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Live Day 3- Intermediate Statistics With Python In Data Science

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{'title': 'Live Day 3- Intermediate Statistics With Python In Data Science', 'heatmap': [{'end': 1594.019, 'start': 1472.437, 'weight': 0.81}, {'end': 2330.24, 'start': 2100.808, 'weight': 0.763}, {'end': 4945.847, 'start': 4882.325, 'weight': 0.78}], 'summary': 'Covers intermediate statistics in data science with python, including distributions, visualizations, gaussian distribution, z-scores, standard deviation, and practical applications. it also discusses cricket score analysis using z-scores, percentage analysis, and data analysis techniques in python, aiming to enhance understanding and practical skills for data science.', 'chapters': [{'end': 252.977, 'segs': [{'end': 77.562, 'src': 'embed', 'start': 19.192, 'weight': 0, 'content': [{'end': 19.792, 'text': 'Hello guys.', 'start': 19.192, 'duration': 0.6}, {'end': 33.38, 'text': 'Am I audible? Am I audible everyone? Hello.', 'start': 20.273, 'duration': 13.107}, {'end': 50.793, 'text': 'hi, hi, hi, hi, hi, hello.', 'start': 47.25, 'duration': 3.543}, {'end': 63.065, 'text': "so we will be continuing the session, what we had left today, and we'll just wait for some time, probably to pick up some questions.", 'start': 50.793, 'duration': 12.272}, {'end': 69.711, 'text': 'you know, till then you just have to hit like okay, and today is the day three.', 'start': 63.065, 'duration': 6.646}, {'end': 72.374, 'text': 'okay, so we are.', 'start': 69.711, 'duration': 2.663}, {'end': 77.562, 'text': "I'm just waiting for everybody to join.", 'start': 74.8, 'duration': 2.762}], 'summary': 'Continuing session, awaiting questions, day three.', 'duration': 58.37, 'max_score': 19.192, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw19192.jpg'}, {'end': 183.905, 'src': 'embed', 'start': 158.845, 'weight': 1, 'content': [{'end': 164.046, 'text': 'that we can basically work with data set and all, and whatever concepts we have discussed till now,', 'start': 158.845, 'duration': 5.201}, {'end': 167.427, 'text': 'everything will be getting covered in those practical things right.', 'start': 164.046, 'duration': 3.381}, {'end': 174.094, 'text': 'So, yes, and this will again be going till Friday.', 'start': 168.227, 'duration': 5.867}, {'end': 183.905, 'text': 'probably everything that is required you know in data science with respect to data scientists or data analysts will try to cover up all the statistics part.', 'start': 174.094, 'duration': 9.811}], 'summary': 'Practical data science concepts covered till friday, including statistics for data scientists and analysts.', 'duration': 25.06, 'max_score': 158.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw158845.jpg'}], 'start': 19.192, 'title': 'Data science session and meeting prep', 'summary': 'Covers a data science session on distributions and python code, running until friday, and also discusses meeting preparation to start within two minutes of the last participant joining.', 'chapters': [{'end': 183.905, 'start': 19.192, 'title': 'Data science session: distributions and python code', 'summary': 'Covers the continuation of a data science session focusing on distributions and practical application of python code, aiming to cover all statistics relevant to data science professionals, and it will run until friday.', 'duration': 164.713, 'highlights': ['The session will cover all statistics relevant to data science professionals, and it will run until Friday.', 'The chapter focuses on discussing distributions and practical application of Python code, aiming to cover all statistics relevant to data science professionals.', 'The session will include writing a lot of Python code to help understand various concepts and work with datasets.']}, {'end': 252.977, 'start': 184.105, 'title': 'Meeting preparation and discussion recap', 'summary': 'Discussed the preparation for a meeting, including waiting for all participants to join, checking audio and visual connections, and recapping previous discussions, aiming to start the meeting within two minutes of the last participant joining.', 'duration': 68.872, 'highlights': ['The importance of waiting for all participants to join before starting the meeting, with a timeframe of two minutes mentioned', 'Emphasizing the need for participants to check their audio and visual connections before the meeting begins', 'Recapping previous discussions and seeking input from participants on the topics discussed in the previous meeting']}], 'duration': 233.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw19192.jpg', 'highlights': ['The session will cover all statistics relevant to data science professionals, and it will run until Friday.', 'The chapter focuses on discussing distributions and practical application of Python code, aiming to cover all statistics relevant to data science professionals.', 'The session will include writing a lot of Python code to help understand various concepts and work with datasets.', 'The importance of waiting for all participants to join before starting the meeting, with a timeframe of two minutes mentioned', 'Emphasizing the need for participants to check their audio and visual connections before the meeting begins', 'Recapping previous discussions and seeking input from participants on the topics discussed in the previous meeting']}, {'end': 950.491, 'segs': [{'end': 699.514, 'src': 'embed', 'start': 669.601, 'weight': 2, 'content': [{'end': 674.163, 'text': 'Distribution main purpose is to why this different,', 'start': 669.601, 'duration': 4.562}, {'end': 679.125, 'text': 'different kind of distributions are there? So that we can basically have some idea about a data set.', 'start': 674.163, 'duration': 4.962}, {'end': 683.486, 'text': 'Okay Now, first of all, when we discuss about Gaussian or normal distribution,', 'start': 679.305, 'duration': 4.181}, {'end': 687.248, 'text': 'most of the time you have seen this kind of distribution in this specific way.', 'start': 683.486, 'duration': 3.762}, {'end': 690.649, 'text': 'So here probably you have seen a bell curve.', 'start': 687.928, 'duration': 2.721}, {'end': 694.191, 'text': 'Okay Now this bell curve, this is my bell curve.', 'start': 690.849, 'duration': 3.342}, {'end': 699.514, 'text': 'Now they are very important information when I probably talk about this bell curve.', 'start': 695.231, 'duration': 4.283}], 'summary': 'Gaussian distribution provides important insights into data sets, often represented as a bell curve.', 'duration': 29.913, 'max_score': 669.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw669601.jpg'}, {'end': 747.552, 'src': 'embed', 'start': 719.586, 'weight': 0, 'content': [{'end': 726.414, 'text': 'If I have a distribution and probably this distribution follows this kind of bell curve,', 'start': 719.586, 'duration': 6.828}, {'end': 732.843, 'text': 'and one important property of this bell curve is that this side is exactly symmetrical to this side.', 'start': 726.414, 'duration': 6.429}, {'end': 740.205, 'text': 'Okay So there are many inferential statistics that we will probably be discussing about in the future.', 'start': 733.439, 'duration': 6.766}, {'end': 747.552, 'text': 'About this bell curve, about this entire distribution or Gaussian distribution, here you can see that, it is exactly similar.', 'start': 740.986, 'duration': 6.566}], 'summary': 'The bell curve distribution is symmetrical and will be discussed further in inferential statistics.', 'duration': 27.966, 'max_score': 719.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw719586.jpg'}, {'end': 835.781, 'src': 'embed', 'start': 804.19, 'weight': 1, 'content': [{'end': 807.351, 'text': "Always understand whenever let's draw this distribution once again.", 'start': 804.19, 'duration': 3.161}, {'end': 811.433, 'text': 'Now suppose this is my distribution.', 'start': 809.812, 'duration': 1.621}, {'end': 815.365, 'text': "Let's consider that I'm very bad at drawing.", 'start': 813.223, 'duration': 2.142}, {'end': 819.028, 'text': "Okay So I think I'm good at drawing also.", 'start': 815.925, 'duration': 3.103}, {'end': 821.43, 'text': 'Okay So this is my.', 'start': 819.488, 'duration': 1.942}, {'end': 826.615, 'text': 'I cannot draw straight line, difficult, but it will get created.', 'start': 823.413, 'duration': 3.202}, {'end': 829.157, 'text': 'Okay, so this will be a mean median mode.', 'start': 826.675, 'duration': 2.482}, {'end': 835.781, 'text': 'Then you can go one step towards right, second step towards right, first step towards right.', 'start': 829.937, 'duration': 5.844}], 'summary': 'Drawing distribution with mean, median, mode, and moving steps to the right.', 'duration': 31.591, 'max_score': 804.19, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw804190.jpg'}], 'start': 253.477, 'title': 'Data distributions and visualization', 'summary': 'Covers various data distributions such as normal, log normal, and binomial, along with their practical applications using python programming. it also emphasizes visualizing continuous data distribution through histograms, focusing on gaussian distribution and the significance of standard deviation in drawing conclusions.', 'chapters': [{'end': 530.094, 'start': 253.477, 'title': 'Statistics: distributions and percentiles', 'summary': 'Covers important topics like distributions, percentiles, including normal distribution, z scores, log normal distribution, bernoulli distribution, and binomial distribution, along with practical applications using python programming language and creating histograms and pdfs.', 'duration': 276.617, 'highlights': ['We will cover important topics like distributions, percentiles, normal distribution, z scores, log normal distribution, bernoulli distribution, and binomial distribution, along with practical applications using Python programming language and creating histograms and PDFs.', 'We will discuss about standard normal distribution, z scores, and the concept of z table and why z scores are used.', 'The chapter will also cover mean, median, mode, variance, standard deviation, and creating histograms and PDFs using Python programming language.', 'The session will include a discussion on bar plot and violent plot along with other relevant topics.']}, {'end': 950.491, 'start': 530.094, 'title': 'Visualizing continuous data distribution', 'summary': 'Discusses visualizing continuous data distribution through histograms, focusing on gaussian or normal distribution, and the importance of standard deviation in deriving conclusions.', 'duration': 420.397, 'highlights': ['The chapter emphasizes the need for visualizing continuous data through various graphs to understand the distribution, particularly focusing on Gaussian or normal distribution. Emphasis on visualizing continuous data through graphs, focus on Gaussian or normal distribution.', 'The importance of bell curve in Gaussian or normal distribution, representing mean, median, and mode, along with its symmetrical properties, is explained. Explanation of bell curve in Gaussian or normal distribution, representation of mean, median, and mode, symmetrical properties.', 'The significance of standard deviation is highlighted, along with its role in deriving conclusions from the graphs of continuous data distribution. Significance of standard deviation, role in deriving conclusions from continuous data distribution graphs.']}], 'duration': 697.014, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw253477.jpg', 'highlights': ['Covers various data distributions such as normal, log normal, and binomial, along with their practical applications using Python programming.', 'Emphasis on visualizing continuous data through graphs, focus on Gaussian or normal distribution.', 'The significance of standard deviation is highlighted, along with its role in deriving conclusions from the graphs of continuous data distribution.', 'We will cover important topics like distributions, percentiles, normal distribution, z scores, log normal distribution, Bernoulli distribution, and binomial distribution, along with practical applications using Python programming language and creating histograms and PDFs.', 'The chapter emphasizes the need for visualizing continuous data through various graphs to understand the distribution, particularly focusing on Gaussian or normal distribution.']}, {'end': 1406.987, 'segs': [{'end': 1023.199, 'src': 'embed', 'start': 988.467, 'weight': 6, 'content': [{'end': 989.908, 'text': 'Okay? Percentage rule.', 'start': 988.467, 'duration': 1.441}, {'end': 993.851, 'text': "This basically indicates that, let's go with 68.", 'start': 990.388, 'duration': 3.463}, {'end': 999.535, 'text': 'Within the first standard deviation around, suppose if I have some distribution data.', 'start': 993.851, 'duration': 5.684}, {'end': 1004.019, 'text': "Let's consider that I have a data set which have 100 data points.", 'start': 999.555, 'duration': 4.464}, {'end': 1006.441, 'text': 'Which have 100 data points.', 'start': 1004.9, 'duration': 1.541}, {'end': 1010.183, 'text': 'Okay Which have 100 data points.', 'start': 1008.28, 'duration': 1.903}, {'end': 1013.106, 'text': 'Now, what does this basically indicate?', 'start': 1011.564, 'duration': 1.542}, {'end': 1023.199, 'text': 'is that between the first standard deviation between this region in this entire region, around 68% of the distribution is present.', 'start': 1013.106, 'duration': 10.093}], 'summary': 'Around 68% of a distribution with 100 data points falls within the first standard deviation.', 'duration': 34.732, 'max_score': 988.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw988467.jpg'}, {'end': 1118.2, 'src': 'embed', 'start': 1093.974, 'weight': 5, 'content': [{'end': 1101.216, 'text': 'around 99.7% of the entire distribution will fall in this region.', 'start': 1093.974, 'duration': 7.242}, {'end': 1106.157, 'text': 'Okay So that is the reason why it is basically called as 68, 95 and 99.7 percentile low.', 'start': 1101.876, 'duration': 4.281}, {'end': 1111.198, 'text': 'Okay? So everybody is clear.', 'start': 1109.378, 'duration': 1.82}, {'end': 1118.2, 'text': 'That basically means that now, if you have a distribution which is Gaussian or normally distributed,', 'start': 1111.559, 'duration': 6.641}], 'summary': 'About 99.7% of the distribution falls within 68, 95, and 99.7 percentile.', 'duration': 24.226, 'max_score': 1093.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1093974.jpg'}, {'end': 1299.711, 'src': 'embed', 'start': 1249.376, 'weight': 0, 'content': [{'end': 1253.639, 'text': 'So, this was the thing with respect to Gaussian or normally distributed.', 'start': 1249.376, 'duration': 4.263}, {'end': 1258.661, 'text': 'That basically means, suppose now let us consider one thing.', 'start': 1254.219, 'duration': 4.442}, {'end': 1261.122, 'text': 'Let us everybody is clear with this at least?', 'start': 1258.701, 'duration': 2.421}, {'end': 1262.863, 'text': 'Shall I start the next topic?', 'start': 1261.602, 'duration': 1.261}, {'end': 1267.425, 'text': 'Because from this we will try to derive something which is called as Z-score.', 'start': 1263.183, 'duration': 4.242}, {'end': 1272.982, 'text': 'Everybody is clear with this? Yes? Hit the like button.', 'start': 1267.986, 'duration': 4.996}, {'end': 1274.523, 'text': 'I need to see lot of likes now.', 'start': 1273.082, 'duration': 1.441}, {'end': 1279.564, 'text': 'Because now I am going to use multiple colors and show you it in an efficient way.', 'start': 1274.743, 'duration': 4.821}, {'end': 1283.566, 'text': 'Okay But everybody is clear with this? I will talk about iris dataset.', 'start': 1279.945, 'duration': 3.621}, {'end': 1285.627, 'text': 'If you have not seen iris dataset, what it is.', 'start': 1283.606, 'duration': 2.021}, {'end': 1286.827, 'text': 'I will show you practically.', 'start': 1285.647, 'duration': 1.18}, {'end': 1289.708, 'text': 'Okay I will talk about it practically.', 'start': 1287.687, 'duration': 2.021}, {'end': 1290.908, 'text': "Okay Don't worry.", 'start': 1290.008, 'duration': 0.9}, {'end': 1299.711, 'text': 'This rules apply to those dataset which follow a Gaussian distribution or normal distribution.', 'start': 1293.829, 'duration': 5.882}], 'summary': 'Discussion about gaussian distribution and use of iris dataset practically.', 'duration': 50.335, 'max_score': 1249.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1249376.jpg'}, {'end': 1416.714, 'src': 'embed', 'start': 1386.481, 'weight': 2, 'content': [{'end': 1389.763, 'text': 'So when we go from basics, you will be able to understand each and everything.', 'start': 1386.481, 'duration': 3.282}, {'end': 1394.466, 'text': 'Is it clear or do you want to directly learn the last thing? Tell me.', 'start': 1390.223, 'duration': 4.243}, {'end': 1402.804, 'text': 'Should I directly show you the last day, seventh day that I am actually planning to do or do you want to learn in this particular way? Tell me.', 'start': 1395.859, 'duration': 6.945}, {'end': 1405.206, 'text': 'You can tell me.', 'start': 1404.626, 'duration': 0.58}, {'end': 1406.987, 'text': 'Based on that,', 'start': 1406.227, 'duration': 0.76}, {'end': 1416.714, 'text': 'I will may directly teach you the last day things and then you can go home and probably be confused whenever there is an interview going on.', 'start': 1406.987, 'duration': 9.727}], 'summary': 'Learn basics to understand everything, decide on learning approach for last day.', 'duration': 30.233, 'max_score': 1386.481, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1386481.jpg'}], 'start': 952.025, 'title': 'Gaussian distribution and percentile rule', 'summary': 'Covers the empirical formula, the 68, 95, 99.7 percentile rule, and gaussian distribution, illustrating how they represent data distribution within one, two, and three standard deviations, with 68%, 95%, and 99.7% of data falling within these regions.', 'chapters': [{'end': 1118.2, 'start': 952.025, 'title': 'Empirical formula and percentile rule', 'summary': 'Discusses the empirical formula and the 68, 95, 99.7 percentile rule, explaining how they indicate the percentage of data within one, two, and three standard deviations in a distribution, with 68%, 95%, and 99.7% of data falling within these regions respectively.', 'duration': 166.175, 'highlights': ['The 68, 95, 99.7 percentile rule explains that within one, two, and three standard deviations, approximately 68%, 95%, and 99.7% of the data in a distribution lies within these ranges.', 'Within the first standard deviation, around 68% of the distribution is present, indicating that out of 100 data points, 68 data points will be present in this region.', 'Within the second standard deviation, around 95% of the entire data lies in this region.', 'Within the third standard deviation, around 99.7% of the entire distribution will fall in this region.']}, {'end': 1406.987, 'start': 1118.2, 'title': 'Understanding gaussian distribution', 'summary': 'Explains the concept of gaussian distribution, including the significance of standard deviations, examples of gaussian distributed data such as height, weight, and iris dataset, and the application of the empirical rule in determining data distribution.', 'duration': 288.787, 'highlights': ['The concept of Gaussian distribution and the significance of standard deviations are explained, with examples such as height, weight, and Iris dataset. The chapter provides examples of Gaussian distributed data, including height, weight, and Iris dataset, and explains the significance of standard deviations within the distribution.', 'The application of the empirical rule in determining data distribution based on Gaussian distribution is discussed. The chapter discusses the application of the empirical rule, including the 68, 95, 99.7 percentile rule, in determining data distribution based on Gaussian distribution.', 'The approach of gradually understanding the concept from basics is emphasized, with the intention to cover advanced topics in subsequent sessions. The chapter emphasizes the gradual understanding of the concept from basics, with the intention to cover advanced topics in subsequent sessions, highlighting the need for a comprehensive learning approach.']}], 'duration': 454.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw952025.jpg', 'highlights': ['The 68, 95, 99.7 percentile rule explains that within one, two, and three standard deviations, approximately 68%, 95%, and 99.7% of the data in a distribution lies within these ranges.', 'Within the third standard deviation, around 99.7% of the entire distribution will fall in this region.', 'Within the second standard deviation, around 95% of the entire data lies in this region.', 'Within the first standard deviation, around 68% of the distribution is present, indicating that out of 100 data points, 68 data points will be present in this region.', 'The chapter discusses the application of the empirical rule, including the 68, 95, 99.7 percentile rule, in determining data distribution based on Gaussian distribution.', 'The concept of Gaussian distribution and the significance of standard deviations are explained, with examples such as height, weight, and Iris dataset.', 'The chapter emphasizes the gradual understanding of the concept from basics, with the intention to cover advanced topics in subsequent sessions, highlighting the need for a comprehensive learning approach.']}, {'end': 2430.222, 'segs': [{'end': 1524.001, 'src': 'embed', 'start': 1472.437, 'weight': 2, 'content': [{'end': 1482.526, 'text': 'Obviously, when I say 5 is first standard deviation to the right, that basically means 4 will be plus 0.5 standard deviation to the right.', 'start': 1472.437, 'duration': 10.089}, {'end': 1490.668, 'text': 'Yes, everybody is agreeing with this? 0.5 standard deviation, right? Understand, 0.5 standard deviation.', 'start': 1483.286, 'duration': 7.382}, {'end': 1499.25, 'text': 'If you say 1 standard deviation, it is basically coming to 5, right? It is 0.5 standard deviation, right? Okay.', 'start': 1490.768, 'duration': 8.482}, {'end': 1509.867, 'text': 'Now similarly if I say, where does 4.75 fall? Then how you will be able to see it? See, point, the standard deviation was 1, right.', 'start': 1499.49, 'duration': 10.377}, {'end': 1512.91, 'text': 'I told 4.5.', 'start': 1511.508, 'duration': 1.402}, {'end': 1517.094, 'text': 'So, 4.5 will be something falling over here and this is like 0.5 standard deviation.', 'start': 1512.91, 'duration': 4.184}, {'end': 1524.001, 'text': 'But in the case of 4.75, it will be very much difficult for you to do the calculation, right.', 'start': 1517.674, 'duration': 6.327}], 'summary': 'Explaining standard deviation and its application with examples.', 'duration': 51.564, 'max_score': 1472.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1472437.jpg'}, {'end': 1594.019, 'src': 'heatmap', 'start': 1472.437, 'weight': 0.81, 'content': [{'end': 1482.526, 'text': 'Obviously, when I say 5 is first standard deviation to the right, that basically means 4 will be plus 0.5 standard deviation to the right.', 'start': 1472.437, 'duration': 10.089}, {'end': 1490.668, 'text': 'Yes, everybody is agreeing with this? 0.5 standard deviation, right? Understand, 0.5 standard deviation.', 'start': 1483.286, 'duration': 7.382}, {'end': 1499.25, 'text': 'If you say 1 standard deviation, it is basically coming to 5, right? It is 0.5 standard deviation, right? Okay.', 'start': 1490.768, 'duration': 8.482}, {'end': 1509.867, 'text': 'Now similarly if I say, where does 4.75 fall? Then how you will be able to see it? See, point, the standard deviation was 1, right.', 'start': 1499.49, 'duration': 10.377}, {'end': 1512.91, 'text': 'I told 4.5.', 'start': 1511.508, 'duration': 1.402}, {'end': 1517.094, 'text': 'So, 4.5 will be something falling over here and this is like 0.5 standard deviation.', 'start': 1512.91, 'duration': 4.184}, {'end': 1524.001, 'text': 'But in the case of 4.75, it will be very much difficult for you to do the calculation, right.', 'start': 1517.674, 'duration': 6.327}, {'end': 1528.745, 'text': 'So, that is the reason what we can do is that we can use a concept which is called as Z-score.', 'start': 1524.541, 'duration': 4.204}, {'end': 1539.149, 'text': 'Now Z score will basically help you find out whenever I talk about a value, how much standard deviation away it is from the mean.', 'start': 1529.446, 'duration': 9.703}, {'end': 1545.912, 'text': 'Okay So this formula is X of I minus mu divided by standard deviation.', 'start': 1539.69, 'duration': 6.222}, {'end': 1551.073, 'text': 'Okay X of I minus mu divided by standard deviation.', 'start': 1547.332, 'duration': 3.741}, {'end': 1553.094, 'text': 'Now I need to find out for 4.75.', 'start': 1551.454, 'duration': 1.64}, {'end': 1556.095, 'text': "Okay So let's go and compute.", 'start': 1553.094, 'duration': 3.001}, {'end': 1558.156, 'text': 'So over here, I will just write.', 'start': 1556.315, 'duration': 1.841}, {'end': 1566.63, 'text': 'I will just write 4.75 minus mu is what? Mu is 4.', 'start': 1560.228, 'duration': 6.402}, {'end': 1569.291, 'text': '4 divided by standard deviation is 1.', 'start': 1566.63, 'duration': 2.661}, {'end': 1571.072, 'text': 'So here I am actually getting 0.75.', 'start': 1569.291, 'duration': 1.781}, {'end': 1576.473, 'text': 'So now I can see that it is 0.75 standard deviation to the right.', 'start': 1571.072, 'duration': 5.401}, {'end': 1578.034, 'text': 'Why it is saying right?', 'start': 1577.014, 'duration': 1.02}, {'end': 1580.575, 'text': 'Why it is basically said as right?', 'start': 1578.914, 'duration': 1.661}, {'end': 1581.615, 'text': 'Why not left?', 'start': 1580.955, 'duration': 0.66}, {'end': 1583.976, 'text': 'Why not left?', 'start': 1583.496, 'duration': 0.48}, {'end': 1587.354, 'text': 'Why not left?', 'start': 1586.673, 'duration': 0.681}, {'end': 1594.019, 'text': 'Why I am saying that it is falling here 0.75 standard deviation to the right?', 'start': 1588.034, 'duration': 5.985}], 'summary': 'Explaining how to calculate z-score for a value like 4.75 using the formula x of i minus mu divided by standard deviation.', 'duration': 121.582, 'max_score': 1472.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1472437.jpg'}, {'end': 1830.178, 'src': 'embed', 'start': 1801.406, 'weight': 1, 'content': [{'end': 1806.429, 'text': 'z of 3, 1, minus 4, sorry, 3 minus 4, divided by 1..', 'start': 1801.406, 'duration': 5.023}, {'end': 1808.29, 'text': 'Okay, sorry.', 'start': 1806.429, 'duration': 1.861}, {'end': 1813.487, 'text': 'So 3 minus 4 divided by 1, what will happen? Minus 1.', 'start': 1809.844, 'duration': 3.643}, {'end': 1817.049, 'text': 'So minus 3 will now get converted to minus 1.', 'start': 1813.487, 'duration': 3.562}, {'end': 1818.93, 'text': 'Then 4 will get converted to 0.', 'start': 1817.049, 'duration': 1.881}, {'end': 1820.51, 'text': 'Then it will get converted to 1, 2, 3.', 'start': 1818.93, 'duration': 1.58}, {'end': 1824.794, 'text': 'Okay? Now understand the main magic in this.', 'start': 1820.511, 'duration': 4.283}, {'end': 1826.816, 'text': 'With the help of z-score.', 'start': 1824.974, 'duration': 1.842}, {'end': 1830.178, 'text': 'is this not the standard deviation of the same elements that we got over here??', 'start': 1826.816, 'duration': 3.362}], 'summary': 'Explanation of z-score and standard deviation using numerical example.', 'duration': 28.772, 'max_score': 1801.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1801406.jpg'}, {'end': 1919.416, 'src': 'embed', 'start': 1893.226, 'weight': 0, 'content': [{'end': 1903.01, 'text': 'Right? Now, what is this distribution then called? Anybody guesses? Any guesses? This was initially a normal distribution.', 'start': 1893.226, 'duration': 9.784}, {'end': 1907.371, 'text': 'A normal distribution or a Gaussian distribution.', 'start': 1905.151, 'duration': 2.22}, {'end': 1913.414, 'text': 'After I applied a z-score, what kind of distribution we are actually getting?', 'start': 1908.072, 'duration': 5.342}, {'end': 1917.695, 'text': 'And what is this basic distribution called as? Anyone?', 'start': 1913.954, 'duration': 3.741}, {'end': 1919.416, 'text': 'Anyone can answer me?', 'start': 1918.596, 'duration': 0.82}], 'summary': 'Applied z-score, transformed normal distribution to unknown distribution', 'duration': 26.19, 'max_score': 1893.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1893226.jpg'}, {'end': 2330.24, 'src': 'heatmap', 'start': 2100.808, 'weight': 0.763, 'content': [{'end': 2111.592, 'text': 'Now whenever I have some values like this, like 24, 25, 26, 27, salary may be 40k, 50k, 60k, 70k, something.', 'start': 2100.808, 'duration': 10.784}, {'end': 2118.114, 'text': 'Weight may be 70kg, 80kg, 55kg, 45kg.', 'start': 2113.312, 'duration': 4.802}, {'end': 2123.355, 'text': 'Now here when you have this kind of data, always understand.', 'start': 2119.654, 'duration': 3.701}, {'end': 2128.237, 'text': 'Now in this data, obviously you can see the units are completely different.', 'start': 2124.656, 'duration': 3.581}, {'end': 2139.311, 'text': 'Our main target should be that we should try to bring up in a form probably in this particular form where my mean is 0 and standard deviation equal to 1..', 'start': 2129.208, 'duration': 10.103}, {'end': 2145.533, 'text': 'Okay At that point of time, I can definitely apply standard normal distribution.', 'start': 2139.311, 'duration': 6.222}, {'end': 2159.971, 'text': 'That basically means I can take up this entire data, this entire data and apply z score to and convert this into standard normal distribution.', 'start': 2146.094, 'duration': 13.877}, {'end': 2164.534, 'text': 'Okay I can convert this into standard normal distribution.', 'start': 2161.232, 'duration': 3.302}, {'end': 2168.356, 'text': 'Similarly, I can go ahead and take up this particular data set.', 'start': 2165.154, 'duration': 3.202}, {'end': 2174.12, 'text': 'I can apply z-score and I can basically convert this into standard normal distribution.', 'start': 2168.876, 'duration': 5.244}, {'end': 2181.244, 'text': 'This process is basically called as standardization.', 'start': 2174.82, 'duration': 6.424}, {'end': 2186.788, 'text': 'Okay Very super important.', 'start': 2184.026, 'duration': 2.762}, {'end': 2189.646, 'text': 'Many people will talk about normalization.', 'start': 2187.645, 'duration': 2.001}, {'end': 2192.468, 'text': 'Okay? Normalization.', 'start': 2190.227, 'duration': 2.241}, {'end': 2196.19, 'text': 'I will talk about the difference between standardization and normalization.', 'start': 2192.488, 'duration': 3.702}, {'end': 2203.014, 'text': 'Whenever we talk about standardization, in short, internally there is a Z-score formula getting applied.', 'start': 2196.871, 'duration': 6.143}, {'end': 2208.398, 'text': 'Okay? So I hope you are able to understand.', 'start': 2205.576, 'duration': 2.822}, {'end': 2209.918, 'text': 'Right? Everyone.', 'start': 2208.918, 'duration': 1}, {'end': 2214.81, 'text': 'I guess everybody is able to understand with respect to standardization.', 'start': 2211.067, 'duration': 3.743}, {'end': 2220.455, 'text': 'Guys, those whoever are spamming, they will be removed from this entire live thing.', 'start': 2215.471, 'duration': 4.984}, {'end': 2223.298, 'text': 'I will hide the user completely.', 'start': 2221.036, 'duration': 2.262}, {'end': 2224.679, 'text': 'Example, Naveen.', 'start': 2223.798, 'duration': 0.881}, {'end': 2228.902, 'text': 'Okay So Naveen will be moved out now.', 'start': 2225.78, 'duration': 3.122}, {'end': 2230.464, 'text': 'Yes, he is moved out now.', 'start': 2229.343, 'duration': 1.121}, {'end': 2232.285, 'text': 'Now you cannot see Naveen message.', 'start': 2230.924, 'duration': 1.361}, {'end': 2237.027, 'text': 'Focus on learning over here.', 'start': 2234.186, 'duration': 2.841}, {'end': 2244.388, 'text': 'So standardization is a process where I am basically trying to convert a distribution into standard normal distribution.', 'start': 2237.827, 'duration': 6.561}, {'end': 2249.63, 'text': 'The property is that the mean is 0 and the standard deviation is 1.', 'start': 2244.468, 'duration': 5.162}, {'end': 2253.25, 'text': 'Now let us go ahead towards something called as normalization.', 'start': 2249.63, 'duration': 3.62}, {'end': 2255.851, 'text': 'Now, what exactly is normalization?', 'start': 2254.111, 'duration': 1.74}, {'end': 2265.286, 'text': 'In standardization, whenever we talk about here, we are getting converted as mean is equal to 0 and standard deviation equal to 1..', 'start': 2256.624, 'duration': 8.662}, {'end': 2267.526, 'text': 'Mean equal to 0 and standard equal to 1.', 'start': 2265.286, 'duration': 2.24}, {'end': 2269.587, 'text': 'Standard deviation equal to 1.', 'start': 2267.526, 'duration': 2.061}, {'end': 2271.727, 'text': 'Now in normalization, you have an option.', 'start': 2269.587, 'duration': 2.14}, {'end': 2280.869, 'text': 'You will say that I want to shift this entire values or whatever values that I have between 0 to 1.', 'start': 2272.387, 'duration': 8.482}, {'end': 2281.869, 'text': 'Let us consider like this.', 'start': 2280.869, 'duration': 1}, {'end': 2286.773, 'text': 'I want to change all these particular values between 0 to 1, right?', 'start': 2282.97, 'duration': 3.803}, {'end': 2291.036, 'text': 'So in this particular case, I may definitely apply normalization, okay?', 'start': 2287.213, 'duration': 3.823}, {'end': 2293.398, 'text': 'Now, how do we do normalization?', 'start': 2291.637, 'duration': 1.761}, {'end': 2297.541, 'text': 'There is a very important formula, which is called as min-max scalar.', 'start': 2293.478, 'duration': 4.063}, {'end': 2304.547, 'text': 'In the min-max scalar, you just have to provide 0 to 1 and automatically this kind of normalization will happen.', 'start': 2298.362, 'duration': 6.185}, {'end': 2307.729, 'text': "And yes, I will show you practically also, don't worry.", 'start': 2305.288, 'duration': 2.441}, {'end': 2314.136, 'text': 'If I want to probably shift this between minus 1 to plus 1, I can basically apply this.', 'start': 2308.55, 'duration': 5.586}, {'end': 2323.998, 'text': 'So normalization gives you a process where you can basically define the lower bound and upper bound and you can convert your data between them.', 'start': 2315.577, 'duration': 8.421}, {'end': 2330.24, 'text': 'Now very important thing, where do we use normalization? I hope everybody knows about deep learning.', 'start': 2324.658, 'duration': 5.582}], 'summary': 'Standardization and normalization aim to bring data to mean 0 and standard deviation 1, or scale data between 0 to 1 or -1 to 1, respectively.', 'duration': 229.432, 'max_score': 2100.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw2100808.jpg'}], 'start': 1406.987, 'title': 'Understanding statistical concepts', 'summary': 'Delves into understanding standard deviation, z-scores, and its application in achieving a standard normal distribution. it also covers the concepts of standardization and normalization, and their practical applications in data processing and machine learning, such as achieving a mean of 0 and standard deviation of 1, and converting pixel values between 0 to 1 in image training for cnn.', 'chapters': [{'end': 1644.715, 'start': 1406.987, 'title': 'Understanding standard deviation and z-score', 'summary': 'Explains the concept of standard deviation, using examples of 4.5 and 4.75, and introduces the z-score formula to calculate the deviation of values from the mean, such as 3.75 being -0.25 standard deviation to the left.', 'duration': 237.728, 'highlights': ['The Z-score formula (X of I minus mu divided by standard deviation) is used to calculate the deviation of values from the mean, with examples such as 4.75 being 0.75 standard deviation to the right.', 'The concept of standard deviation is illustrated with examples of 4.5 and 4.75, showing the difficulty in calculating deviations and the application of the Z-score formula.', 'The calculation for 3.75 using the Z-score formula reveals it to be -0.25 standard deviation to the left, indicating the position relative to the mean.']}, {'end': 2237.027, 'start': 1646.636, 'title': 'Understanding standard normal distribution', 'summary': 'Covers the application of z-scores to convert data into a standard normal distribution, discussing its relevance in machine learning, and the importance of standardization for achieving a mean of 0 and standard deviation of 1, with practical examples.', 'duration': 590.391, 'highlights': ['The application of z-scores to convert data into a standard normal distribution is discussed, emphasizing the significance of achieving a mean of 0 and standard deviation of 1 for practical application in machine learning and algorithms.', 'The process of standardization, involving the application of z-score to data to convert it into a standard normal distribution, is explained, highlighting its importance in achieving a consistent format with a mean of 0 and standard deviation of 1 for different units of measurement in datasets.', 'The concept of standard normal distribution is introduced, emphasizing its properties of having a mean of 0 and standard deviation of 1, and its relevance in data analysis and machine learning for achieving a consistent format across different units of measurement in datasets.', 'The calculations involving z-scores to convert data into a standard normal distribution are demonstrated, showcasing the transformation of data values to achieve a mean of 0 and standard deviation of 1, with specific examples and calculations provided for clarity and understanding.', 'The importance of standardization in achieving a consistent format with a mean of 0 and standard deviation of 1 for different units of measurement in datasets is highlighted, particularly in the context of machine learning and algorithms for practical application.']}, {'end': 2430.222, 'start': 2237.827, 'title': 'Standardization and normalization in data processing', 'summary': 'Discusses the concepts of standardization and normalization, including their definitions, formulas, and applications in data processing and deep learning, such as in image training in cnn, where pixel values are converted between 0 to 1 using normalization.', 'duration': 192.395, 'highlights': ['Standardization involves converting a distribution into a standard normal distribution with a mean of 0 and standard deviation of 1.', 'Normalization includes the process of shifting values between 0 to 1 using the min-max scalar formula.', 'In deep learning, specifically in CNN for image training, pixel values ranging from 0 to 255 can be converted between 0 to 1 for processing by dividing each pixel by 255.']}], 'duration': 1023.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw1406987.jpg', 'highlights': ['The Z-score formula (X of I minus mu divided by standard deviation) is used to calculate the deviation of values from the mean, with examples such as 4.75 being 0.75 standard deviation to the right.', 'The application of z-scores to convert data into a standard normal distribution is discussed, emphasizing the significance of achieving a mean of 0 and standard deviation of 1 for practical application in machine learning and algorithms.', 'Standardization involves converting a distribution into a standard normal distribution with a mean of 0 and standard deviation of 1.', 'The process of standardization, involving the application of z-score to data to convert it into a standard normal distribution, is explained, highlighting its importance in achieving a consistent format with a mean of 0 and standard deviation of 1 for different units of measurement in datasets.', 'Normalization includes the process of shifting values between 0 to 1 using the min-max scalar formula.', 'In deep learning, specifically in CNN for image training, pixel values ranging from 0 to 255 can be converted between 0 to 1 for processing by dividing each pixel by 255.']}, {'end': 3030.247, 'segs': [{'end': 2662.305, 'src': 'embed', 'start': 2616.018, 'weight': 0, 'content': [{'end': 2621.883, 'text': 'In 2020, the series average of the team scoring in 2020 was 260.', 'start': 2616.018, 'duration': 5.865}, {'end': 2634.392, 'text': 'The standard deviation of the score of all the matches is 12.', 'start': 2621.883, 'duration': 12.509}, {'end': 2643.798, 'text': 'Okay And then Over here, probably Rishabh Pant, Rishabh Pant, Rishabh, Rishpant I have written.', 'start': 2634.392, 'duration': 9.406}, {'end': 2648.22, 'text': 'Okay Final score is 75.', 'start': 2643.818, 'duration': 4.402}, {'end': 2651.561, 'text': 'Not 75.', 'start': 2648.22, 'duration': 3.341}, {'end': 2658.784, 'text': 'Let us say his final score is 68.', 'start': 2651.561, 'duration': 7.223}, {'end': 2662.305, 'text': 'Now, my question is that, my question is very much simple.', 'start': 2658.784, 'duration': 3.521}], 'summary': "In 2020, the team's average score was 260, with a standard deviation of 12. rishabh pant's final score was 68.", 'duration': 46.287, 'max_score': 2616.018, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw2616018.jpg'}, {'end': 2760.657, 'src': 'embed', 'start': 2723.652, 'weight': 2, 'content': [{'end': 2729.858, 'text': 'So for checking this, obviously many people will say 2020, 2021, lot of confusion will be there.', 'start': 2723.652, 'duration': 6.206}, {'end': 2732.7, 'text': 'So we will just try to apply for Z score.', 'start': 2730.378, 'duration': 2.322}, {'end': 2736.684, 'text': 'Now for the 2021, we will apply the Z score.', 'start': 2733.501, 'duration': 3.183}, {'end': 2742.849, 'text': 'So Z score will be nothing but, it will be X of I minus mu divided by standard deviation.', 'start': 2736.804, 'duration': 6.045}, {'end': 2745.591, 'text': 'We know what is X of I in this particular case.', 'start': 2743.51, 'duration': 2.081}, {'end': 2747.233, 'text': 'X of I is nothing but..', 'start': 2746.172, 'duration': 1.061}, {'end': 2760.657, 'text': 'X of I is nothing but Rishabh final score is 70.', 'start': 2756.653, 'duration': 4.004}], 'summary': "Using z score, rishabh's final score for 2021 is 70.", 'duration': 37.005, 'max_score': 2723.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw2723652.jpg'}], 'start': 2433.505, 'title': 'Comparing cricket scores using z-scores', 'summary': "Discusses the practical examples of using z-scores to compare rishabh pant's final scores in the 2020 and 2021 odi series, revealing that his final score was relatively better in 2020 compared to 2021. it also covers the calculation of z scores for cricket scores of rishabh pant and the team, using average scores of 240 and 245 and standard deviations of 10 and 12, providing insights on deviations from the mean scores and the potential impact of different players' performances on the team's overall score.", 'chapters': [{'end': 2723.652, 'start': 2433.505, 'title': "Comparing rishabh pant's scores", 'summary': "Discusses practical examples of using z-score to compare rishabh pant's final scores in the 2020 and 2021 odi series, revealing that his final score was relatively better in 2020 compared to 2021.", 'duration': 290.147, 'highlights': ["Rishabh Pant's final score in the 2020 ODI series was 68, with an average series score of 260 and standard deviation of 12.", "In the 2021 ODI series, Rishabh Pant's final score was 70, with an average series score of 250 and standard deviation of 10.", "The chapter presents a question about comparing Rishabh Pant's final scores in the 2020 and 2021 ODI series, revealing the quantitative data for the series averages and standard deviations to analyze which year he performed better."]}, {'end': 3030.247, 'start': 2723.652, 'title': 'Calculating z score for cricket scores', 'summary': "Discusses the calculation of z scores for cricket scores of rishabh pant and the team, using average scores of 240 and 245 and standard deviations of 10 and 12, providing insights on deviations from the mean scores and the potential impact of different players' performances on the team's overall score.", 'duration': 306.595, 'highlights': ["Explaining the Z score formula and applying it to 2021 and 2020 scores, with Rishabh Pant's average score being 240 and 245 and standard deviations of 10 and 12. Rishabh Pant's average score, standard deviations of 10 and 12.", "Demonstrating the calculation of Z scores for Rishabh Pant's scores of 70 and the team's scores of 240 and 245, resulting in deviations of -1 and -1.25 standard deviations. Deviations of -1 and -1.25 standard deviations.", "Discussing the potential impact of different players' performances on the team's overall score, considering the team's average score and individual player scores. Impact of different players' performances on the team's overall score."]}], 'duration': 596.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw2433505.jpg', 'highlights': ["Rishabh Pant's final score in the 2020 ODI series was 68, with an average series score of 260 and standard deviation of 12.", "In the 2021 ODI series, Rishabh Pant's final score was 70, with an average series score of 250 and standard deviation of 10.", "Explaining the Z score formula and applying it to 2021 and 2020 scores, with Rishabh Pant's average score being 240 and 245 and standard deviations of 10 and 12.", "Demonstrating the calculation of Z scores for Rishabh Pant's scores of 70 and the team's scores of 240 and 245, resulting in deviations of -1 and -1.25 standard deviations."]}, {'end': 3731.226, 'segs': [{'end': 3103.645, 'src': 'embed', 'start': 3069.678, 'weight': 0, 'content': [{'end': 3071.319, 'text': 'Over here you can see the mean is 250.', 'start': 3069.678, 'duration': 1.641}, {'end': 3078.404, 'text': 'X of i is nothing but how much? It is nothing but 240.', 'start': 3071.319, 'duration': 7.085}, {'end': 3080.245, 'text': 'And the mean is 10.', 'start': 3078.404, 'duration': 1.841}, {'end': 3084.588, 'text': 'Sorry, and the standard deviation is 10.', 'start': 3080.245, 'duration': 4.343}, {'end': 3088.591, 'text': 'Okay And the standard deviation is 10.', 'start': 3084.588, 'duration': 4.003}, {'end': 3098.58, 'text': 'If I have this information, can I draw the bell curve? Can I draw the bell curve? So this is my bell curve.', 'start': 3088.591, 'duration': 9.989}, {'end': 3102.464, 'text': 'The mean is how much? 250.', 'start': 3099.441, 'duration': 3.023}, {'end': 3103.645, 'text': 'Standard deviation is 10.', 'start': 3102.464, 'duration': 1.181}], 'summary': 'Mean is 250, standard deviation is 10, can draw bell curve', 'duration': 33.967, 'max_score': 3069.678, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw3069678.jpg'}, {'end': 3468.792, 'src': 'embed', 'start': 3407.976, 'weight': 1, 'content': [{'end': 3417.699, 'text': '4, 5, 6, 7, 3, 2, 1 and let us say that I have a bell curve which looks like this.', 'start': 3407.976, 'duration': 9.723}, {'end': 3427.241, 'text': 'Now I want to know, my question is, my question is, my question is very simple.', 'start': 3420.099, 'duration': 7.142}, {'end': 3448.666, 'text': 'My question is what percentage of scores fall above 4.25?', 'start': 3429.246, 'duration': 19.42}, {'end': 3451.89, 'text': 'Did you understand the question everybody?', 'start': 3448.666, 'duration': 3.224}, {'end': 3462.926, 'text': 'If I answer everything, then what research you will do more?', 'start': 3460.263, 'duration': 2.663}, {'end': 3468.792, 'text': 'Okay?, What research you will do more?', 'start': 3464.187, 'duration': 4.605}], 'summary': 'Analyzing bell curve to find percentage above 4.25.', 'duration': 60.816, 'max_score': 3407.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw3407976.jpg'}], 'start': 3030.988, 'title': 'Z scores and percentage analysis', 'summary': 'Covers the concept of z scores, performance analysis, and finding the percentage above a certain score. it provides examples of team final scores for 2020 and 2021, with 2021 showing a lower standard deviation, indicating potentially better performance. the estimation of the percentage above a certain score is approximately 48 to 49% using the z-score formula in a symmetrical bell curve.', 'chapters': [{'end': 3406.996, 'start': 3030.988, 'title': 'Understanding z scores and performance analysis', 'summary': 'Covers the concept of z scores and performance analysis using mean and standard deviation, providing examples of team final scores for 2020 and 2021, with 2021 showing a lower standard deviation, indicating potentially better performance. the chapter also emphasizes the importance of understanding z scores in practical scenarios and interview questions.', 'duration': 376.008, 'highlights': ['The chapter demonstrates the calculation of Z scores and their significance in analyzing performance, with the example of Team final scores for 2020 and 2021, where 2021 exhibited a lower standard deviation, potentially indicating better performance.', 'The instructor emphasizes the importance of understanding Z scores in practical scenarios and interviews, hinting at their relevance in statistical analysis and decision-making processes.', 'The chapter introduces the concept of random variables in statistical interview questions, highlighting their importance in understanding probability distributions and data analysis.']}, {'end': 3731.226, 'start': 3407.976, 'title': 'Finding percentage above a certain score', 'summary': 'Discusses the concept of finding the percentage of scores falling above a certain value, using the z-score formula in a symmetrical bell curve, emphasizing the area of the body curve, and concludes with the estimation of the percentage as approximately 48 to 49%.', 'duration': 323.25, 'highlights': ['The chapter discusses the concept of finding the percentage of scores falling above a certain value. The main focus is on determining the percentage of scores that fall above a specific value, which in this case is 4.25.', 'Using the Z-score formula in a symmetrical bell curve to determine the area of the body curve. The Z-score formula is explained and utilized to find the area of the body curve, which is crucial in calculating the percentage of scores falling above the given value.', 'Estimating the percentage as approximately 48 to 49% using common sense and simple calculations. A simple estimation is made using common sense and basic calculations, resulting in an approximate percentage of 48 to 49% for scores falling above the specified value.']}], 'duration': 700.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw3030988.jpg', 'highlights': ['The chapter demonstrates the calculation of Z scores and their significance in analyzing performance, with the example of Team final scores for 2020 and 2021, where 2021 exhibited a lower standard deviation, potentially indicating better performance.', 'The chapter introduces the concept of random variables in statistical interview questions, highlighting their importance in understanding probability distributions and data analysis.', 'The instructor emphasizes the importance of understanding Z scores in practical scenarios and interviews, hinting at their relevance in statistical analysis and decision-making processes.', 'Using the Z-score formula in a symmetrical bell curve to determine the area of the body curve. The Z-score formula is explained and utilized to find the area of the body curve, which is crucial in calculating the percentage of scores falling above the given value.', 'The chapter discusses the concept of finding the percentage of scores falling above a certain value. The main focus is on determining the percentage of scores that fall above a specific value, which in this case is 4.25.', 'Estimating the percentage as approximately 48 to 49% using common sense and simple calculations. A simple estimation is made using common sense and basic calculations, resulting in an approximate percentage of 48 to 49% for scores falling above the specified value.']}, {'end': 4727.793, 'segs': [{'end': 4146.654, 'src': 'embed', 'start': 4117.167, 'weight': 0, 'content': [{'end': 4119.849, 'text': 'Yeah, Data Scientist Pro, come with your real name.', 'start': 4117.167, 'duration': 2.682}, {'end': 4122.689, 'text': 'I will show you some examples of okay, okay, overloaded.', 'start': 4119.908, 'duration': 2.781}, {'end': 4132.745, 'text': "Vaibhav Singh says why subtracting from 1? It's very simple, no? See guys, again I am talking about this.", 'start': 4126.6, 'duration': 6.145}, {'end': 4140.77, 'text': 'My question is that, this is my mean, from this particular curve I want to find out what is the percentage of the distribution.', 'start': 4133.566, 'duration': 7.204}, {'end': 4146.654, 'text': 'Then what I can do if I want to find out this curve? I can take this whole curve, subtract with the left one.', 'start': 4142.292, 'duration': 4.362}], 'summary': 'Data scientist pro discusses finding percentage of distribution and curve subtraction.', 'duration': 29.487, 'max_score': 4117.167, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw4117167.jpg'}, {'end': 4205.647, 'src': 'embed', 'start': 4169.033, 'weight': 2, 'content': [{'end': 4174.698, 'text': 'from this to this, from 0.25 standard deviation to the left part.', 'start': 4169.033, 'duration': 5.665}, {'end': 4190.077, 'text': 'Yes? So, now did you find out how important this is for the interview questions guys? If you are able to explain.', 'start': 4180.151, 'duration': 9.926}, {'end': 4194.32, 'text': 'Why not directly taking from the right table? Understand guys, right table is not given.', 'start': 4190.537, 'duration': 3.783}, {'end': 4196.761, 'text': 'No This is not right table.', 'start': 4194.4, 'duration': 2.361}, {'end': 4199.403, 'text': 'This is only given from here to here.', 'start': 4197.442, 'duration': 1.961}, {'end': 4201.364, 'text': 'Only here to here.', 'start': 4200.384, 'duration': 0.98}, {'end': 4205.647, 'text': 'Yeah, here to here only this is given.', 'start': 4203.686, 'duration': 1.961}], 'summary': 'Importance of explaining interview questions, not given in right table.', 'duration': 36.614, 'max_score': 4169.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw4169033.jpg'}], 'start': 3732.147, 'title': 'Calculating z-score and understanding distribution', 'summary': "Covers calculating z-score using z table, understanding left and right z table, explaining z-score's importance in tail distribution and demonstrating percentage calculation above a certain value, resulting in a 40% distribution above 4.25. it also discusses standardization and practical examples of z-scores and percentage calculations.", 'chapters': [{'end': 3924.562, 'start': 3732.147, 'title': 'Calculating z-score and using z table', 'summary': 'Explains how to calculate z-score using z-scope, finding area under the curve using z table, and understanding left and right z table in statistics, with z value of 0.25 and instructions on finding area in both tails.', 'duration': 192.415, 'highlights': ['The Z value is 0.25, and the chapter demonstrates how to use the Z table to find the area under the curve.', 'Explains the significance of left and right Z table in finding the area in both tails in statistics, emphasizing the process of using these values to find the area between 0 and any positive value.', 'Emphasizes the importance of understanding the Z table, which shows the area to the right hand side of the curve and provides guidance on finding the area in the left tail using the left tail Z table.']}, {'end': 4233.227, 'start': 3925.263, 'title': 'Understanding z-score and tail distribution', 'summary': 'Explains the importance of z-score in understanding tail distribution and demonstrates the process of calculating the percentage of scores above a certain value using left z table, resulting in a 40% distribution above 4.25.', 'duration': 307.964, 'highlights': ['The importance of understanding Z-score and tail distribution is emphasized, as it allows for the calculation of the percentage of scores above a certain value, demonstrated by the example of 40% distribution above 4.25.', 'The process of using the left Z table to find the percentage of scores above a certain value is explained using the example of subtracting 1 minus the left area, resulting in a clear understanding of the concept.', 'The significance of not having information in the right table is highlighted, specifying that only the information from a certain range is given, emphasizing the importance of using the left table for calculations.']}, {'end': 4727.793, 'start': 4233.917, 'title': 'Understanding z-score and standardization', 'summary': 'Covers the computation of z-scores, standard deviation, and percentage calculations in a practical example, discussing the area under the curve and providing guidance on using z-tables.', 'duration': 493.876, 'highlights': ['The instructor explains the computation of Z-scores, standard deviation, and percentage calculations in a practical example, providing insights into the area under the curve and the use of Z-tables.', 'The example involves calculating the percentage of the population with an IQ lower than 85, demonstrating the application of Z-score calculations in real-world scenarios.', 'The instructor emphasizes the importance of understanding the body area under the curve and provides guidance on solving similar problems involving IQ ranges.', 'The instructor encourages revising and understanding the concepts efficiently due to time constraints and the need to cover numerous topics.']}], 'duration': 995.646, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw3732147.jpg', 'highlights': ['Demonstrates how to use the Z table to find the area under the curve with a Z value of 0.25.', 'Emphasizes the importance of understanding Z-score and tail distribution for calculating the percentage of scores above a certain value.', 'Practical example involves calculating the percentage of the population with an IQ lower than 85 using Z-score calculations.']}, {'end': 5679.584, 'segs': [{'end': 4945.847, 'src': 'heatmap', 'start': 4882.325, 'weight': 0.78, 'content': [{'end': 4890.154, 'text': 'Okay Now first thing first, how to compute mean, mean median mode.', 'start': 4882.325, 'duration': 7.829}, {'end': 4894.177, 'text': 'Okay, we are going to see that.', 'start': 4893.016, 'duration': 1.161}, {'end': 4902.084, 'text': 'First of all, let me load a data set which is called as, I load a data set which is called as tips.', 'start': 4894.758, 'duration': 7.326}, {'end': 4912.232, 'text': 'Okay And this will basically be giving me df is equal to this one.', 'start': 4903.785, 'duration': 8.447}, {'end': 4917.157, 'text': 'Okay Then I will say df.head.', 'start': 4913.693, 'duration': 3.464}, {'end': 4924.423, 'text': 'Okay So here you can see this is my entire data set.', 'start': 4921.22, 'duration': 3.203}, {'end': 4936.012, 'text': 'Now quickly if you want to see how to do mean for this, let us say that I am using np.mean function for finding the total bill mean.', 'start': 4925.183, 'duration': 10.829}, {'end': 4939.355, 'text': 'Total bill of mean.', 'start': 4938.154, 'duration': 1.201}, {'end': 4942.257, 'text': 'Okay So if I execute this, you will be able to see the answer.', 'start': 4939.475, 'duration': 2.782}, {'end': 4945.847, 'text': 'So this is the what is the mean of the total bill.', 'start': 4943.126, 'duration': 2.721}], 'summary': "Using np.mean function, the mean of the total bill in the 'tips' dataset is computed.", 'duration': 63.522, 'max_score': 4882.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw4882325.jpg'}, {'end': 5196.836, 'src': 'embed', 'start': 5167.939, 'weight': 3, 'content': [{'end': 5175.122, 'text': 'Does this follow a Gaussian distribution? Does this follow a Gaussian distribution? No, I guess.', 'start': 5167.939, 'duration': 7.183}, {'end': 5177.102, 'text': "Let's try with sepal width.", 'start': 5175.862, 'duration': 1.24}, {'end': 5181.764, 'text': "Finally, we'll be able to see something.", 'start': 5180.163, 'duration': 1.601}, {'end': 5185.645, 'text': 'Wow, this follows a Gaussian distribution, everyone.', 'start': 5182.424, 'duration': 3.221}, {'end': 5191.611, 'text': 'Guys, you have to do it in Google Collab, not Collab Pro.', 'start': 5188.928, 'duration': 2.683}, {'end': 5196.836, 'text': 'Okay You do it in Google Collab because Collab Pro takes a subscription charges for $10 per month.', 'start': 5191.911, 'duration': 4.925}], 'summary': 'Data analysis indicates gaussian distribution in sepal width. use google collab to avoid $10/month charges.', 'duration': 28.897, 'max_score': 5167.939, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw5167939.jpg'}, {'end': 5642.306, 'src': 'embed', 'start': 5611.651, 'weight': 0, 'content': [{'end': 5618.334, 'text': 'And within some time, when more than 200, 300 courses gets uploaded, because right now it is maturing.', 'start': 5611.651, 'duration': 6.683}, {'end': 5620.455, 'text': 'we are updating more and more, more and more content.', 'start': 5618.334, 'duration': 2.121}, {'end': 5623.036, 'text': 'Right? So it is important.', 'start': 5620.995, 'duration': 2.041}, {'end': 5629.478, 'text': 'Okay So please go ahead and take it.', 'start': 5625.496, 'duration': 3.982}, {'end': 5634.26, 'text': 'Okay And yes, this was it from my side.', 'start': 5630.819, 'duration': 3.441}, {'end': 5640.766, 'text': 'Why you removed your stats from beginner free course from dashboard? Because I have uploaded the course.', 'start': 5636.024, 'duration': 4.742}, {'end': 5642.306, 'text': 'No Full re-recorded.', 'start': 5640.886, 'duration': 1.42}], 'summary': 'Over 200-300 courses will be uploaded as the platform matures, with continuous content updates.', 'duration': 30.655, 'max_score': 5611.651, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw5611651.jpg'}], 'start': 4730.296, 'title': 'Data analysis with python', 'summary': 'Covers python data analysis techniques including symmetry in negative and positive values, mean, median, mode computation, outlier detection using libraries like numpy and matplotlib, and exploration of the iris flower dataset, with a call to action for audience engagement and course promotion.', 'chapters': [{'end': 4796.466, 'start': 4730.296, 'title': 'Symmetry in negative and positive values', 'summary': 'Discusses the symmetry in negative and positive values, emphasizing the ability to check for negative values and demonstrates the intention to proceed with programming sessions using google collab pro.', 'duration': 66.17, 'highlights': ['The chapter emphasizes the symmetry in negative and positive values, highlighting the ability to check for minus 1 as well as 0.15886 being 1 minus 0.84.', 'The speaker expresses the intention to proceed with programming sessions using Google Collab Pro and acknowledges the potential fatigue of the audience.']}, {'end': 5106.948, 'start': 4797.487, 'title': 'Python data analysis with mean, median, mode, and outliers', 'summary': 'Covers python data analysis using libraries like numpy and matplotlib to compute mean, median, mode, and detect outliers in a dataset, showcasing examples and visualizations.', 'duration': 309.461, 'highlights': ['The chapter covers Python data analysis using libraries like numpy and matplotlib to compute mean, median, mode, and detect outliers in a dataset. The chapter demonstrates the use of Python libraries numpy and matplotlib to calculate mean, median, mode, and identify outliers in a dataset.', 'The chapter showcases examples and visualizations to illustrate the concepts of mean, median, mode, and outlier detection. The chapter provides examples and visualizations to illustrate the concepts of mean, median, mode, and outlier detection in a dataset.', 'The chapter demonstrates the calculation of mean, median, and mode for a given dataset using numpy and statistics libraries. The chapter showcases the usage of numpy and statistics libraries to compute the mean, median, and mode for a given dataset.', 'The chapter emphasizes the utilization of matplotlib to create visualizations such as box plots and histograms for outlier detection and data distribution analysis. The chapter highlights the usage of matplotlib to generate box plots and histograms for outlier detection and data distribution analysis.']}, {'end': 5679.584, 'start': 5106.988, 'title': 'Exploring iris dataset in python', 'summary': 'Explores the analysis of the iris flower dataset using python, including plotting features, identifying distributions, and outlier detection, with a call to action for audience engagement and course promotion.', 'duration': 572.596, 'highlights': ['The dataset used for analysis is the iris flower dataset, containing information about different types of flowers and their features. The dataset used for analysis is the iris flower dataset, which provides data about different types of flowers and their features.', 'Visualization techniques such as plotting features and identifying distributions, specifically Gaussian and normal distributions, are demonstrated. The chapter demonstrates visualization techniques, including plotting features and identifying Gaussian and normal distributions.', 'Instructions for outlier detection using percentile calculations and a call to action for audience engagement and course promotion, with a discount coupon provided. The session provides instructions for outlier detection using percentile calculations and encourages audience engagement and course promotion, offering a discount coupon.']}], 'duration': 949.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/y1y1ATTMpaw/pics/y1y1ATTMpaw4730296.jpg', 'highlights': ['The chapter covers Python data analysis using libraries like numpy and matplotlib to compute mean, median, mode, and detect outliers in a dataset.', 'The chapter emphasizes the utilization of matplotlib to create visualizations such as box plots and histograms for outlier detection and data distribution analysis.', 'The dataset used for analysis is the iris flower dataset, containing information about different types of flowers and their features.', 'Instructions for outlier detection using percentile calculations and a call to action for audience engagement and course promotion, with a discount coupon provided.']}], 'highlights': ['The chapter emphasizes the need for visualizing continuous data through various graphs to understand the distribution, particularly focusing on Gaussian or normal distribution.', 'The session will cover all statistics relevant to data science professionals, and it will run until Friday.', 'The chapter discusses the application of the empirical rule, including the 68, 95, 99.7 percentile rule, in determining data distribution based on Gaussian distribution.', 'The Z-score formula (X of I minus mu divided by standard deviation) is used to calculate the deviation of values from the mean, with examples such as 4.75 being 0.75 standard deviation to the right.', "Rishabh Pant's final score in the 2020 ODI series was 68, with an average series score of 260 and standard deviation of 12.", 'The chapter demonstrates the calculation of Z scores and their significance in analyzing performance, with the example of Team final scores for 2020 and 2021, where 2021 exhibited a lower standard deviation, potentially indicating better performance.', 'The chapter covers Python data analysis using libraries like numpy and matplotlib to compute mean, median, mode, and detect outliers in a dataset.']}