing a single line of code to retrieve stock data for companies like Tesla from the past five years The chapter demonstrates how to use a single line of code to access stock data for companies like Tesla for the past five years, providing a quick and efficient method for retrieving historical stock data.', 'Using Geo 5G to improve internet speed for data retrieval The chapter advises using Geo 5G to improve internet speed for data retrieval, highlighting the importance of fast internet connection for efficient data access and analysis.']}, {'end': 910.14, 'start': 723.792, 'title': 'Analyzing stock price data', 'summary': 'Covers analyzing stock price data, including high, low, open, close, and volume, and plotting time series data to visualize trends and fluctuations, ultimately showcasing the increase and recent decrease in tesla stock price.', 'duration': 186.348, 'highlights': ['The chapter covers analyzing stock price data, including high, low, open, close, and volume. The speaker discusses the data available for stock analysis, including high, low, open, close, and volume.', 'Plotting time series data to visualize trends and fluctuations, showcasing the increase and recent decrease in Tesla stock price. The speaker demonstrates the process of plotting time series data to visualize the increase and recent decrease in Tesla stock price, prompting audience interaction to identify the reason for the recent decrease.']}], 'duration': 300.672, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE609468.jpg', 'highlights': ['Accessing stock data from sources like iex, google finance, and yahoo finance, offering flexibility to choose any stock data.', 'Utilizing a single line of code to retrieve stock data for companies like Tesla from the past five years, providing a quick and efficient method for retrieving historical stock data.', 'The chapter covers analyzing stock price data, including high, low, open, close, and volume, providing comprehensive insights into stock data analysis.', 'Plotting time series data to visualize trends and fluctuations, showcasing the increase and recent decrease in Tesla stock price, prompting audience interaction to identify the reason for the recent decrease.', 'Using Geo 5G to improve internet speed for data retrieval, highlighting the importance of fast internet connection for efficient data access and analysis.']}, {'end': 1666.928, 'segs': [{'end': 941.087, 'src': 'embed', 'start': 910.14, 'weight': 0, 'content': [{'end': 912.52, 'text': 'there is some specific reason for that?', 'start': 910.14, 'duration': 2.38}, {'end': 915.041, 'text': 'uh, russia is trying to raise.', 'start': 912.52, 'duration': 2.521}, {'end': 919.762, 'text': "uh, it's already raised an amazing war with ukraine, uh, which should not happen.", 'start': 915.041, 'duration': 4.721}, {'end': 924.845, 'text': "we should definitely work for peace, uh, and can't help, you know.", 'start': 919.762, 'duration': 5.083}, {'end': 929.147, 'text': "so even diplo diplomatic talks are not working, can't help, okay.", 'start': 924.845, 'duration': 4.302}, {'end': 931.708, 'text': 'so because of that, right now you can see the price is going down.', 'start': 929.147, 'duration': 2.561}, {'end': 935.342, 'text': 'god bless ukraine, right?', 'start': 933.24, 'duration': 2.102}, {'end': 936.943, 'text': 'that is what we can basically say.', 'start': 935.342, 'duration': 1.601}, {'end': 941.087, 'text': 'okay, df underscore tesla with this plot figure size of 12, comma 4.', 'start': 936.943, 'duration': 4.144}], 'summary': "Russia's conflict with ukraine has caused a drop in prices; diplomatic talks have failed.", 'duration': 30.947, 'max_score': 910.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE910140.jpg'}, {'end': 1002.256, 'src': 'embed', 'start': 974.473, 'weight': 1, 'content': [{'end': 978.514, 'text': "Okay So here I'm actually going to say X limit and Y limit.", 'start': 974.473, 'duration': 4.041}, {'end': 985.416, 'text': 'So X limit and Y limit, I really want to provide some limit with respect to this so that I can see that graph clearly.', 'start': 978.994, 'duration': 6.422}, {'end': 990.417, 'text': 'And similarly to this, with respect to the Y limit also, I want to provide some value, some limitations.', 'start': 985.736, 'duration': 4.681}, {'end': 998.953, 'text': 'Okay, now, in order to provide x limit, i will just say xlim is equal to in this specific list.', 'start': 990.917, 'duration': 8.036}, {'end': 1002.256, 'text': 'you can also provide the x limit in a tuple or in a list.', 'start': 998.953, 'duration': 3.303}], 'summary': 'Setting x and y limits to clearly visualize the graph.', 'duration': 27.783, 'max_score': 974.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE974473.jpg'}, {'end': 1098.961, 'src': 'embed', 'start': 1069.24, 'weight': 3, 'content': [{'end': 1070.781, 'text': "i'm just going to keep this as 900.", 'start': 1069.24, 'duration': 1.541}, {'end': 1077.506, 'text': 'okay, so now, if i execute it now, you can see my graph a little bit much more better than the previous one.', 'start': 1070.781, 'duration': 6.725}, {'end': 1080.628, 'text': "okay, candlestick plot also, i'll show you how to do it.", 'start': 1077.506, 'duration': 3.122}, {'end': 1081.849, 'text': 'but just focus right now.', 'start': 1080.628, 'duration': 1.221}, {'end': 1083.35, 'text': "we'll go step by step.", 'start': 1081.849, 'duration': 1.501}, {'end': 1085.631, 'text': "okay, everything we will do it, don't worry.", 'start': 1083.35, 'duration': 2.281}, {'end': 1089.034, 'text': 'okay, now is this clear everybody?', 'start': 1085.631, 'duration': 3.403}, {'end': 1090.315, 'text': 'are you liking the session?', 'start': 1089.034, 'duration': 1.281}, {'end': 1092.076, 'text': 'are you able to understand everything?', 'start': 1090.315, 'duration': 1.761}, {'end': 1096.779, 'text': 'just quickly tell me yes, yes, everybody.', 'start': 1092.076, 'duration': 4.703}, {'end': 1098.961, 'text': 'i hope everybody is loving the session.', 'start': 1096.779, 'duration': 2.182}], 'summary': 'Graph improved at 900, will show candlestick plot, ensuring understanding and engagement.', 'duration': 29.721, 'max_score': 1069.24, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1069240.jpg'}, {'end': 1406.348, 'src': 'heatmap', 'start': 1351.337, 'weight': 0.833, 'content': [{'end': 1360.522, 'text': "Now let's say that, okay, fine, this is my index, okay, I'm going to save it, and tell me what will I get probably if I execute this.", 'start': 1351.337, 'duration': 9.185}, {'end': 1366.204, 'text': "If I execute this, and probably I'll write open over here.", 'start': 1362.683, 'duration': 3.521}, {'end': 1379.511, 'text': 'So what will I get, okay? What will I get? So here will basically my stock open, or let me just write my share price open, right? Open data.', 'start': 1367.125, 'duration': 12.386}, {'end': 1383.403, 'text': "and here I'm just going to execute it right.", 'start': 1380.982, 'duration': 2.421}, {'end': 1385.083, 'text': 'so here I definitely.', 'start': 1383.403, 'duration': 1.68}, {'end': 1393.885, 'text': 'if you go and see my share underscore open here, you are actually able to get the open data set with respect to this specific data right in the index.', 'start': 1385.083, 'duration': 8.802}, {'end': 1398.306, 'text': 'you will be able to see all the dates that you are specifically wanting it.', 'start': 1393.885, 'duration': 4.421}, {'end': 1399.966, 'text': "why I'm specifically doing it?", 'start': 1398.306, 'duration': 1.66}, {'end': 1406.348, 'text': "don't worry, I'll tell you, because I'm just going to plot some amazing diagrams and try to show you what all things we can actually do.", 'start': 1399.966, 'duration': 6.382}], 'summary': "Executing the index will retrieve the stock's open data set for specific dates.", 'duration': 55.011, 'max_score': 1351.337, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1351337.jpg'}, {'end': 1507.08, 'src': 'embed', 'start': 1483.413, 'weight': 2, 'content': [{'end': 1491.875, 'text': "Okay So here I'm actually going to create some kind of subplot so that, you know, I will be able to display some kind of data with respect to dates.", 'start': 1483.413, 'duration': 8.462}, {'end': 1499.458, 'text': 'Okay So in order to create the plots, I will create my figure, comma, axis and use plt.subplots.', 'start': 1491.955, 'duration': 7.503}, {'end': 1502.739, 'text': "I hope I've made this video tutorial already in my channel.", 'start': 1499.878, 'duration': 2.861}, {'end': 1507.08, 'text': "Okay And then I'm going to basically say axis.plot.", 'start': 1503.299, 'duration': 3.781}], 'summary': 'Creating subplot for data display using plt.subplots.', 'duration': 23.667, 'max_score': 1483.413, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1483413.jpg'}], 'start': 910.14, 'title': 'Data visualization in russia-ukraine conflict', 'summary': 'Discusses the russia-ukraine conflict and emphasizes the need for peace, while providing a tutorial on setting limits for data visualization, and also covers data visualization with matplotlib, including applying coloring, using different parameters, plotting subplots, and preventing overlapping in the plot.', 'chapters': [{'end': 1090.315, 'start': 910.14, 'title': 'Russia-ukraine conflict and data visualization', 'summary': 'Discusses the russia-ukraine conflict and emphasizes the need for peace, while also providing a detailed tutorial on setting limits for data visualization, using specific examples and code snippets.', 'duration': 180.175, 'highlights': ['The chapter emphasizes the need for peace in the Russia-Ukraine conflict and mentions the impact on prices.', 'The tutorial provides detailed instructions on setting x and y limits for data visualization, with specific examples and code snippets.', 'The speaker expresses interest in analyzing data related to regions experiencing price increases.', 'The tutorial includes step-by-step guidance on improving data visualization and offers to cover candlestick plots in the future.']}, {'end': 1666.928, 'start': 1090.315, 'title': 'Data visualization with matplotlib', 'summary': 'Covers data visualization with matplotlib, including applying coloring, using different parameters, plotting subplots, and preventing overlapping in the plot with quantifiable data and key points.', 'duration': 576.613, 'highlights': ['The chapter covers data visualization with Matplotlib, including applying coloring, using different parameters, plotting subplots, and preventing overlapping in the plot. Covers data visualization with Matplotlib, applying coloring, using different parameters, plotting subplots, and preventing overlapping in the plot.', 'Broadcasting indexing to perform a similar task and reading specific indexes and rows from a dataset. Broadcasting indexing, reading specific indexes and rows from a dataset.', 'Using matplotlib.pyplot to create subplots and displaying data with respect to dates, and overcoming overlapping in the plot. Using matplotlib.pyplot to create subplots, displaying data with respect to dates, and overcoming overlapping in the plot.', 'Utilizing figure.dot.auto_fmt_x_date to prevent overlapping in the plot and improve the visualization. Utilizing figure.dot.auto_fmt_x_date to prevent overlapping in the plot and improve the visualization.']}], 'duration': 756.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE910140.jpg', 'highlights': ['The chapter emphasizes the need for peace in the Russia-Ukraine conflict and mentions the impact on prices.', 'The tutorial provides detailed instructions on setting x and y limits for data visualization, with specific examples and code snippets.', 'The chapter covers data visualization with Matplotlib, including applying coloring, using different parameters, plotting subplots, and preventing overlapping in the plot.', 'The tutorial includes step-by-step guidance on improving data visualization and offers to cover candlestick plots in the future.']}, {'end': 2306.557, 'segs': [{'end': 1726.077, 'src': 'embed', 'start': 1695.058, 'weight': 0, 'content': [{'end': 1699.32, 'text': "Now let's go and discuss about one more EDA topic which is called as date time index.", 'start': 1695.058, 'duration': 4.262}, {'end': 1703.722, 'text': 'But till now we have specifically worked with respect to date time data.', 'start': 1699.38, 'duration': 4.342}, {'end': 1710.227, 'text': 'Okay, now, if i probably go and see my df underscore, tesla and if i write dot info.', 'start': 1704.042, 'duration': 6.185}, {'end': 1714.791, 'text': "so here, if i'm executing it by default, you can see that.", 'start': 1710.227, 'duration': 4.564}, {'end': 1719.115, 'text': "okay, uh, fine, the index column, let's see.", 'start': 1714.791, 'duration': 4.324}, {'end': 1726.077, 'text': 'okay, see this guys, everybody, if i write df, dot, tesla, dot reset index, If I execute this here,', 'start': 1719.115, 'duration': 6.962}], 'summary': 'Discussion about date time index and data manipulation in a dataframe.', 'duration': 31.019, 'max_score': 1695.058, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1695058.jpg'}, {'end': 1769.151, 'src': 'heatmap', 'start': 1719.115, 'weight': 0.712, 'content': [{'end': 1726.077, 'text': 'okay, see this guys, everybody, if i write df, dot, tesla, dot reset index, If I execute this here,', 'start': 1719.115, 'duration': 6.962}, {'end': 1728.96, 'text': "you'll be able to see I'm getting a date time column separately now.", 'start': 1726.077, 'duration': 2.883}, {'end': 1732.625, 'text': 'Because initially this was my date column as an index.', 'start': 1729.341, 'duration': 3.284}, {'end': 1737.892, 'text': 'Now suppose if I probably save it in df underscore Tesla over here and execute it.', 'start': 1732.665, 'duration': 5.227}, {'end': 1742.337, 'text': 'And if I write df underscore, Tesla dot info.', 'start': 1738.313, 'duration': 4.024}, {'end': 1744.438, 'text': 'right. so here you can basically see that.', 'start': 1742.337, 'duration': 2.101}, {'end': 1747.72, 'text': 'fine, i have a date column which is in the form of date time 64.', 'start': 1744.438, 'duration': 3.282}, {'end': 1751.303, 'text': 'uh, this basically means that it is in the date time format.', 'start': 1747.72, 'duration': 3.583}, {'end': 1752.684, 'text': 'okay, always remember,', 'start': 1751.303, 'duration': 1.381}, {'end': 1764.65, 'text': "whenever you probably have some kind of data in which a date column is actually there And that specific date column in most of the use cases you'll be seeing in an object format.", 'start': 1752.684, 'duration': 11.966}, {'end': 1766.43, 'text': 'Object basically means in a string format.', 'start': 1764.73, 'duration': 1.7}, {'end': 1769.151, 'text': 'Now, in order to convert that, there are multiple ways.', 'start': 1766.79, 'duration': 2.361}], 'summary': 'Converting date column in dataframe to datetime format using df.tesla.reset_index()', 'duration': 50.036, 'max_score': 1719.115, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1719115.jpg'}, {'end': 1860.476, 'src': 'heatmap', 'start': 1814.915, 'weight': 0.751, 'content': [{'end': 1816.956, 'text': 'then it will try to convert that into datetime.', 'start': 1814.915, 'duration': 2.041}, {'end': 1822.02, 'text': 'So if this is an object, right now it is already in datetime, so you will be able to see it in datetime.', 'start': 1817.197, 'duration': 4.823}, {'end': 1831.106, 'text': 'But now here you have already seen that if I am writing df underscore tesla, here is my dataset, right now this date is not an index.', 'start': 1822.52, 'duration': 8.586}, {'end': 1836.249, 'text': 'It is always good that if you really want to plot in an amazing way, we set this as an index.', 'start': 1831.226, 'duration': 5.023}, {'end': 1837.79, 'text': 'But I did reset index over here.', 'start': 1836.289, 'duration': 1.501}, {'end': 1841.113, 'text': 'If I want to revert it, just write dot set index.', 'start': 1838.171, 'duration': 2.942}, {'end': 1849.971, 'text': 'and here you just specify your df.tesla with respect to your date column.', 'start': 1842.307, 'duration': 7.664}, {'end': 1853.933, 'text': 'so if you execute this here, you will be able to see that it is there.', 'start': 1849.971, 'duration': 3.962}, {'end': 1856.174, 'text': 'but here still the date column has not been dropped.', 'start': 1853.933, 'duration': 2.241}, {'end': 1860.476, 'text': 'so i think there should be an option which is like drop is equal to true.', 'start': 1856.174, 'duration': 4.302}], 'summary': 'Demonstrates converting a column to datetime and setting it as an index in a dataset.', 'duration': 45.561, 'max_score': 1814.915, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1814915.jpg'}, {'end': 2035.77, 'src': 'embed', 'start': 1996.336, 'weight': 2, 'content': [{'end': 2000.638, 'text': "uh, i don't know what he was doing at that point of time, but that is there.", 'start': 1996.336, 'duration': 4.302}, {'end': 2006.399, 'text': 'and now they cannot be changed, changing it, because it has a lot of internal, different type of functions.', 'start': 2000.638, 'duration': 5.761}, {'end': 2012.481, 'text': 'okay, now, suppose if you really why this date time can actually play a very important role, because any kind of data set.', 'start': 2006.399, 'duration': 6.082}, {'end': 2015.621, 'text': 'when i say about this data, right, it is a date time object only.', 'start': 2012.481, 'duration': 3.14}, {'end': 2017.722, 'text': 'okay, and with the help of this date time object.', 'start': 2015.621, 'duration': 2.101}, {'end': 2020.162, 'text': 'you can really play in an amazing way.', 'start': 2017.722, 'duration': 2.44}, {'end': 2021.943, 'text': 'with respect to the date time data.', 'start': 2020.162, 'duration': 1.781}, {'end': 2024.924, 'text': 'okay, Suppose if I say I want to create a date.', 'start': 2021.943, 'duration': 2.981}, {'end': 2027.626, 'text': "Suppose let's say I want to create a date with respect to date time.", 'start': 2025.044, 'duration': 2.582}, {'end': 2035.77, 'text': 'So if I press shift tab here, you can see that I have parameters like year, month, day, hour, minutes, seconds, microseconds,', 'start': 2028.146, 'duration': 7.624}], 'summary': 'The importance of date time in data manipulation and creation.', 'duration': 39.434, 'max_score': 1996.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1996336.jpg'}], 'start': 1667.629, 'title': 'Working with date time index and date time', 'summary': 'Discusses working with date time index, converting date columns to datetime format, setting them as index, and using date time objects to create dates and measure time taken for execution, with examples using pandas. it emphasizes the importance of date time in manipulating date time data.', 'chapters': [{'end': 1874.22, 'start': 1667.629, 'title': 'Working with date time index', 'summary': 'Discusses working with date time index, including converting date columns to datetime format and setting them as index for better visualization, with examples using pandas and resetting and setting index.', 'duration': 206.591, 'highlights': ['The chapter explains how to convert a date column to datetime format using pandas.datetime, and the importance of setting it as an index for better visualization.', "The speaker demonstrates using pandas' reset_index and set_index functions to manipulate the date columns and enhance the dataset for visualization purposes.", 'The session emphasizes the significance of working with datetime data for stock analysis and offers practical guidance on setting and resetting index columns for effective plotting.']}, {'end': 2306.557, 'start': 1874.22, 'title': 'Working with date index and date time', 'summary': 'Discusses setting date index, using date time object to create dates and measure time taken for execution, emphasizing the importance of date time in manipulating date time data.', 'duration': 432.337, 'highlights': ['Using date time object to create dates with parameters like year, month, day, hour, minutes, seconds, microseconds, and measuring time taken for function execution. The speaker demonstrates the process of creating a date using the date time object with specific parameters and measures the time taken for function execution.', 'Emphasizing the importance of date time in manipulating date time data and its role in playing with and manipulating the date time data. The speaker highlights the crucial role of date time in manipulating date time data and its significance in playing and manipulating the data.', 'Discussing the process of setting the date index, dropping columns, and using in-place operations to manipulate the dataframe. The speaker explains the process of setting the date index, dropping columns, and using in-place operations to manipulate the dataframe.']}], 'duration': 638.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE1667629.jpg', 'highlights': ['The chapter explains how to convert a date column to datetime format using pandas.datetime, and the importance of setting it as an index for better visualization.', 'The session emphasizes the significance of working with datetime data for stock analysis and offers practical guidance on setting and resetting index columns for effective plotting.', 'Using date time object to create dates with parameters like year, month, day, hour, minutes, seconds, microseconds, and measuring time taken for function execution.', 'Emphasizing the importance of date time in manipulating date time data and its role in playing with and manipulating the date time data.', 'Discussing the process of setting the date index, dropping columns, and using in-place operations to manipulate the dataframe.']}, {'end': 2847.074, 'segs': [{'end': 2331.934, 'src': 'embed', 'start': 2306.557, 'weight': 2, 'content': [{'end': 2315.162, 'text': 'so if you go and probably write date dot date and probably call this as a function, so here you will be able to see this is my entire date.', 'start': 2306.557, 'duration': 8.605}, {'end': 2325.368, 'text': 'okay, if you write date dot day, then here it will be int object is not callable, okay, sorry.', 'start': 2315.162, 'duration': 10.206}, {'end': 2331.934, 'text': "So if you probably write date.day here you are able to see that 21st is the day and I'm able to get the answer.", 'start': 2326.212, 'duration': 5.722}], 'summary': 'Accessing date information using python, 21st is the day.', 'duration': 25.377, 'max_score': 2306.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2306557.jpg'}, {'end': 2584.431, 'src': 'heatmap', 'start': 2392.912, 'weight': 0, 'content': [{'end': 2399.536, 'text': 'so till here, date time index, i hope i have given you a very routine for things to basically follow in date time index.', 'start': 2392.912, 'duration': 6.624}, {'end': 2404.26, 'text': 'now what we are going to learn about is something called as time resampling.', 'start': 2399.536, 'duration': 4.724}, {'end': 2406.682, 'text': 'this is quite amazing, quite important.', 'start': 2404.26, 'duration': 2.422}, {'end': 2408.763, 'text': 'okay, with respect to resampling.', 'start': 2406.682, 'duration': 2.081}, {'end': 2417.95, 'text': "so i'm just going to make this as a markdown and here i'm going to create a lot of cells.", 'start': 2408.763, 'duration': 9.187}, {'end': 2424.294, 'text': "okay, Now, first thing, first I'll write df underscore, Tesla dot head.", 'start': 2417.95, 'duration': 6.344}, {'end': 2429.06, 'text': 'Obviously, you know that I have this entire information with respect to df dot Tesla.', 'start': 2424.975, 'duration': 4.085}, {'end': 2432.465, 'text': "I've already shown you set index and reset index.", 'start': 2429.681, 'duration': 2.784}, {'end': 2435.348, 'text': "Now let's go and work with respect to resample.", 'start': 2432.565, 'duration': 2.783}, {'end': 2444.198, 'text': 'Okay Now, what is this resample? And probably I have was like there are a lot of options with respect to resample.', 'start': 2435.769, 'duration': 8.429}, {'end': 2447.621, 'text': 'I just want to show you one specific article.', 'start': 2444.718, 'duration': 2.903}, {'end': 2450.224, 'text': 'OK, this was from Towards Data Science.', 'start': 2447.901, 'duration': 2.323}, {'end': 2452.025, 'text': 'OK, Towards Data Science.', 'start': 2450.844, 'duration': 1.181}, {'end': 2455.449, 'text': 'Some person had actually written like Solani Mishra.', 'start': 2452.065, 'duration': 3.384}, {'end': 2462.555, 'text': 'a good article altogether, where you have a lot of options, which is called as data offset options.', 'start': 2456.79, 'duration': 5.765}, {'end': 2467.539, 'text': 'okay, this all options you can basically apply in your resampling techniques.', 'start': 2462.555, 'duration': 4.984}, {'end': 2468.8, 'text': 'let me just show you some of them.', 'start': 2467.539, 'duration': 1.261}, {'end': 2473.723, 'text': "okay, so uh, and then we'll probably discuss about some of the sampling techniques.", 'start': 2468.8, 'duration': 4.923}, {'end': 2481.45, 'text': 'if i probably write df, underscore, tesla dot, resample, okay.', 'start': 2473.723, 'duration': 7.727}, {'end': 2485.011, 'text': 'and here, if i go and see resample, okay.', 'start': 2481.45, 'duration': 3.561}, {'end': 2492.732, 'text': 'here there is something called a rule access and all information are there rule, the offset string or object representing the target conversion.', 'start': 2485.011, 'duration': 7.721}, {'end': 2493.473, 'text': "i'll talk about what.", 'start': 2492.732, 'duration': 0.741}, {'end': 2495.373, 'text': 'all different, different rules are there.', 'start': 2493.473, 'duration': 1.9}, {'end': 2498.074, 'text': "let's say that i'm going to use a rule which is called as a.", 'start': 2495.373, 'duration': 2.701}, {'end': 2502.691, 'text': "I'll tell you what exactly is A.", 'start': 2500.13, 'duration': 2.561}, {'end': 2505.753, 'text': 'So rule, there is like A, B, different different rule is there.', 'start': 2502.691, 'duration': 3.062}, {'end': 2512.296, 'text': "So if I execute this, here you'll be able to see that we are getting as an output called as DateTimeIndexResampler.", 'start': 2506.113, 'duration': 6.183}, {'end': 2517.618, 'text': 'DateTimeIndexResampler. Suppose, if I say min.', 'start': 2513.636, 'duration': 3.982}, {'end': 2521.82, 'text': 'now, this just see, focus over here.', 'start': 2519.239, 'duration': 2.581}, {'end': 2523.261, 'text': 'min is an aggregate function.', 'start': 2521.82, 'duration': 1.441}, {'end': 2524.942, 'text': 'okay, what it is going to do?', 'start': 2523.261, 'duration': 1.681}, {'end': 2525.642, 'text': 'you just see this.', 'start': 2524.942, 'duration': 0.7}, {'end': 2527.683, 'text': 'okay, we already have five years data.', 'start': 2525.642, 'duration': 2.041}, {'end': 2530.144, 'text': 'okay, five years with respect to everyday data.', 'start': 2527.683, 'duration': 2.461}, {'end': 2534.847, 'text': "okay, now, if i execute this here, you'll be able to see everybody focus over here.", 'start': 2530.144, 'duration': 4.703}, {'end': 2542.229, 'text': 'Now you will be able to see only 5 data and every year, last day is there.', 'start': 2535.527, 'duration': 6.702}, {'end': 2550.352, 'text': 'and for the entire, which was the minimum high, which was the minimum low, which was the minimum open price, which was the minimum close price,', 'start': 2542.229, 'duration': 8.123}, {'end': 2555.654, 'text': 'which was the minimum number of stocks or shares that were traded, which was the minimum adjusted column.', 'start': 2550.352, 'duration': 5.302}, {'end': 2562.337, 'text': 'So this entire information is getting get retrieved based on the minimum throughout the entire year.', 'start': 2555.994, 'duration': 6.343}, {'end': 2568.121, 'text': 'so this is an amazing way to basically find out the data like in a specific year.', 'start': 2562.337, 'duration': 5.784}, {'end': 2576.546, 'text': "here, suppose, if i want to find out what's the what was the maximum maximum cap maximum share price that had gone for those specific years?", 'start': 2568.121, 'duration': 8.425}, {'end': 2578.167, 'text': 'so here in 2017, you can see that.', 'start': 2576.546, 'duration': 1.621}, {'end': 2579.748, 'text': 'okay, fine, i had, uh, 77.', 'start': 2578.167, 'duration': 1.581}, {'end': 2584.431, 'text': 'i had 77 over here with respect to 2018.', 'start': 2579.748, 'duration': 4.683}], 'summary': 'Introduces time resampling and demonstrates resampling techniques using dataframe tesla data, showcasing minimum and maximum values for specific years.', 'duration': 25.038, 'max_score': 2392.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2392912.jpg'}, {'end': 2568.121, 'src': 'embed', 'start': 2542.229, 'weight': 1, 'content': [{'end': 2550.352, 'text': 'and for the entire, which was the minimum high, which was the minimum low, which was the minimum open price, which was the minimum close price,', 'start': 2542.229, 'duration': 8.123}, {'end': 2555.654, 'text': 'which was the minimum number of stocks or shares that were traded, which was the minimum adjusted column.', 'start': 2550.352, 'duration': 5.302}, {'end': 2562.337, 'text': 'So this entire information is getting get retrieved based on the minimum throughout the entire year.', 'start': 2555.994, 'duration': 6.343}, {'end': 2568.121, 'text': 'so this is an amazing way to basically find out the data like in a specific year.', 'start': 2562.337, 'duration': 5.784}], 'summary': 'Retrieve minimum high, low, open, close prices, stocks traded, and adjusted column data for a specific year.', 'duration': 25.892, 'max_score': 2542.229, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2542229.jpg'}, {'end': 2688.627, 'src': 'embed', 'start': 2661.965, 'weight': 4, 'content': [{'end': 2671.899, 'text': 'probably i will be able to see that Rule A basically means year end frequency.', 'start': 2661.965, 'duration': 9.934}, {'end': 2679.163, 'text': 'So if I want to talk about this, A basically means year end frequency.', 'start': 2672.459, 'duration': 6.704}, {'end': 2683.185, 'text': 'So that basically means the end of the year data you are basically going to start.', 'start': 2679.203, 'duration': 3.982}, {'end': 2685.446, 'text': 'Suppose you want quarterly.', 'start': 2683.565, 'duration': 1.881}, {'end': 2688.627, 'text': 'So I will also show you that specific code.', 'start': 2686.426, 'duration': 2.201}], 'summary': 'Rule a represents year-end frequency for data analysis.', 'duration': 26.662, 'max_score': 2661.965, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2661965.jpg'}], 'start': 2306.557, 'title': 'Working with date and time in python and aggregate functions in data analysis', 'summary': 'Covers functionalities of the date and time module in python including accessing day, week, year attributes, and time resampling in data analysis. it also discusses the use of min aggregate function to retrieve minimum values for stock data attributes and the use of frequency codes for trend analysis.', 'chapters': [{'end': 2517.618, 'start': 2306.557, 'title': 'Working with date and time in python', 'summary': 'Covers various functionalities of the date and time module in python, such as accessing day, week, year, and other attributes of a date object, as well as introducing the concept of time resampling in data analysis, including discussing resampling techniques and rules, with practical examples.', 'duration': 211.061, 'highlights': ['The chapter covers various functionalities of the date and time module in Python, such as accessing day, week, year, and other attributes of a date object, as well as introducing the concept of time resampling in data analysis, including discussing resampling techniques and rules, with practical examples.', "The speaker demonstrates accessing different attributes of a date object in Python, such as day, weekday, and year, providing a practical insight into working with date and time data (e.g., '21st is the day,' '6th day of the week,' '2021 is the year').", "The concept of time resampling in data analysis is introduced, along with a demonstration of resampling techniques and rules, including discussing options such as data offset options and different resampling rules (e.g., 'A,' 'B'), showcasing practical applications and outputs of resampling operations in Python."]}, {'end': 2847.074, 'start': 2519.239, 'title': 'Aggregate functions in data analysis', 'summary': "Discusses the use of the min aggregate function to retrieve minimum values for different stock data attributes throughout the years, and demonstrates the use of different frequency codes such as 'a' for year-end frequency and 'q' for quarterly start frequency to analyze stock data trends.", 'duration': 327.835, 'highlights': ['The min aggregate function is used to retrieve minimum values for various stock data attributes, such as high, low, open price, close price, number of stocks traded, and adjusted column, across the entire year, providing insights into the stock performance. This is demonstrated with specific examples and values for different years, highlighting the changes in stock prices over time.', "The use of frequency codes such as 'A' for year-end frequency and 'Q' for quarterly start frequency allows for in-depth analysis of stock data trends, enabling the visualization of stock price fluctuations over different time periods, and providing valuable insights into the stock market trends.", "The discussion also includes examples of other frequency codes like 'B' for business year-end frequency and 'BQS' for business quarter start frequency, showcasing the versatility of frequency coding in analyzing stock data trends and providing insights into business cycles and stock market performance."]}], 'duration': 540.517, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2306557.jpg', 'highlights': ['The chapter covers various functionalities of the date and time module in Python, such as accessing day, week, year, and other attributes of a date object, as well as introducing the concept of time resampling in data analysis, including discussing resampling techniques and rules, with practical examples.', 'The min aggregate function is used to retrieve minimum values for various stock data attributes, such as high, low, open price, close price, number of stocks traded, and adjusted column, across the entire year, providing insights into the stock performance. This is demonstrated with specific examples and values for different years, highlighting the changes in stock prices over time.', "The speaker demonstrates accessing different attributes of a date object in Python, such as day, weekday, and year, providing a practical insight into working with date and time data (e.g., '21st is the day,' '6th day of the week,' '2021 is the year').", "The concept of time resampling in data analysis is introduced, along with a demonstration of resampling techniques and rules, including discussing options such as data offset options and different resampling rules (e.g., 'A,' 'B'), showcasing practical applications and outputs of resampling operations in Python.", "The use of frequency codes such as 'A' for year-end frequency and 'Q' for quarterly start frequency allows for in-depth analysis of stock data trends, enabling the visualization of stock price fluctuations over different time periods, and providing valuable insights into the stock market trends.", "The discussion also includes examples of other frequency codes like 'B' for business year-end frequency and 'BQS' for business quarter start frequency, showcasing the versatility of frequency coding in analyzing stock data trends and providing insights into business cycles and stock market performance."]}, {'end': 3550.218, 'segs': [{'end': 2908.945, 'src': 'embed', 'start': 2884.994, 'weight': 0, 'content': [{'end': 2894.599, 'text': 'So if I take up DF Tesla and probably plot the open, I can also take a single single column and already do resample of this.', 'start': 2884.994, 'duration': 9.605}, {'end': 2896.74, 'text': 'Take the mean max.', 'start': 2895.459, 'duration': 1.281}, {'end': 2903.863, 'text': 'Suppose if I want the mean, I can also take the mean dot plot and I can use a bar plot over here.', 'start': 2896.78, 'duration': 7.083}, {'end': 2908.945, 'text': 'So specifically if I write kind is equal to bar, you will be able to see this kind of plots also.', 'start': 2903.903, 'duration': 5.042}], 'summary': 'Using python, resample the open data of df tesla to calculate mean max and create bar plots.', 'duration': 23.951, 'max_score': 2884.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2884994.jpg'}, {'end': 2993.851, 'src': 'embed', 'start': 2964.385, 'weight': 1, 'content': [{'end': 2969.408, 'text': "So here I'm actually going to plot with respect to the kind of bar chart.", 'start': 2964.385, 'duration': 5.023}, {'end': 2974.182, 'text': 'So here you can basically see how the data looks like.', 'start': 2971.701, 'duration': 2.481}, {'end': 2982.326, 'text': 'Oh my god, this is really, really, really, really very much completely integrated.', 'start': 2974.262, 'duration': 8.064}, {'end': 2984.827, 'text': "But again, don't worry, we can actually fix this.", 'start': 2982.366, 'duration': 2.461}, {'end': 2987.208, 'text': "Here I'm just going to use figure size.", 'start': 2985.387, 'duration': 1.821}, {'end': 2993.851, 'text': "Okay, I'm just going to use figure size and make this plot a little bit beautiful.", 'start': 2987.228, 'duration': 6.623}], 'summary': 'Plotting bar chart to improve data visualization.', 'duration': 29.466, 'max_score': 2964.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2964385.jpg'}, {'end': 3115.434, 'src': 'embed', 'start': 3064.812, 'weight': 5, 'content': [{'end': 3073.016, 'text': "all the graphs data, because whenever i've seen people working with time series data right, they they're really afraid.", 'start': 3064.812, 'duration': 8.204}, {'end': 3084.982, 'text': "i don't know why, because it's good, like the library is quite strong, arima and all.", 'start': 3073.016, 'duration': 11.966}, {'end': 3088.745, 'text': 'tomorrow it will happen this today, only i, without doing eda.', 'start': 3084.982, 'duration': 3.763}, {'end': 3097.208, 'text': "if i'd show you forecasting, then done, no, everybody will leave the class and go.", 'start': 3088.745, 'duration': 8.463}, {'end': 3115.434, 'text': 'okay, okay, okay, okay.', 'start': 3097.208, 'duration': 18.226}], 'summary': "The speaker discusses time series data and forecasting using arima, expressing surprise at people's fear of working with it.", 'duration': 50.622, 'max_score': 3064.812, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3064812.jpg'}, {'end': 3307.912, 'src': 'heatmap', 'start': 3215.453, 'weight': 0.965, 'content': [{'end': 3225.14, 'text': 'so if i write rolling as 10 and probably if i write dot, then i can apply an aggregate function and try to find out the mean.', 'start': 3215.453, 'duration': 9.687}, {'end': 3229.803, 'text': 'so here you can see that initially.', 'start': 3225.14, 'duration': 4.663}, {'end': 3235.107, 'text': 'let me just show you df of underscore tesla dot head.', 'start': 3229.803, 'duration': 5.304}, {'end': 3236.708, 'text': 'now what is happening?', 'start': 3235.107, 'duration': 1.601}, {'end': 3239.45, 'text': "i'm just trying to find out the rolling mean.", 'start': 3236.708, 'duration': 2.742}, {'end': 3243.673, 'text': 'that basically means initially, when i go one step down, that basically means this two.', 'start': 3239.45, 'duration': 4.223}, {'end': 3246.543, 'text': "We'll just move one step down.", 'start': 3245.182, 'duration': 1.361}, {'end': 3249.405, 'text': "We'll make the mean of this and we'll try to find out the answer.", 'start': 3247.024, 'duration': 2.381}, {'end': 3254.369, 'text': 'Then when we go down, then again, the mean of this two will get calculated and we are going to go down.', 'start': 3250.026, 'duration': 4.343}, {'end': 3258.233, 'text': 'Similarly, when we go down, we are going to calculate the mean and go down.', 'start': 3254.93, 'duration': 3.303}, {'end': 3265.338, 'text': 'Okay So this is specifically happening with respect to the high column that is actually shown over here.', 'start': 3258.413, 'duration': 6.925}, {'end': 3269.702, 'text': "Okay So after 10 records, probably you'll be able to see all these values as you go down.", 'start': 3265.759, 'duration': 3.943}, {'end': 3278.688, 'text': 'So if I write dot, head of 20 here, you will be able to see the main different different mean values.', 'start': 3270.162, 'duration': 8.526}, {'end': 3281.789, 'text': 'as you keep on going down, you make the values as nan.', 'start': 3278.688, 'duration': 3.101}, {'end': 3288.972, 'text': 'so if i probably make 11, then you can see that these two values mean will get calculated and this value will become nan.', 'start': 3281.789, 'duration': 7.183}, {'end': 3291.913, 'text': 'if i, if i want to show you, i can show you here also.', 'start': 3288.972, 'duration': 2.941}, {'end': 3297.175, 'text': 'so see, for this particular data, 2017, 310, this value will now become nan.', 'start': 3291.913, 'duration': 5.262}, {'end': 3303.057, 'text': 'okay. so here you can see this is value has become nan because now we have calculated the mean and we have gone down.', 'start': 3297.175, 'duration': 5.882}, {'end': 3307.912, 'text': 'okay. so this is an example with respect to the rolling mean.', 'start': 3303.669, 'duration': 4.243}], 'summary': 'Using rolling mean with a window of 10, calculates mean values as data goes down, resulting in nan values at specific points.', 'duration': 92.459, 'max_score': 3215.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3215453.jpg'}, {'end': 3338.229, 'src': 'embed', 'start': 3310.633, 'weight': 4, 'content': [{'end': 3314.776, 'text': 'you can try to get different, different aggregate functions and you can actually do it.', 'start': 3310.633, 'duration': 4.143}, {'end': 3319.199, 'text': 'okay. so this was an example with respect to, uh, rolling.', 'start': 3314.776, 'duration': 4.423}, {'end': 3325.443, 'text': 'similarly, you can do it for any value of rollings, okay, like 30, 40, 50, it is up to you.', 'start': 3319.199, 'duration': 6.244}, {'end': 3335.784, 'text': 'okay, clear, everybody, everybody, clear.', 'start': 3325.443, 'duration': 10.341}, {'end': 3338.229, 'text': "now let's go ahead and see one amazing thing.", 'start': 3335.784, 'duration': 2.445}], 'summary': 'Example of using different aggregate functions for various rolling values.', 'duration': 27.596, 'max_score': 3310.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3310633.jpg'}, {'end': 3550.218, 'src': 'embed', 'start': 3525.552, 'weight': 3, 'content': [{'end': 3531.433, 'text': 'It is rolling down by 10 steps, okay? See the inbuilt function definition over here.', 'start': 3525.552, 'duration': 5.881}, {'end': 3535.974, 'text': 'If I probably go and see, provides rolling windows calculation.', 'start': 3532.473, 'duration': 3.501}, {'end': 3539.415, 'text': 'And that is what we specifically do in moving average.', 'start': 3536.154, 'duration': 3.261}, {'end': 3543.736, 'text': 'okay, so that is the reason why we specifically are doing that.', 'start': 3540.055, 'duration': 3.681}, {'end': 3544.616, 'text': 'so here you can.', 'start': 3543.736, 'duration': 0.88}, {'end': 3550.218, 'text': 'that is the reason you cannot see the starting lines.', 'start': 3544.616, 'duration': 5.602}], 'summary': 'Rolling down by 10 steps for moving average calculation.', 'duration': 24.666, 'max_score': 3525.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3525552.jpg'}], 'start': 2847.074, 'title': 'Data visualization and time series analysis', 'summary': 'Demonstrates using pandas for data visualization, including plotting open prices and creating bar charts, and covers rolling and expanding in time series data analysis using the rolling function for calculating mean and smoothening the data, preparing students for assignments and forecasting.', 'chapters': [{'end': 3023.354, 'start': 2847.074, 'title': 'Plotting data with pandas: visualization', 'summary': 'Demonstrates using pandas to plot and visualize data, including plotting open prices, resampling data, and creating bar charts to analyze trends and patterns in the data.', 'duration': 176.28, 'highlights': ['Using Pandas to plot open prices and perform resampling to analyze data trends.', 'Creating bar charts to visualize data and identify patterns and trends.', 'Adjusting plot aesthetics with figure size to enhance visualization and analysis.']}, {'end': 3550.218, 'start': 3024.781, 'title': 'Time series data analysis with rolling function', 'summary': 'Covers the concept of rolling and expanding in time series data analysis, using the rolling function to calculate mean and smoothen the data, with examples and explanations, preparing students for assignments and forecasting.', 'duration': 525.437, 'highlights': ['The rolling function is used to calculate the rolling mean of time series data by specifying a window size, allowing for smoothening and data analysis, preparing students for forecasting and assignments.', 'The rolling function can be applied to different columns and different window sizes, enabling the calculation of rolling mean for various parameters and improving data analysis skills.', 'The concept of rolling in time series data analysis provides a way to understand and analyze data trends, enabling students to gain confidence in working with time series data and libraries like ARIMA.']}], 'duration': 703.144, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE2847074.jpg', 'highlights': ['Using Pandas to plot open prices and perform resampling to analyze data trends.', 'Creating bar charts to visualize data and identify patterns and trends.', 'Adjusting plot aesthetics with figure size to enhance visualization and analysis.', 'The rolling function is used to calculate the rolling mean of time series data by specifying a window size, allowing for smoothening and data analysis, preparing students for forecasting and assignments.', 'The rolling function can be applied to different columns and different window sizes, enabling the calculation of rolling mean for various parameters and improving data analysis skills.', 'The concept of rolling in time series data analysis provides a way to understand and analyze data trends, enabling students to gain confidence in working with time series data and libraries like ARIMA.']}, {'end': 4529.538, 'segs': [{'end': 3753.976, 'src': 'embed', 'start': 3681.373, 'weight': 0, 'content': [{'end': 3684.515, 'text': 'first task assignment.', 'start': 3681.373, 'duration': 3.142}, {'end': 3700.855, 'text': 'okay, first task read the microsoft data using pandas data reader.', 'start': 3684.515, 'duration': 16.34}, {'end': 3727.147, 'text': 'second task get the maximum maximum price of the year from 2017 to 2022..', 'start': 3700.855, 'duration': 26.292}, {'end': 3730.848, 'text': 'This is the second task, get the maximum price of the year from 2017.', 'start': 3727.147, 'duration': 3.701}, {'end': 3736.449, 'text': 'Price of the stock of the share from 2017 to 2019.', 'start': 3730.848, 'duration': 5.601}, {'end': 3753.976, 'text': 'Which is the date of the highest price of the stock? out of all the five years?', 'start': 3736.449, 'duration': 17.527}], 'summary': 'Analyze microsoft stock data, find max price from 2017-2022, identify highest price date.', 'duration': 72.603, 'max_score': 3681.373, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3681373.jpg'}, {'end': 3987.165, 'src': 'embed', 'start': 3919.107, 'weight': 1, 'content': [{'end': 3923.509, 'text': 'sorry, in the description quickly, just let me know.', 'start': 3919.107, 'duration': 4.402}, {'end': 3927.331, 'text': 'just reload the page, everybody.', 'start': 3923.509, 'duration': 3.822}, {'end': 3934.975, 'text': 'just reload the page quickly.', 'start': 3927.331, 'duration': 7.644}, {'end': 3939.297, 'text': "great, okay, let's get some basic idea about time series data.", 'start': 3934.975, 'duration': 4.322}, {'end': 3954.487, 'text': 'The first thing that you really need to know is that what is upward trend?', 'start': 3950.165, 'duration': 4.322}, {'end': 3960.149, 'text': 'What is stationary data?', 'start': 3958.169, 'duration': 1.98}, {'end': 3966.692, 'text': 'What is downward trend?', 'start': 3965.352, 'duration': 1.34}, {'end': 3974.756, 'text': 'Then what is cyclic data?', 'start': 3972.815, 'duration': 1.941}, {'end': 3978.418, 'text': "Before we start tomorrow's session.", 'start': 3976.857, 'duration': 1.561}, {'end': 3987.165, 'text': 'Okay, now, remember, guys.', 'start': 3978.878, 'duration': 8.287}], 'summary': "Introduction to time series data: upward trend, stationary data, downward trend, cyclic data. ready for tomorrow's session.", 'duration': 68.058, 'max_score': 3919.107, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3919107.jpg'}, {'end': 4216.094, 'src': 'heatmap', 'start': 4165.671, 'weight': 0.855, 'content': [{'end': 4168.794, 'text': 'And this stationary data will actually help us to do many things.', 'start': 4165.671, 'duration': 3.123}, {'end': 4176.379, 'text': "You'll be seeing that later on when we have the specific data, we'll try to convert this into stationary data and then we'll further do forecasting.", 'start': 4169.274, 'duration': 7.105}, {'end': 4181.166, 'text': "okay, then we'll further do forecasting.", 'start': 4178.024, 'duration': 3.142}, {'end': 4187.269, 'text': 'okay, so definitely, you will be able to see that later on, and it is a very good reason.', 'start': 4181.166, 'duration': 6.103}, {'end': 4191.772, 'text': 'why do we make a seasonal data as a stationary data in order to do the forecasting?', 'start': 4187.269, 'duration': 4.503}, {'end': 4193.593, 'text': 'because it will give us a very good result.', 'start': 4191.772, 'duration': 1.821}, {'end': 4196.034, 'text': 'okay, so this is what forecasting is all about.', 'start': 4193.593, 'duration': 2.441}, {'end': 4198.356, 'text': 'uh, and not only this.', 'start': 4196.034, 'duration': 2.322}, {'end': 4201.618, 'text': "for the other data also, we'll try to see how forecasting will be done.", 'start': 4198.356, 'duration': 3.262}, {'end': 4212.433, 'text': 'then, one more data that i specifically want to talk about is One more data that I specifically want to talk about is your cyclic data.', 'start': 4201.618, 'duration': 10.815}, {'end': 4216.094, 'text': 'Now can anybody tell me what exactly is this cyclic data?', 'start': 4213.253, 'duration': 2.841}], 'summary': 'Using stationary data for forecasting gives good results, applicable to seasonal and cyclic data.', 'duration': 50.423, 'max_score': 4165.671, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE4165671.jpg'}, {'end': 4242.749, 'src': 'embed', 'start': 4217.095, 'weight': 2, 'content': [{'end': 4222.297, 'text': 'Any data, like your stocks, which will be increasing and decreasing like this and going up right?', 'start': 4217.095, 'duration': 5.202}, {'end': 4225.958, 'text': 'So here you will be able to see there are ups and downs.', 'start': 4222.977, 'duration': 2.981}, {'end': 4227.039, 'text': 'suddenly it may go up.', 'start': 4225.958, 'duration': 1.081}, {'end': 4228.159, 'text': 'suddenly it may go down.', 'start': 4227.039, 'duration': 1.12}, {'end': 4231.5, 'text': 'So this kind of data is basically called a cyclic data.', 'start': 4228.579, 'duration': 2.921}, {'end': 4235.502, 'text': 'And the most difficult part of the forecasting is this specific data.', 'start': 4232.02, 'duration': 3.482}, {'end': 4242.749, 'text': 'okay, even though i create any kind of models, i cannot guarantee you that my forecasting is absolutely right.', 'start': 4235.982, 'duration': 6.767}], 'summary': 'Forecasting cyclic stock data is challenging and uncertain.', 'duration': 25.654, 'max_score': 4217.095, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE4217095.jpg'}, {'end': 4336.531, 'src': 'embed', 'start': 4308.959, 'weight': 3, 'content': [{'end': 4311.62, 'text': "We'll be discussing about error trend seasonality.", 'start': 4308.959, 'duration': 2.661}, {'end': 4313.862, 'text': "We'll be discussing about ARIMA.", 'start': 4312.181, 'duration': 1.681}, {'end': 4315.803, 'text': "We'll be discussing about ACF.", 'start': 4313.922, 'duration': 1.881}, {'end': 4321.405, 'text': "We'll discuss about PACF autocorrelation plot, partial autocorrelation plot,", 'start': 4316.363, 'duration': 5.042}, {'end': 4326.567, 'text': "and then we will be solving a lot of use cases with the help of Arimax and Serimax in tomorrow's session.", 'start': 4321.405, 'duration': 5.162}, {'end': 4332.69, 'text': "So today, the reason I'm going a little bit slow, because learn it and come.", 'start': 4327.408, 'duration': 5.282}, {'end': 4336.531, 'text': 'Whatever things we have done, tomorrow there will be a lot of coding along with different maps.', 'start': 4333.23, 'duration': 3.301}], 'summary': "Discussion of error trend seasonality, arima, acf, pacf, arimax, and serimax for use cases in tomorrow's session.", 'duration': 27.572, 'max_score': 4308.959, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE4308959.jpg'}], 'start': 3550.218, 'title': 'Time series data analysis basics', 'summary': 'Covers tasks such as reading microsoft data using pandas data reader, finding the maximum price of the year from 2017 to 2022, identifying the date of the highest and lowest stock prices, and understanding basic concepts related to time series data such as upward trend, stationary data, downward trend, and cyclic data.', 'chapters': [{'end': 3681.373, 'start': 3550.218, 'title': 'Tesla dataset analysis', 'summary': 'Covers the analysis of tesla dataset using pandas data reader, with a focus on eda and theoretical information, along with the possibility of providing assignments for the audience.', 'duration': 131.155, 'highlights': ['The chapter covers the analysis of Tesla dataset using Pandas data reader. The instructor reads the dataset of Tesla using Pandas data reader.', 'Focus on EDA and theoretical information. The chapter includes discussions about EDA and theoretical information related to the dataset analysis.', 'Possibility of providing assignments for the audience. The instructor considers providing assignments and seeks feedback from the audience about their interest in doing assignments.']}, {'end': 4140.064, 'start': 3681.373, 'title': 'Time series data analysis basics', 'summary': 'Covers tasks including reading microsoft data using pandas data reader, finding the maximum price of the year from 2017 to 2022, identifying the date of the highest and lowest stock prices, and understanding basic concepts related to time series data such as upward trend, stationary data, downward trend, and cyclic data.', 'duration': 458.691, 'highlights': ['The chapter covers tasks including reading Microsoft data using pandas data reader, finding the maximum price of the year from 2017 to 2022, identifying the date of the highest and lowest stock prices, and understanding basic concepts related to time series data such as upward trend, stationary data, downward trend, and cyclic data.', 'The mean of the upward trend in the sales data will keep on increasing as time progresses, with almost the same variance and a consistent upward movement.', 'Understanding the concepts of upward trend, stationary data, downward trend, and cyclic data is crucial for analyzing time series data and identifying patterns and trends.']}, {'end': 4375.352, 'start': 4142.486, 'title': 'Understanding time series data', 'summary': 'Discusses the concepts of stationary, cyclic, and seasonal data in time series analysis, emphasizing the challenges in forecasting cyclic data and the potential for forecasting sales data. it also outlines the topics to be covered in the next session, including rolling window, exponential weight moving average, arima, acf, pacf, autocorrelation plot, partial autocorrelation plot, and use cases with arimax and serimax.', 'duration': 232.866, 'highlights': ["The specific types of time series data, including stationary, cyclic, and seasonal data, are explained, with an emphasis on the challenges in forecasting cyclic data due to its unpredictable nature and dependence on external factors such as people's sentiments and economic conditions.", 'The potential for forecasting sales data is highlighted, indicating that forecasting can be done effectively in this context, setting it apart from the challenges posed by forecasting cyclic data.', 'The upcoming topics for the next session are outlined, including discussions on rolling window, exponential weight moving average, ARIMA, ACF, PACF, autocorrelation plot, partial autocorrelation plot, and solving use cases with Arimax and Serimax, with an emphasis on practical applications and coding exercises.', 'The limitations of the current session are acknowledged, with the explanation that deep learning concepts such as LSTM and bi-directional LSTM will not be covered due to the need for a deeper understanding of these concepts before demonstrating the associated code.']}, {'end': 4529.538, 'start': 4375.352, 'title': 'Data science updates and course offer', 'summary': "Discussed updates on fb profit library, announced limited lifetime access to the one neuron course with 230+ courses, and emphasized using code 'krishna' for 10% discount before the offer ends.", 'duration': 154.186, 'highlights': ["Limited Lifetime Access to One Neuron Course: The course with 230+ courses, including popular youtuber courses, is available for a limited time, with a 10% discount using code 'Krishna'.", "Announcement of fb profit Library Usage: Tomorrow's session will cover the usage of fb profit library developed by Facebook for forecasting.", 'Promotion of YouTube Channel and Data Science Videos: Encouraged subscribing to Krishna Hindichi YouTube channel for data science videos and updates.', 'Urging Collaboration and Course Enrollment: Emphasized collaborating with popular youtubers and urged viewers to enroll in courses before the limited lifetime access offer ends.', 'Encouraging Engagement and Sharing: Encouraged engagement by hitting like, sharing on LinkedIn, and announced the upcoming session at 7 p.m.', "Reminder to Act Quickly for Discount: Urged viewers to use code 'Krishna' for 10% more discount and highlighted the limited time offer for the course, expressing the potential loss if not availed."]}], 'duration': 979.32, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/W-YCKMNTcqE/pics/W-YCKMNTcqE3550218.jpg', 'highlights': ['The chapter covers tasks including reading Microsoft data using pandas data reader, finding the maximum price of the year from 2017 to 2022, identifying the date of the highest and lowest stock prices, and understanding basic concepts related to time series data such as upward trend, stationary data, downward trend, and cyclic data.', 'Understanding the concepts of upward trend, stationary data, downward trend, and cyclic data is crucial for analyzing time series data and identifying patterns and trends.', "The specific types of time series data, including stationary, cyclic, and seasonal data, are explained, with an emphasis on the challenges in forecasting cyclic data due to its unpredictable nature and dependence on external factors such as people's sentiments and economic conditions.", 'The upcoming topics for the next session are outlined, including discussions on rolling window, exponential weight moving average, ARIMA, ACF, PACF, autocorrelation plot, partial autocorrelation plot, and solving use cases with Arimax and Serimax, with an emphasis on practical applications and coding exercises.']}], 'highlights': ['The potential deep learning live session, spanning seven days, will provide in-depth understanding of implementing time series with the help of deep learning.', "The chapter will cover EDA, ETS, EWMA, ARIMA, SARIMAX, ACF, PSEF, and FB profit in tomorrow's session, which will involve extensive visualization and parameter exploration.", 'The speaker seeks audience engagement by encouraging viewers to like and share the upcoming videos with friends, emphasizing the desire for wider reach and interaction.', 'The speaker expresses excitement for the upcoming time series EDM live sessions, indicating a new content direction for the YouTube channel.', 'The third day will focus on a machine learning project with time series data, utilizing ARIMAX or SARIMAX to solve stock forecasting and sales forecasting problems.', 'Pandas data reader provides remote data access for financial data from multiple platforms', 'The process of installing pandas data reader and importing necessary libraries for reading financial time series data is described', 'The necessity of an API key for accessing financial data is highlighted, with an explanation of how to read the data without requiring an API key', 'The different data sources available through pandas data reader, including Bank of Canada, Yahoo, Quandl, and others, are mentioned', 'Accessing stock data from sources like iex, google finance, and yahoo finance, offering flexibility to choose any stock data.', 'Utilizing a single line of code to retrieve stock data for companies like Tesla from the past five years, providing a quick and efficient method for retrieving historical stock data.', 'The chapter covers analyzing stock price data, including high, low, open, close, and volume, providing comprehensive insights into stock data analysis.', 'Plotting time series data to visualize trends and fluctuations, showcasing the increase and recent decrease in Tesla stock price, prompting audience interaction to identify the reason for the recent decrease.', 'The chapter emphasizes the need for peace in the Russia-Ukraine conflict and mentions the impact on prices.', 'The tutorial provides detailed instructions on setting x and y limits for data visualization, with specific examples and code snippets.', 'The chapter covers data visualization with Matplotlib, including applying coloring, using different parameters, plotting subplots, and preventing overlapping in the plot.', 'The chapter explains how to convert a date column to datetime format using pandas.datetime, and the importance of setting it as an index for better visualization.', 'The session emphasizes the significance of working with datetime data for stock analysis and offers practical guidance on setting and resetting index columns for effective plotting.', 'The chapter covers various functionalities of the date and time module in Python, such as accessing day, week, year, and other attributes of a date object, as well as introducing the concept of time resampling in data analysis, including discussing resampling techniques and rules, with practical examples.', 'The min aggregate function is used to retrieve minimum values for various stock data attributes, such as high, low, open price, close price, number of stocks traded, and adjusted column, across the entire year, providing insights into the stock performance. This is demonstrated with specific examples and values for different years, highlighting the changes in stock prices over time.', 'Using Pandas to plot open prices and perform resampling to analyze data trends.', 'Creating bar charts to visualize data and identify patterns and trends.', 'Adjusting plot aesthetics with figure size to enhance visualization and analysis.', 'The rolling function is used to calculate the rolling mean of time series data by specifying a window size, allowing for smoothening and data analysis, preparing students for forecasting and assignments.', 'The chapter covers tasks including reading Microsoft data using pandas data reader, finding the maximum price of the year from 2017 to 2022, identifying the date of the highest and lowest stock prices, and understanding basic concepts related to time series data such as upward trend, stationary data, downward trend, and cyclic data.', 'Understanding the concepts of upward trend, stationary data, downward trend, and cyclic data is crucial for analyzing time series data and identifying patterns and trends.', "The specific types of time series data, including stationary, cyclic, and seasonal data, are explained, with an emphasis on the challenges in forecasting cyclic data due to its unpredictable nature and dependence on external factors such as people's sentiments and economic conditions.", 'The upcoming topics for the next session are outlined, including discussions on rolling window, exponential weight moving average, ARIMA, ACF, PACF, autocorrelation plot, partial autocorrelation plot, and solving use cases with Arimax and Serimax, with an emphasis on practical applications and coding exercises.']}