title

Intro to Data Science - Crash Course for Beginners

description

Learn the basic components of Data Science in this crash course for beginners.
If you want to learn more about data science after completing this course, check out Max's Free Getting Started with Data Science Workshop: https://codingwithmax.com/webinar/data-scientist/
In this course for beginners, you will learn about:
1. Statistics: we talk about the types of data you'll encounter, types of averages, variance, standard deviation, correlation, and more.
2. Data visualization: we talk about why we need to visualize our data, and the different ways of doing it (1 variable graphs, 2 variable graphs and 3 variable graphs.)
3. Programming: we talk about why programming helps us with data science including the ease of automation and recommended Python libraries for you to get started with data science.
⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:10:52) Statistical Data Types
⌨️ (0:25:10) Types of Averages
⌨️ (0:38:55) Spread of Data
⌨️ (0:50:54) Quantiles and Percentiles
⌨️ (0:55:52) Importance of Data Visualization
⌨️ (1:05:14) One Variable Graphs
⌨️ (1:12:04) Two Variable Graphs
⌨️ (1:25:08) Three and Higher Variable Graphs
⌨️ (1:31:20) Programming
Course from Coding With Max. Check out the Coding With Max blog: https://www.codingwithmax.com/blog
Full data science course: https://codingwithmax.teachable.com/p/data-scientist-10-weeks
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detail

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actual definition is quite vague.', 'start': 14.703, 'duration': 5.782}, {'end': 28.269, 'text': 'This mini-course is designed to help those of you who are curious about data science develop a better and more specific understanding of the topic.', 'start': 21.266, 'duration': 7.003}, {'end': 33.429, 'text': 'There are definitely more advanced techniques within data science, such as machine learning,', 'start': 29.087, 'duration': 4.342}, {'end': 37.211, 'text': "but even these can be traced back to the three essential components that we'll cover.", 'start': 33.429, 'duration': 3.782}, {'end': 40.953, 'text': "Before we get straight into it, I thought I'd quickly introduce myself.", 'start': 37.951, 'duration': 3.002}, {'end': 44.195, 'text': 'My name is Max, and I work as a data scientist.', 'start': 41.673, 'duration': 2.522}, {'end': 49.938, 'text': 'After getting my degree in physics, I find myself more and more drawn into the world of data science.', 'start': 45.055, 'duration': 4.883}, {'end': 53.3, 'text': 'So, instead of diving into the realm of physics research,', 'start': 50.498, 'duration': 2.802}], 'summary': 'Mini-course on data science essentials by max, a data scientist, providing a basic understanding of the three main components.', 'duration': 50.322, 'max_score': 2.978, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2978.jpg'}], 'start': 2.978, 'title': 'Essentials of data science', 'summary': 'Introduces the essentials of data science, including a basic look into data science, its three main components, and aims to provide a more specific understanding of the topic to those curious about data science.', 'chapters': [{'end': 53.3, 'start': 2.978, 'title': 'Essentials of data science mini-course', 'summary': 'Introduces the essentials of data science, including a basic look into data science, its three main components, and aims to provide a more specific understanding of the topic to those curious about data science.', 'duration': 50.322, 'highlights': ['The mini-course provides a basic look into data science, its three main components, and aims to provide a better understanding of the topic.', 'Data science is a mainstream word with a vague definition, and even advanced techniques like machine learning can be traced back to the three essential components.', 'The instructor, Max, introduces himself and shares his background as a data scientist with a degree in physics.']}], 'duration': 50.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2978.jpg', 'highlights': ['Data science is a mainstream word with a vague definition, and even advanced techniques like machine learning can be traced back to the three essential components.', 'The mini-course provides a basic look into data science, its three main components, and aims to provide a better understanding of the topic.', 'The instructor, Max, introduces himself and shares his background as a data scientist with a degree in physics.']}, {'end': 629.913, 'segs': [{'end': 104.523, 'src': 'embed', 'start': 75.003, 'weight': 1, 'content': [{'end': 77.224, 'text': 'So what is data science?', 'start': 75.003, 'duration': 2.221}, {'end': 79.385, 'text': 'Well, data science is.', 'start': 77.664, 'duration': 1.721}, {'end': 84.848, 'text': 'you can kind of summarize it in different ways, but the main parts of it are transforming data into information.', 'start': 79.385, 'duration': 5.463}, {'end': 91.251, 'text': 'And this is a really big step, because a lot of people talk about data and big data and all of these things,', 'start': 85.328, 'duration': 5.923}, {'end': 94.433, 'text': "but data by itself isn't really that useful.", 'start': 91.251, 'duration': 3.182}, {'end': 98.417, 'text': 'until you can turn it into information.', 'start': 95.213, 'duration': 3.204}, {'end': 104.523, 'text': "and so if you just have a bunch of numbers appearing somewhere and it's just you know so much of it no one can make sense of that.", 'start': 98.417, 'duration': 6.106}], 'summary': 'Data science transforms data into useful information.', 'duration': 29.52, 'max_score': 75.003, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg75003.jpg'}, {'end': 206.224, 'src': 'embed', 'start': 175.584, 'weight': 0, 'content': [{'end': 178.846, 'text': 'And then of course be able to apply as well as understand them.', 'start': 175.584, 'duration': 3.262}, {'end': 187.911, 'text': "And so once you have this data, you know it's great, but turning it into information, into great information that you can use and directly apply.", 'start': 178.906, 'duration': 9.005}, {'end': 194.276, 'text': "that's where the real power lies, and that's also kind of the role of a data scientist.", 'start': 187.911, 'duration': 6.365}, {'end': 195.697, 'text': "so that's what the data.", 'start': 194.276, 'duration': 1.421}, {'end': 197.878, 'text': "that's what data science pretty much is.", 'start': 195.697, 'duration': 2.181}, {'end': 200.42, 'text': 'and so what does the data scientist do?', 'start': 197.878, 'duration': 2.542}, {'end': 206.224, 'text': "well, we kind of already talked about this just a little bit, but let's go over it again in more concrete examples.", 'start': 200.42, 'duration': 5.804}], 'summary': 'Data science turns data into useful information for direct application.', 'duration': 30.64, 'max_score': 175.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg175584.jpg'}, {'end': 305.67, 'src': 'embed', 'start': 264.999, 'weight': 6, 'content': [{'end': 271.723, 'text': 'then you can start to do some visualizations which help you as a data scientist, maybe see some trends or patterns already.', 'start': 264.999, 'duration': 6.724}, {'end': 278.387, 'text': "but visualizations are also really key because they let you show it to other people and they're a great means of communication.", 'start': 271.723, 'duration': 6.664}, {'end': 283.87, 'text': 'so they help both you as a data scientist as well as helping others when you try to convey this information to them.', 'start': 278.387, 'duration': 5.483}, {'end': 288.694, 'text': 'Alright. and then, finally, you have to suggest some applications of the information.', 'start': 284.79, 'duration': 3.904}, {'end': 294.519, 'text': "So it's not really enough to just be able to look at it and say like, yeah, I see it goes up and down.", 'start': 288.734, 'duration': 5.785}, {'end': 295.68, 'text': "And that's, that's good.", 'start': 294.539, 'duration': 1.141}, {'end': 297.122, 'text': 'But what does that mean?', 'start': 296.081, 'duration': 1.041}, {'end': 299.484, 'text': 'How does this transfer into something useful?', 'start': 297.442, 'duration': 2.042}, {'end': 305.67, 'text': "And that's also one of the key roles of a data scientist transferring information into knowledge.", 'start': 299.524, 'duration': 6.146}], 'summary': 'Visualizations aid data scientists in identifying trends and patterns, and communicating insights effectively.', 'duration': 40.671, 'max_score': 264.999, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg264999.jpg'}, {'end': 405.385, 'src': 'embed', 'start': 378.619, 'weight': 5, 'content': [{'end': 386.468, 'text': 'then, of course, you need to understand some key statistical terms, like the different types of means, and also understanding fluctuations in data,', 'start': 378.619, 'duration': 7.849}, {'end': 393.617, 'text': 'and the reason that this is important is because these key statistical terms give you an overview of how this data is behaving,', 'start': 386.468, 'duration': 7.149}, {'end': 397.141, 'text': 'and depending on how the data is behaving, you may want to approach it differently.', 'start': 393.617, 'duration': 3.524}, {'end': 405.385, 'text': "So if you know that your data is very clean there's very little fluctuation then if you visualize things, you can probably trust what's going on,", 'start': 397.782, 'duration': 7.603}], 'summary': 'Understanding statistical terms helps in interpreting data behavior and choosing appropriate approach.', 'duration': 26.766, 'max_score': 378.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg378619.jpg'}, {'end': 458.645, 'src': 'embed', 'start': 430.339, 'weight': 2, 'content': [{'end': 437.545, 'text': "So it's probably good that you're kind of comfortable with these things and that you can be able to get some meaning out of them.", 'start': 430.339, 'duration': 7.206}, {'end': 439.286, 'text': 'All right.', 'start': 438.405, 'duration': 0.881}, {'end': 448.213, 'text': 'And then finally, in statistics, to be able to split up group or segment data points so that when you have this big data set,', 'start': 439.346, 'duration': 8.867}, {'end': 452.357, 'text': 'you want to be able to maybe split it up into smaller things, compare different regions.', 'start': 448.213, 'duration': 4.144}, {'end': 458.645, 'text': 'um, look more into more detail into some things, and maybe you know, isolate two components, because you know.', 'start': 452.897, 'duration': 5.748}], 'summary': 'Statistics involves segmenting data for analysis and comparison.', 'duration': 28.306, 'max_score': 430.339, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg430339.jpg'}, {'end': 502.231, 'src': 'embed', 'start': 472.162, 'weight': 3, 'content': [{'end': 476.107, 'text': "so the next big thing and we've already talked about this too is data visualization.", 'start': 472.162, 'duration': 3.945}, {'end': 482.014, 'text': "And we'll see why data visualization is a really key skill for data scientists.", 'start': 477.328, 'duration': 4.686}, {'end': 488.622, 'text': "And then we're also gonna be covering different types of graphs that you can use and how you can compare different number of variables.", 'start': 482.494, 'duration': 6.128}, {'end': 489.863, 'text': 'So, for example,', 'start': 489.242, 'duration': 0.621}, {'end': 496.727, 'text': 'you can have one variable graph where you only look at one thing and you only want to look at this and you want to see how this changes.', 'start': 489.863, 'duration': 6.864}, {'end': 502.231, 'text': 'You have your typical two variable graphs, which you probably know where you have this X and a Y axis.', 'start': 497.167, 'duration': 5.064}], 'summary': 'Data visualization is a key skill for data scientists, covering various types of graphs and comparing different variables.', 'duration': 30.069, 'max_score': 472.162, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg472162.jpg'}, {'end': 550.25, 'src': 'embed', 'start': 528.571, 'weight': 4, 'content': [{'end': 538.1, 'text': 'Now not every data scientist can do this, but this is really really essential, in my opinion, to your role as a data scientist,', 'start': 528.571, 'duration': 9.529}, {'end': 541.203, 'text': 'because knowing how to program is going to make your life so much easier.', 'start': 538.1, 'duration': 3.103}, {'end': 542.724, 'text': 'If you know how to program,', 'start': 541.703, 'duration': 1.021}, {'end': 550.25, 'text': 'you can kind of take your ideas and your thoughts and you can put them into actions in the computer and you can just automate everything.', 'start': 542.724, 'duration': 7.526}], 'summary': 'Programming skills essential for data scientists, enabling automation and idea implementation.', 'duration': 21.679, 'max_score': 528.571, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg528571.jpg'}], 'start': 53.3, 'title': 'Data science essentials', 'summary': 'Discusses the role of a data scientist in transforming raw data into valuable information, emphasizing the importance of analyzing, contextualizing, and applying the data, the essential components of data science including visualization and statistics, and the necessary skills such as statistical analysis, data visualization, and programming for automation and customization.', 'chapters': [{'end': 264.999, 'start': 53.3, 'title': 'Data science: transforming data into information', 'summary': 'Discusses the role of a data scientist in transforming raw data into valuable information, emphasizing the importance of analyzing, contextualizing, and applying the data, and highlights the process of cleaning and analyzing raw data to derive valuable insights.', 'duration': 211.699, 'highlights': ['The role of a data scientist involves transforming raw data into valuable information, emphasizing the importance of analyzing, contextualizing, and applying the data. (relevance: 5)', 'Data science entails transforming data into information by cleaning and analyzing raw data to derive valuable insights, with the ability to contextualize and apply the derived information being crucial. (relevance: 4)']}, {'end': 429.278, 'start': 264.999, 'title': 'Essential components of data science', 'summary': 'Emphasizes the essential components of data science, including the importance of visualization, applications of information, and the role of statistics in understanding different data types and fluctuations.', 'duration': 164.279, 'highlights': ['The role of statistics in understanding different data types and fluctuations', 'Importance of visualization in data science', 'Applications of information in data science']}, {'end': 629.913, 'start': 430.339, 'title': 'Essential skills for data scientists', 'summary': 'Covers essential statistical components, data visualization, and the importance of programming for data scientists, emphasizing the ability to split up data points, visualize different types of graphs with multiple variables, and the significant advantage of programming for automation and customization.', 'duration': 199.574, 'highlights': ['The ability to split up group or segment data points to compare different regions and isolate components is an essential statistical component for data scientists.', 'Data visualization is a key skill for data scientists, involving the ability to compare different types of graphs with multiple variables.', 'Programming is essential for data scientists, enabling automation, customization, and independence from relying on tools built by others.']}], 'duration': 576.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg53300.jpg', 'highlights': ['The role of a data scientist involves transforming raw data into valuable information, emphasizing the importance of analyzing, contextualizing, and applying the data. (relevance: 5)', 'Data science entails transforming data into information by cleaning and analyzing raw data to derive valuable insights, with the ability to contextualize and apply the derived information being crucial. (relevance: 4)', 'The ability to split up group or segment data points to compare different regions and isolate components is an essential statistical component for data scientists.', 'Data visualization is a key skill for data scientists, involving the ability to compare different types of graphs with multiple variables.', 'Programming is essential for data scientists, enabling automation, customization, and independence from relying on tools built by others.', 'The role of statistics in understanding different data types and fluctuations', 'Importance of visualization in data science', 'Applications of information in data science']}, {'end': 1323.532, 'segs': [{'end': 681.888, 'src': 'embed', 'start': 652.168, 'weight': 2, 'content': [{'end': 655.37, 'text': "In this chapter, we're going to talk about statistical data types.", 'start': 652.168, 'duration': 3.202}, {'end': 664.716, 'text': "Now, we're going to look at the three different types of data, which are summarized as numerical, categorical, and ordinal types of data.", 'start': 656.431, 'duration': 8.285}, {'end': 672.522, 'text': "Now, these are the types of data that we talked about before, how you can't just expect your data to be kind of numerical.", 'start': 665.377, 'duration': 7.145}, {'end': 681.888, 'text': "And so we'll see numerical data, but we'll also see the two other types of data that you may be encountering in your career as a data scientist.", 'start': 673.122, 'duration': 8.766}], 'summary': 'Discusses three types of statistical data: numerical, categorical, and ordinal.', 'duration': 29.72, 'max_score': 652.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg652168.jpg'}, {'end': 729.884, 'src': 'embed', 'start': 699.079, 'weight': 1, 'content': [{'end': 699.459, 'text': 'you know.', 'start': 699.079, 'duration': 0.38}, {'end': 704.763, 'text': 'saying this plus this makes sense a is greater than b.', 'start': 699.459, 'duration': 5.304}, {'end': 707.805, 'text': 'these are, you know, all examples of numerical data.', 'start': 704.763, 'duration': 3.042}, {'end': 710.727, 'text': 'numerical data can be split up into two different segments.', 'start': 707.805, 'duration': 2.922}, {'end': 712.809, 'text': 'One of them is going to be discrete.', 'start': 711.367, 'duration': 1.442}, {'end': 717.052, 'text': 'And so discrete means the values only take on distinct numbers.', 'start': 713.249, 'duration': 3.803}, {'end': 723.318, 'text': 'And an example of this would be, you know, IQ or something like that, a measurement of IQ.', 'start': 717.393, 'duration': 5.925}, {'end': 728.023, 'text': 'Or, if you do a coin toss, the number of times that you toss heads so you can.', 'start': 723.538, 'duration': 4.485}, {'end': 729.884, 'text': 'you know you can have 15 heads.', 'start': 728.023, 'duration': 1.861}], 'summary': 'Examples of numerical data, split into discrete and continuous segments, with examples such as iq measurements and coin toss outcomes.', 'duration': 30.805, 'max_score': 699.079, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg699079.jpg'}, {'end': 802.288, 'src': 'embed', 'start': 768.726, 'weight': 0, 'content': [{'end': 771.168, 'text': 'So all of these kinds of comparisons, they make sense.', 'start': 768.726, 'duration': 2.442}, {'end': 774.39, 'text': "So that's the discrete part of numerical data.", 'start': 772.128, 'duration': 2.262}, {'end': 776.772, 'text': 'Then we have the continuous part.', 'start': 775.03, 'duration': 1.742}, {'end': 783.177, 'text': "And now the continuous part is really that values can just take on any number and they're not limited by decimal place.", 'start': 777.412, 'duration': 5.765}, {'end': 790.442, 'text': "So a value that can be like 1.1 and then the next value would be 1.2, that's not continuous.", 'start': 783.817, 'duration': 6.625}, {'end': 795.646, 'text': "That's still discrete because you have this step size of 0.1.", 'start': 790.923, 'duration': 4.723}, {'end': 802.288, 'text': 'continuous means literally every number from start to finish can be taken on.', 'start': 795.646, 'duration': 6.642}], 'summary': 'Data can be discrete or continuous, with continuous data having infinite potential values.', 'duration': 33.562, 'max_score': 768.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg768726.jpg'}, {'end': 967.689, 'src': 'embed', 'start': 943.562, 'weight': 5, 'content': [{'end': 952.192, 'text': "so that's how continuous data looks like, and it's important to understand the difference between this discrete and continuous,", 'start': 943.562, 'duration': 8.63}, {'end': 954.815, 'text': 'just because you may want to approach it differently now.', 'start': 952.192, 'duration': 2.623}, {'end': 961.003, 'text': "of course, if we're dealing with computers, our computers can't deal with infinite numbers in the decimal places.", 'start': 954.815, 'duration': 6.188}, {'end': 967.689, 'text': 'we have to cut it off somewhere, and so usually continuous data is going to be rounded off at some point.', 'start': 961.563, 'duration': 6.126}], 'summary': 'Understanding the difference between discrete and continuous data is important, as computers cannot handle infinite decimal numbers.', 'duration': 24.127, 'max_score': 943.562, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg943562.jpg'}, {'end': 1091.803, 'src': 'embed', 'start': 1063.711, 'weight': 3, 'content': [{'end': 1066.052, 'text': "it doesn't give you a third category or something.", 'start': 1063.711, 'duration': 2.341}, {'end': 1069.773, 'text': "so categories, You can't really apply math to them.", 'start': 1066.052, 'duration': 3.721}, {'end': 1072.634, 'text': 'but there are nice ways to split up or group your data.', 'start': 1069.773, 'duration': 2.861}, {'end': 1076.956, 'text': 'And they provide these nice qualitative pieces of information that are still important.', 'start': 1072.994, 'duration': 3.962}, {'end': 1082.558, 'text': "It's just you can't really go that well about plotting them on a line or something like that.", 'start': 1077.556, 'duration': 5.002}, {'end': 1086.28, 'text': 'So those are important things to note with categorical data.', 'start': 1084.019, 'duration': 2.261}, {'end': 1089.582, 'text': 'And then another example would, for example, be ethnicity.', 'start': 1086.66, 'duration': 2.922}, {'end': 1091.803, 'text': 'Or you could also have nationality.', 'start': 1090.142, 'duration': 1.661}], 'summary': 'Categorical data provides qualitative information not suitable for mathematical analysis or plotting.', 'duration': 28.092, 'max_score': 1063.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1063711.jpg'}, {'end': 1130.218, 'src': 'embed', 'start': 1107.197, 'weight': 4, 'content': [{'end': 1115.266, 'text': 'How are you going to compare nationalities? There is really no definition for comparing one type of category to another.', 'start': 1107.197, 'duration': 8.069}, {'end': 1122.128, 'text': 'all right, and so the third type of data that you can encounter is something called ordinal data.', 'start': 1116.761, 'duration': 5.367}, {'end': 1130.218, 'text': 'now, ordinal data is a mixture of numerical and categorical data, and a good example of this would be hotel ratings.', 'start': 1122.128, 'duration': 8.09}], 'summary': 'Comparing nationalities is challenging. ordinal data mixes numerical and categorical data, e.g., hotel ratings.', 'duration': 23.021, 'max_score': 1107.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1107197.jpg'}], 'start': 629.933, 'title': 'Types of numerical data and data types', 'summary': 'Explores various types of numerical data, such as discrete and continuous data, and discusses the distinctions between continuous, categorical, and ordinal data, highlighting their significance in data analysis and providing illustrative examples.', 'chapters': [{'end': 942.561, 'start': 629.933, 'title': 'Types of numerical data', 'summary': 'Discusses the different types of numerical data, including discrete and continuous data, providing examples and distinctions between the two, and its relevance to data analysis and becoming a successful data scientist.', 'duration': 312.628, 'highlights': ['Numerical data can be split into discrete and continuous segments, with examples such as IQ measurements and coin toss outcomes, illustrating distinct numbers and continuous range of values.', 'Continuous data encompasses a range of values without limitations based on decimal place, illustrated through examples such as filling a bottle with water and accelerating a car to reach a speed limit.', 'The chapter emphasizes the importance of understanding numerical data types for data analysis and the journey towards becoming a successful data scientist.']}, {'end': 1323.532, 'start': 943.562, 'title': 'Types of data: continuous, categorical, ordinal', 'summary': 'Discusses the differences between continuous, categorical, and ordinal data, emphasizing the inability to perform mathematical operations on categorical data and the challenges of defining ordinal data, illustrated with examples.', 'duration': 379.97, 'highlights': ['Categorical data lacks mathematical meaning and cannot be compared or added, but can be split up for computer understanding.', 'Ordinal data, like hotel ratings, are a mix of numerical and categorical data, and their comparison is not straightforward due to varying standards and interpretations.', 'Continuous data is important to understand due to the ability to have values in between certain places, despite being rounded off at some point for computer processing.']}], 'duration': 693.599, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg629933.jpg', 'highlights': ['Continuous data encompasses a range of values without limitations based on decimal place.', 'Numerical data can be split into discrete and continuous segments, with examples such as IQ measurements and coin toss outcomes.', 'The chapter emphasizes the importance of understanding numerical data types for data analysis and the journey towards becoming a successful data scientist.', 'Categorical data lacks mathematical meaning and cannot be compared or added, but can be split up for computer understanding.', 'Ordinal data, like hotel ratings, are a mix of numerical and categorical data, and their comparison is not straightforward due to varying standards and interpretations.', 'Continuous data is important to understand due to the ability to have values in between certain places, despite being rounded off at some point for computer processing.']}, {'end': 2068.326, 'segs': [{'end': 1617.57, 'src': 'embed', 'start': 1590.554, 'weight': 3, 'content': [{'end': 1595.018, 'text': "and all of a sudden we have like ten thousand in there that's really gonna affect our mean.", 'start': 1590.554, 'duration': 4.464}, {'end': 1600.382, 'text': 'so the mean is heavily influenced by outliers, and the bigger the outlier, the more the mean is influenced by it.', 'start': 1595.018, 'duration': 5.364}, {'end': 1601.943, 'text': 'All right.', 'start': 1601.463, 'duration': 0.48}, {'end': 1603.584, 'text': "so let's see some examples of the mean.", 'start': 1601.943, 'duration': 1.641}, {'end': 1610.707, 'text': "We'll go through a worked example first, and we can see our data set here, which is just a bunch of numbers.", 'start': 1604.144, 'duration': 6.563}, {'end': 1617.57, 'text': "And what we're gonna do to calculate the mean is we're just gonna take every single one of these numbers, and we're gonna add them up.", 'start': 1612.328, 'duration': 5.242}], 'summary': 'Outliers can heavily influence the mean, with a dataset of numbers affecting the mean calculation.', 'duration': 27.016, 'max_score': 1590.554, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1590554.jpg'}, {'end': 1659.319, 'src': 'embed', 'start': 1629.836, 'weight': 4, 'content': [{'end': 1632.218, 'text': 'which then gives us our mean, as we can see here.', 'start': 1629.836, 'duration': 2.382}, {'end': 1637.902, 'text': "So that's an example calculation of the mean, but let's see some example applications of the mean.", 'start': 1632.798, 'duration': 5.104}, {'end': 1644.988, 'text': 'So when would we use it? Well, a good application would say, if you look at the time it takes you to walk to the supermarket.', 'start': 1637.942, 'duration': 7.046}, {'end': 1649.952, 'text': 'So sometimes you walk a little bit faster and maybe it takes you 20 minutes to get there.', 'start': 1645.408, 'duration': 4.544}, {'end': 1652.033, 'text': 'Sometimes you walk a little bit slower.', 'start': 1650.292, 'duration': 1.741}, {'end': 1659.319, 'text': 'it takes you 25, but on average it takes you somewhere like 22 or maybe 22 and a half minutes, or something like that.', 'start': 1652.033, 'duration': 7.286}], 'summary': 'Mean calculation: 22.5 minutes to walk to the supermarket.', 'duration': 29.483, 'max_score': 1629.836, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1629836.jpg'}, {'end': 1826.4, 'src': 'embed', 'start': 1794.158, 'weight': 1, 'content': [{'end': 1798.661, 'text': "so it's going to be the two median values added together and then divided by two.", 'start': 1794.158, 'duration': 4.503}, {'end': 1806.267, 'text': 'So the pros of using a median value is that the median can sometimes be more accurate than the mean.', 'start': 1799.742, 'duration': 6.525}, {'end': 1808.108, 'text': "And we'll see some examples of this.", 'start': 1806.587, 'duration': 1.521}, {'end': 1810.77, 'text': 'The median also evenly splits your data.', 'start': 1808.748, 'duration': 2.022}, {'end': 1819.155, 'text': "So you're not really affected by the mean, in the sense that if you have an outlier in the mean and it drags everything to the right,", 'start': 1811.03, 'duration': 8.125}, {'end': 1826.4, 'text': 'it could be that your outlier drags things so far to the right that all of your data is to the left of the mean and only the outliers to the right.', 'start': 1819.155, 'duration': 7.245}], 'summary': 'Using median can be more accurate than mean, evenly splits data, not affected by outliers.', 'duration': 32.242, 'max_score': 1794.158, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1794158.jpg'}, {'end': 1866.406, 'src': 'embed', 'start': 1836.447, 'weight': 0, 'content': [{'end': 1840.77, 'text': "So if you have huge outliers at the beginning and at the end, it doesn't really care,", 'start': 1836.447, 'duration': 4.323}, {'end': 1845.033, 'text': "because outliers by definition aren't very common because they're outliers.", 'start': 1840.77, 'duration': 4.263}, {'end': 1851.138, 'text': "And so if you have some at the beginning or have some at the end, they're going to be very few in number, which makes them outliers.", 'start': 1845.714, 'duration': 5.424}, {'end': 1854.98, 'text': "And therefore, the median doesn't really care about outliers that much.", 'start': 1852.158, 'duration': 2.822}, {'end': 1860.603, 'text': "A con, though, is that the median doesn't really give you much information on the rest of the data.", 'start': 1856.161, 'duration': 4.442}, {'end': 1866.406, 'text': "Sure, you know what's at the center, but you don't know how does everything around me behave.", 'start': 1860.843, 'duration': 5.563}], 'summary': 'Median is robust to outliers, but lacks information on data distribution.', 'duration': 29.959, 'max_score': 1836.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1836447.jpg'}, {'end': 1937.495, 'src': 'embed', 'start': 1902.263, 'weight': 2, 'content': [{'end': 1905.687, 'text': "And so that's why we see our median value here is 26.", 'start': 1902.263, 'duration': 3.424}, {'end': 1907.89, 'text': "It's located directly in the center.", 'start': 1905.687, 'duration': 2.203}, {'end': 1911.033, 'text': 'Now, what is the median useful for??', 'start': 1908.991, 'duration': 2.042}, {'end': 1918.501, 'text': 'Well, the median is often used, if you look at, you know, household incomes for a country, because if you were to use the mean,', 'start': 1911.374, 'duration': 7.127}, {'end': 1923.225, 'text': 'then these billionaires, they would just completely you know.', 'start': 1918.501, 'duration': 4.724}, {'end': 1928.651, 'text': 'they would give you a false description of what really an average household income is.', 'start': 1923.225, 'duration': 5.426}, {'end': 1937.495, 'text': 'because Normally, if you have you know, like an average value and you can say, oh, the average household income from this family would be, say, $40,', 'start': 1928.651, 'duration': 8.844}], 'summary': 'Median value is 26, useful for avoiding skew by outliers in income data.', 'duration': 35.232, 'max_score': 1902.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1902263.jpg'}], 'start': 1323.952, 'title': 'Data types and averages, median and applications', 'summary': 'Discusses different data types and mean calculation, emphasizing its susceptibility to outliers. it also explores median, its applications, advantages, and ability to mitigate the impact of outliers for a more accurate representation.', 'chapters': [{'end': 1770.102, 'start': 1323.952, 'title': 'Types of data and averages', 'summary': 'Discusses different types of data (numerical, discrete, continuous) and explains the concept of mean as an average, highlighting its calculation method, application examples, and its susceptibility to outliers.', 'duration': 446.15, 'highlights': ['The mean is the typical average that sums all values and divides by the total number of values, taking into account all data, but it is heavily influenced by outliers.', 'The weight of an adult and the height of a child are examples of continuous numerical data, while the number of coins in a wallet represents discrete numerical data.', 'Application examples of the mean include calculating the time taken to walk to a place, exam scores for a class, and estimating the average amount of chocolate required during a sweet craving.']}, {'end': 2068.326, 'start': 1770.562, 'title': 'Understanding median and its applications', 'summary': 'Explains the concept of median, its advantages, and its applications in representing central tendencies, such as household incomes, travel distances, and purchase prices, with a focus on its ability to mitigate the impact of outliers and provide a more accurate representation compared to mean.', 'duration': 297.764, 'highlights': ['The median can sometimes be more accurate than the mean, as it evenly splits the data and is not affected by outliers, making it a better representation of central tendencies.', 'Household incomes and travel distances are examples of real-world applications where using the median provides a more accurate representation compared to the mean, especially in mitigating the impact of outliers.', "The median's ability to provide a more realistic look at central tendencies compared to the mean is emphasized through examples of household incomes, travel distances, and purchase prices, highlighting its relevance in various scenarios."]}], 'duration': 744.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg1323952.jpg', 'highlights': ['The median evenly splits the data and is not affected by outliers.', 'The median provides a more accurate representation compared to the mean.', 'Household incomes and travel distances are examples where the median is more accurate than the mean.', 'The mean is heavily influenced by outliers and may not provide an accurate representation.', 'Application examples of the mean include calculating time taken to walk, exam scores, and average chocolate consumption.']}, {'end': 2985.229, 'segs': [{'end': 2134.966, 'src': 'embed', 'start': 2110.083, 'weight': 0, 'content': [{'end': 2115.788, 'text': 'And you know that the mode is gonna be the US because there are five people from the US.', 'start': 2110.083, 'duration': 5.705}, {'end': 2125.055, 'text': "So mode is the great average that's not only applicable to numerical data in this sense, but you can technically also apply it to categories.", 'start': 2116.008, 'duration': 9.047}, {'end': 2127.437, 'text': 'or to ordinal numbers if you wanted.', 'start': 2125.996, 'duration': 1.441}, {'end': 2134.966, 'text': 'So that you can say the most common country that we have or the average kind of country that we would expect here is the US.', 'start': 2127.798, 'duration': 7.168}], 'summary': "The mode of the group's country is the us, with five people from there.", 'duration': 24.883, 'max_score': 2110.083, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2110083.jpg'}, {'end': 2190.849, 'src': 'embed', 'start': 2158.411, 'weight': 1, 'content': [{'end': 2161.752, 'text': 'So in discrete numbers, values reoccur often.', 'start': 2158.411, 'duration': 3.341}, {'end': 2163.293, 'text': "And so it's good to use the mode.", 'start': 2161.852, 'duration': 1.441}, {'end': 2170.075, 'text': "A con of the mode is going to be that it doesn't really again give you a good understanding of the rest of the data,", 'start': 2164.633, 'duration': 5.442}, {'end': 2172.076, 'text': 'similar to what we had for the median.', 'start': 2170.075, 'duration': 2.001}, {'end': 2176.918, 'text': "But also, it's not really applicable if you just have a bunch of different types of data.", 'start': 2172.676, 'duration': 4.242}, {'end': 2178.999, 'text': "then there isn't really going to be a mode.", 'start': 2177.518, 'duration': 1.481}, {'end': 2181.842, 'text': "if there's not enough of each data.", 'start': 2178.999, 'duration': 2.843}, {'end': 2183.123, 'text': "it's not really good to use the mode.", 'start': 2181.842, 'duration': 1.281}, {'end': 2190.849, 'text': "you don't want to, you know, have thousands of data points and the most reoccurring value it reoccurs like three times.", 'start': 2183.123, 'duration': 7.726}], 'summary': 'Using mode in discrete data may not be useful due to limited occurrences.', 'duration': 32.438, 'max_score': 2158.411, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2158411.jpg'}, {'end': 2376.494, 'src': 'embed', 'start': 2342.189, 'weight': 7, 'content': [{'end': 2346.271, 'text': "then we're going to move on to understanding what variance and standard deviation means,", 'start': 2342.189, 'duration': 4.082}, {'end': 2349.173, 'text': "and then finally we'll look at covariance as well as correlation.", 'start': 2346.271, 'duration': 2.902}, {'end': 2354.017, 'text': "all right, so let's start off with the range and domain.", 'start': 2350.154, 'duration': 3.863}, {'end': 2355.999, 'text': "now let's start off with the range.", 'start': 2354.017, 'duration': 1.982}, {'end': 2361.984, 'text': 'so the range is basically the difference between the maximum and the minimum value, um in our data set.', 'start': 2355.999, 'duration': 5.985}, {'end': 2364.266, 'text': "so that's that's kind of simple to think about.", 'start': 2361.984, 'duration': 2.282}, {'end': 2367.849, 'text': "um, so let's just kind of go through this with a worked example.", 'start': 2364.266, 'duration': 3.583}, {'end': 2376.494, 'text': "let's set up a company in the town and this is the only company in the town and the owner of the company earns a salary of 200k a year.", 'start': 2367.849, 'duration': 8.645}], 'summary': 'Understanding range, domain, and variance in data analysis.', 'duration': 34.305, 'max_score': 2342.189, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2342189.jpg'}, {'end': 2432.628, 'src': 'embed', 'start': 2403.967, 'weight': 2, 'content': [{'end': 2407.49, 'text': "So that's how big our salary can change.", 'start': 2403.967, 'duration': 3.523}, {'end': 2411.973, 'text': 'So if we start at 15K, it can go all the way up to 200K.', 'start': 2407.55, 'duration': 4.423}, {'end': 2416.296, 'text': "So that's 185K range of salary that people in this company can have.", 'start': 2411.993, 'duration': 4.303}, {'end': 2425.202, 'text': 'Alright, and the domain is going to be the values that our data points can take on or the region that our data points lie in.', 'start': 2417.356, 'duration': 7.846}, {'end': 2432.628, 'text': 'So if we look at this example again, our domain is going to start at 15k and go up to 200k.', 'start': 2426.303, 'duration': 6.325}], 'summary': 'Salaries at the company range from 15k to 200k.', 'duration': 28.661, 'max_score': 2403.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2403967.jpg'}, {'end': 2530.78, 'src': 'embed', 'start': 2502.835, 'weight': 5, 'content': [{'end': 2511.563, 'text': 'And it looks at each mean value and it looks at how different each value is from the mean value, and then it gives us the variance.', 'start': 2502.835, 'duration': 8.728}, {'end': 2513.985, 'text': "it does some calculation and we don't really need to know the formula.", 'start': 2511.563, 'duration': 2.422}, {'end': 2517.889, 'text': "it's more important right now just to understand the concept of variance.", 'start': 2513.985, 'duration': 3.904}, {'end': 2522.873, 'text': 'and so what variance really tells us is it tells us how much our data can fluctuate.', 'start': 2517.889, 'duration': 4.984}, {'end': 2530.78, 'text': 'so if we have a high variance, that means a lot of our values differ greatly from the mean value and that will make our variance bigger.', 'start': 2522.873, 'duration': 7.907}], 'summary': 'Variance measures data fluctuation around the mean value.', 'duration': 27.945, 'max_score': 2502.835, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2502835.jpg'}, {'end': 2687.214, 'src': 'embed', 'start': 2664.659, 'weight': 6, 'content': [{'end': 2676.927, 'text': "and so that's how we can kind of use the variance and the standard deviation or the standard deviation to give us a little bit more perspective on our data and kind of allow us to infer some stuff about our data.", 'start': 2664.659, 'duration': 12.268}, {'end': 2681.11, 'text': "All right, so let's talk about covariance and correlation.", 'start': 2676.947, 'duration': 4.163}, {'end': 2687.214, 'text': 'so covariance will or already has the name variance in it.', 'start': 2682.531, 'duration': 4.683}], 'summary': 'Using variance and standard deviation to gain insights on data; discussing covariance and correlation.', 'duration': 22.555, 'max_score': 2664.659, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2664659.jpg'}, {'end': 2798.239, 'src': 'embed', 'start': 2770.662, 'weight': 4, 'content': [{'end': 2775.965, 'text': "The important thing to just keep in mind is that we're looking at one and we're seeing how much that changes,", 'start': 2770.662, 'duration': 5.303}, {'end': 2779.467, 'text': "and we're seeing how much that change affects the other one.", 'start': 2775.965, 'duration': 3.502}, {'end': 2781.328, 'text': 'All right.', 'start': 2780.528, 'duration': 0.8}, {'end': 2787.833, 'text': 'So there are different types of correlation values that we can have, and they can range anywhere between negative one and one or so.', 'start': 2781.669, 'duration': 6.164}, {'end': 2789.634, 'text': 'their domain is between negative one and one.', 'start': 2787.833, 'duration': 1.801}, {'end': 2794.857, 'text': 'And a correlation of one means a perfect positive correlation.', 'start': 2790.214, 'duration': 4.643}, {'end': 2798.239, 'text': 'So that means when one variable goes up, the other goes up.', 'start': 2794.917, 'duration': 3.322}], 'summary': 'Analyzing correlation values ranging from -1 to 1, with 1 indicating perfect positive correlation.', 'duration': 27.577, 'max_score': 2770.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2770662.jpg'}], 'start': 2068.426, 'title': 'Understanding data analysis', 'summary': 'Covers the concept of mode as an average, its pros and cons, its applications in various scenarios, and introduces the topics of range, domain, variance, standard deviation, covariance, and correlation. it also explains the concepts of covariance and correlation, emphasizing that correlation does not imply causation.', 'chapters': [{'end': 2361.984, 'start': 2068.426, 'title': 'Understanding averages and their applications', 'summary': 'Covers the concept of mode as an average, its pros and cons, and its applications in various scenarios, such as employee income, election outcomes, and data visualization, while also introducing the topics of range, domain, variance, standard deviation, covariance, and correlation.', 'duration': 293.558, 'highlights': ['The mode is the most common value in the data and is not only applicable to numerical data but can also be applied to categories or ordinal numbers.', 'The mode is useful for situations where recurring values occur frequently, such as in discrete numbers.', 'The mode can be utilized to determine the most common value in a dataset, such as in the case of employee income at a company or the outcome of an election.', 'The chapter introduces the topics of range, domain, variance, standard deviation, covariance, and correlation as part of understanding the spread of data.']}, {'end': 2664.659, 'start': 2361.984, 'title': 'Understanding data variability', 'summary': 'Explains the concepts of range, domain, variance, and standard deviation using examples of salary and height, emphasizing how they measure the variability of data and its impact on understanding possible outcomes and differences in populations.', 'duration': 302.675, 'highlights': ['The range of salary in the company is 185K, indicating the potential salary fluctuation from 15K to 200K.', 'The domain of salary in the company is from 15K to 200K, defining the possible salary values within this range.', 'Variance measures the fluctuation of data from the mean, with higher variance indicating greater deviation from the mean value.', 'Standard deviation reflects the dispersion of data around the mean, with lower standard deviation suggesting values closer to the mean.']}, {'end': 2985.229, 'start': 2664.659, 'title': 'Covariance and correlation in data analysis', 'summary': 'Explains the concepts of covariance and correlation, ranging between -1 and 1, to measure the relationship between two variables, and emphasizes that correlation does not imply causation.', 'duration': 320.57, 'highlights': ['Correlation values range between -1 and 1, with 1 indicating a perfect positive correlation, 0 indicating no correlation, and -1 indicating a perfect negative correlation.', 'Covariance measures the degree of change between two different variables, while correlation normalizes the covariance by dividing by the standard deviation of each variable.', 'The chapter emphasizes that correlation does not imply causation, and provides an example to illustrate this point.']}], 'duration': 916.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2068426.jpg', 'highlights': ['The mode is the most common value in the data and is applicable to numerical data, categories, or ordinal numbers.', 'The mode is useful for situations with recurring values, such as in discrete numbers or employee income at a company.', 'The range of salary in the company is 185K, indicating potential salary fluctuation from 15K to 200K.', 'The domain of salary in the company is from 15K to 200K, defining the possible salary values within this range.', 'Correlation values range between -1 and 1, with 1 indicating a perfect positive correlation, 0 indicating no correlation, and -1 indicating a perfect negative correlation.', 'Variance measures the fluctuation of data from the mean, with higher variance indicating greater deviation from the mean value.', 'Covariance measures the degree of change between two different variables, while correlation normalizes the covariance by dividing by the standard deviation of each variable.', 'The chapter introduces the topics of range, domain, variance, standard deviation, covariance, and correlation as part of understanding the spread of data.']}, {'end': 3862.202, 'segs': [{'end': 3032.105, 'src': 'embed', 'start': 3008.92, 'weight': 1, 'content': [{'end': 3017.322, 'text': 'the other value goes up with it and then the closer we reach zero, the less related or the less correlation there is between them,', 'start': 3008.92, 'duration': 8.402}, {'end': 3021.263, 'text': 'and then the more kind of variance we have in data.', 'start': 3017.322, 'duration': 3.941}, {'end': 3029.024, 'text': "so we'll notice, for the case of perfect correlation, which is the one, or the case of perfect anti-correlation, which is the minus one,", 'start': 3021.263, 'duration': 7.761}, {'end': 3032.105, 'text': 'which again we have, the example of more coffee, less tired.', 'start': 3029.024, 'duration': 3.081}], 'summary': 'As values approach zero, correlation decreases, leading to more variance in data.', 'duration': 23.185, 'max_score': 3008.92, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3008920.jpg'}, {'end': 3087.092, 'src': 'embed', 'start': 3057.582, 'weight': 0, 'content': [{'end': 3058.763, 'text': "All right, so let's get started.", 'start': 3057.582, 'duration': 1.181}, {'end': 3060.865, 'text': 'So what are quantiles?', 'start': 3059.404, 'duration': 1.461}, {'end': 3068.013, 'text': "Well, quantiles allow us to split our data into certain regions, that if we're dealing with probability,", 'start': 3061.225, 'duration': 6.788}, {'end': 3070.035, 'text': 'they all have the same probability of occurring.', 'start': 3068.013, 'duration': 2.022}, {'end': 3075.721, 'text': "Or if we're just dealing with, you know, sizes of data, we want to split our data into equal regions.", 'start': 3070.575, 'duration': 5.146}, {'end': 3085.59, 'text': "So that's what we can do with quantiles is just splitting everything up so that every time we split it, you know, we have equal amounts of data.", 'start': 3076.241, 'duration': 9.349}, {'end': 3087.092, 'text': 'All right.', 'start': 3085.63, 'duration': 1.462}], 'summary': 'Quantiles split data into equal regions for same probability or size.', 'duration': 29.51, 'max_score': 3057.582, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3057582.jpg'}, {'end': 3422.462, 'src': 'embed', 'start': 3394.483, 'weight': 3, 'content': [{'end': 3398.525, 'text': "You know, if you think about how faster computers are, they're in the gigahertz range.", 'start': 3394.483, 'duration': 4.042}, {'end': 3400.827, 'text': 'So giga means billions.', 'start': 3398.586, 'duration': 2.241}, {'end': 3403.889, 'text': 'So they just do billions of things every second.', 'start': 3400.887, 'duration': 3.002}, {'end': 3407.111, 'text': "and so they're really good for doing repetitive things,", 'start': 3404.409, 'duration': 2.702}, {'end': 3419.201, 'text': 'because they can do them so fast and then we can give them these logical tasks in terms of programming and we give them a structure and they just do it and they can do it over and over and over again.', 'start': 3407.111, 'duration': 12.09}, {'end': 3422.462, 'text': "they're not going to mess up, can just repeat the same thing,", 'start': 3419.201, 'duration': 3.261}], 'summary': 'Faster computers operate in the gigahertz range, doing billions of tasks every second, making them ideal for repetitive and logical tasks in programming.', 'duration': 27.979, 'max_score': 3394.483, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3394483.jpg'}, {'end': 3537.984, 'src': 'embed', 'start': 3512.171, 'weight': 4, 'content': [{'end': 3523.337, 'text': "now, another thing that's really good for humans is we are very creative and through that creativity we can also use memory and bring it outside knowledge,", 'start': 3512.171, 'duration': 11.166}, {'end': 3528.86, 'text': "and we can also use a general understanding, and so these are all things that computers can't do.", 'start': 3523.337, 'duration': 5.523}, {'end': 3537.984, 'text': "so computers are kind of a means of getting stuff to us, but once it's actually there, it's our job to use our pattern recognition abilities.", 'start': 3528.86, 'duration': 9.124}], 'summary': "Humans' creativity and pattern recognition abilities are unique compared to computers.", 'duration': 25.813, 'max_score': 3512.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3512171.jpg'}, {'end': 3756.672, 'src': 'embed', 'start': 3733.52, 'weight': 2, 'content': [{'end': 3742.789, 'text': 'But just this skill of being able to present data both for yourself as well as for other people is very, very important for a data scientist.', 'start': 3733.52, 'duration': 9.269}, {'end': 3748.531, 'text': "and then we go over to interpreting data, and we've kind of touched on this in the last section already.", 'start': 3743.73, 'duration': 4.801}, {'end': 3756.672, 'text': 'but really, with data visualization, it just allows you to see this data and it allows you to apply some reasoning to the system.', 'start': 3748.531, 'duration': 8.141}], 'summary': 'Data visualization is crucial for data scientists to present and interpret data effectively.', 'duration': 23.152, 'max_score': 3733.52, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3733520.jpg'}], 'start': 2985.629, 'title': 'Correlation, quantiles, and data visualization', 'summary': 'Covers the concept of correlation and quantiles, including examples of perfect and less related correlations, and introduces the use of quantiles and percentiles for data splitting and normalization. it also highlights the importance of data visualization in identifying patterns and interpreting data for a deeper understanding.', 'chapters': [{'end': 3350.951, 'start': 2985.629, 'title': 'Correlation and quantiles', 'summary': 'Explains the concept of correlation, showcasing perfect and less related correlations, and introduces quantiles and percentiles, demonstrating their use in data splitting and normalization for tests, with examples of quartiles and percentiles in test scores.', 'duration': 365.322, 'highlights': ['The chapter explains the concept of correlation, showcasing perfect and less related correlations.', 'The chapter introduces quantiles and percentiles, demonstrating their use in data splitting and normalization for tests.', 'The chapter provides examples of quartiles and percentiles in test scores.']}, {'end': 3862.202, 'start': 3352.151, 'title': 'Importance of data visualization', 'summary': "Emphasizes the role of computers in fast calculations and the human's inherent pattern recognition abilities, advocating for the use of data visualization to identify patterns and interpret data, resulting in a deeper understanding of the data.", 'duration': 510.051, 'highlights': ['Computers are much faster at calculating than humans, operating in the gigahertz range and excelling at repetitive tasks, making them ideal for number crunching and logical tasks.', 'Humans have evolved to excel in identifying patterns, utilizing creativity, memory, and outside knowledge, attributes that computers lack, making them essential for interpreting data.', 'Data visualization is essential to invoke human characteristics, enabling the identification of patterns in data, and serves as an effective means of conveying information to others, especially those less trained in data analysis.']}], 'duration': 876.573, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg2985629.jpg', 'highlights': ['The chapter introduces quantiles and percentiles for data splitting and normalization.', 'The chapter explains the concept of correlation, showcasing perfect and less related correlations.', 'Data visualization is essential for identifying patterns and interpreting data.', 'Computers are much faster at calculating than humans, operating in the gigahertz range.', 'Humans excel in identifying patterns, utilizing creativity, memory, and outside knowledge.']}, {'end': 4750.331, 'segs': [{'end': 3980.683, 'src': 'heatmap', 'start': 3891.104, 'weight': 0, 'content': [{'end': 3894.265, 'text': 'The computer can help you do the number crunching.', 'start': 3891.104, 'duration': 3.161}, {'end': 3898.067, 'text': 'A computer can help you set up the visualizations, and it can plot whatever you want for it.', 'start': 3894.285, 'duration': 3.782}, {'end': 3906.172, 'text': "But ultimately, it's up to you to choose the right visualizations to do, to look at the data, to be able to communicate the visualization as well.", 'start': 3898.567, 'duration': 7.605}, {'end': 3908.633, 'text': 'All of those things are up to you.', 'start': 3906.552, 'duration': 2.081}, {'end': 3912.056, 'text': "And so that's why the human is so, so important in data science.", 'start': 3908.793, 'duration': 3.263}, {'end': 3916.578, 'text': "In this tutorial, we're going to look at one variable graph.", 'start': 3913.256, 'duration': 3.322}, {'end': 3920.901, 'text': "So we're actually going to see some of the types of graphs that we can do.", 'start': 3916.618, 'duration': 4.283}, {'end': 3925.444, 'text': 'that we talked about in our last tutorial, where we just looked at the importance of data visualization.', 'start': 3920.901, 'duration': 4.543}, {'end': 3926.184, 'text': "So now we're going to go.", 'start': 3925.564, 'duration': 0.62}, {'end': 3934.894, 'text': 'into data visualization and look at the types of graphs that you may want to use or that you may want to choose from all right,', 'start': 3927.285, 'duration': 7.609}, {'end': 3942.322, 'text': "and so the graphs that we're going to look at in terms of one variable graphs are going to be histograms, bar plots and pie charts.", 'start': 3934.894, 'duration': 7.428}, {'end': 3944.764, 'text': "so let's get started with histograms.", 'start': 3942.322, 'duration': 2.442}, {'end': 3948.606, 'text': 'Now we can see an example of a histogram on the right,', 'start': 3945.505, 'duration': 3.101}, {'end': 3957.11, 'text': "but what's really cool about histograms is that it shows us the distribution of the data and it shows us the distribution across all the values in our data.", 'start': 3948.606, 'duration': 8.504}, {'end': 3962.993, 'text': 'It shows us what happens the least and it also shows us what happens the most.', 'start': 3958.071, 'duration': 4.922}, {'end': 3969.396, 'text': "Histograms, they let us see where our data is concentrated and they also let us see how it's distributed.", 'start': 3963.573, 'duration': 5.823}, {'end': 3973.177, 'text': 'Through this, it shows a general behavior.', 'start': 3970.556, 'duration': 2.621}, {'end': 3980.683, 'text': 'really what a histogram is is it looks at each value and it just looks at how often the value has occurred.', 'start': 3974.878, 'duration': 5.805}], 'summary': 'Data science emphasizes human interpretation of visualizations, such as histograms, bar plots, and pie charts.', 'duration': 89.579, 'max_score': 3891.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3891104.jpg'}, {'end': 4128.212, 'src': 'embed', 'start': 4099.843, 'weight': 1, 'content': [{'end': 4104.725, 'text': "All right, so the next one variable plot that we'll look at is going to be bar plots.", 'start': 4099.843, 'duration': 4.882}, {'end': 4113.447, 'text': "And so what bar plots do is they may look a little bit similar to histograms at first, but they're very different in some sense,", 'start': 4105.685, 'duration': 7.762}, {'end': 4117.009, 'text': 'because bar plots allow us to compare across different groups.', 'start': 4113.447, 'duration': 3.562}, {'end': 4122.37, 'text': "And so that's what we see on the x axis down there is we look at different groups.", 'start': 4117.129, 'duration': 5.241}, {'end': 4124.151, 'text': 'And so we use the same variable.', 'start': 4122.49, 'duration': 1.661}, {'end': 4128.212, 'text': 'but we can compare that variable over different groups.', 'start': 4125.571, 'duration': 2.641}], 'summary': 'Bar plots compare a variable across different groups.', 'duration': 28.369, 'max_score': 4099.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg4099843.jpg'}, {'end': 4227.249, 'src': 'embed', 'start': 4200.492, 'weight': 3, 'content': [{'end': 4207.039, 'text': 'And what pie charts are allows to do is they allow us to section up our data and then we can kind of split it into percentiles.', 'start': 4200.492, 'duration': 6.547}, {'end': 4211.163, 'text': 'And because of this, we can see what our data is made up of.', 'start': 4207.559, 'duration': 3.604}, {'end': 4214.827, 'text': 'So the whole pie corresponds to 100%.', 'start': 4211.444, 'duration': 3.383}, {'end': 4222.308, 'text': "And then we kind of cut it down into different slices and through that slicing and then hopefully also color coding, like we've done here,", 'start': 4214.827, 'duration': 7.481}, {'end': 4227.249, 'text': 'and maybe even labeling, or most definitely labeling, so that you know what slice corresponds,', 'start': 4222.308, 'duration': 4.941}], 'summary': 'Pie charts visually represent data in percentiles and color-coded slices.', 'duration': 26.757, 'max_score': 4200.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg4200492.jpg'}, {'end': 4382.149, 'src': 'embed', 'start': 4359.48, 'weight': 2, 'content': [{'end': 4370.544, 'text': "so we're always plotting one variable on the x-axis and then another variable on the y-axis and it just pretty much allows us to see how the data is distributed for these two variables.", 'start': 4359.48, 'duration': 11.064}, {'end': 4373.665, 'text': 'and then through that we can also see more dense areas.', 'start': 4370.544, 'duration': 3.121}, {'end': 4378.407, 'text': 'we can also see some sparse areas and we can also look at correlations.', 'start': 4373.665, 'duration': 4.742}, {'end': 4382.149, 'text': 'so maybe you remember in the lecture we talked about correlations.', 'start': 4378.407, 'duration': 3.742}], 'summary': 'Scatter plot allows visualizing data distribution and identifying correlations between variables.', 'duration': 22.669, 'max_score': 4359.48, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg4359480.jpg'}], 'start': 3864.284, 'title': 'Importance of data visualization in data science', 'summary': 'Explains the significance of data visualization, covering types of one-variable graphs such as histograms, bar plots, and pie charts. it also discusses scatter plots and their role in visualizing the spread of data between two variables and identifying correlations. additionally, it emphasizes the importance of understanding when to use line plots and scatter plots and provides examples of their applications.', 'chapters': [{'end': 4506.844, 'start': 3864.284, 'title': 'Importance of data visualization in data science', 'summary': 'Explains the importance of data visualization in data science, covering the types of one-variable graphs such as histograms, bar plots, and pie charts, and their significance in understanding data distribution and comparison across different groups. it also discusses scatter plots and their role in visualizing the spread of data between two variables and identifying correlations.', 'duration': 642.56, 'highlights': ['Histograms demonstrate the distribution of data and indicate the concentration and spread, helping to understand the frequency and occurrence of values.', "Bar plots enable comparison across different groups, allowing the visualization of a single variable's variations within different categories or groups.", 'Pie charts offer a segmented view of data, allowing the visualization of the composition of data in terms of percentages and identifying dominant and minority categories.', 'Scatter plots visualize the spread of data points for two variables, allowing the identification of correlations, dense areas, and sparse areas.']}, {'end': 4750.331, 'start': 4506.844, 'title': 'Understanding scatter and line plots', 'summary': 'Discusses the use of scatter plots for visualizing relationships between variables, emphasizing the importance of understanding when to use line plots and scatter plots and providing examples of their applications.', 'duration': 243.487, 'highlights': ['Scatter plots are useful for visualizing the relationship between variables, such as car price and sales, income and years of education, and distance traveled versus time to work.', 'Line plots are advantageous for depicting trends and evolutions in data, particularly when data points are connected, as seen in examples like distance versus time and profit versus number of employees.']}], 'duration': 886.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg3864284.jpg', 'highlights': ['Histograms demonstrate data distribution and concentration, aiding in understanding frequency and occurrence.', 'Bar plots enable comparison across different groups, visualizing variations within categories.', 'Scatter plots visualize data spread for two variables, identifying correlations and dense areas.', 'Pie charts offer a segmented view of data, visualizing composition in percentages.']}, {'end': 5189.202, 'segs': [{'end': 4777.572, 'src': 'embed', 'start': 4751.292, 'weight': 0, 'content': [{'end': 4756.757, 'text': 'And then what we can see on the right here is we can look at your creativity and how that changes with stress.', 'start': 4751.292, 'duration': 5.465}, {'end': 4760.78, 'text': 'So we can see that the more stressed out you are, the less creative you are.', 'start': 4757.197, 'duration': 3.583}, {'end': 4766.926, 'text': "And here it's also good to use a line plot because you kind of gradually advance in stress.", 'start': 4760.8, 'duration': 6.126}, {'end': 4769.628, 'text': 'And so each point in stress is kind of related.', 'start': 4767.346, 'duration': 2.282}, {'end': 4773.591, 'text': 'And the higher you go up in stress, the lower you go down in creativity.', 'start': 4769.968, 'duration': 3.623}, {'end': 4777.572, 'text': "And so there's this kind of relation where we can see this evolution.", 'start': 4773.631, 'duration': 3.941}], 'summary': 'Stress negatively impacts creativity, as shown by a gradual decrease in creativity with increasing stress levels.', 'duration': 26.28, 'max_score': 4751.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg4751292.jpg'}, {'end': 4897.936, 'src': 'embed', 'start': 4872.039, 'weight': 1, 'content': [{'end': 4877.922, 'text': "But really, if you do a scatter plot and the same thing happens 100 times, it's just going to look like one dot.", 'start': 4872.039, 'duration': 5.883}, {'end': 4889.269, 'text': "Whereas for two-dimensional histograms we can see that it's not just happening once, but we can actually see the frequency of those variables,", 'start': 4878.443, 'duration': 10.826}, {'end': 4891.291, 'text': 'or those two variables together.', 'start': 4889.269, 'duration': 2.022}, {'end': 4897.936, 'text': 'So an example of a two-dimensional histogram would be if we look at ticket prices versus tickets sold.', 'start': 4892.351, 'duration': 5.585}], 'summary': 'Two-dimensional histograms show frequency of variables, e.g. ticket prices vs tickets sold.', 'duration': 25.897, 'max_score': 4872.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg4872039.jpg'}, {'end': 5066.711, 'src': 'embed', 'start': 5040.18, 'weight': 3, 'content': [{'end': 5046.223, 'text': 'And some teams may be much more expensive or their ticket prices may be much more expensive than other ones.', 'start': 5040.18, 'duration': 6.043}, {'end': 5049.645, 'text': 'And so we can compare these ticket prices using box and whisker plots.', 'start': 5046.383, 'duration': 3.262}, {'end': 5054.687, 'text': 'And then we can see, you know, what is the higher end of these costs.', 'start': 5050.205, 'duration': 4.482}, {'end': 5056.567, 'text': 'So those are going to be the more luxurious seats.', 'start': 5054.727, 'duration': 1.84}, {'end': 5061.209, 'text': 'And then we go to the bottom and those are going to be the less luxurious seats, probably the ones where you stand.', 'start': 5056.607, 'duration': 4.602}, {'end': 5066.711, 'text': 'And then you have middle values depending on, you know, the standard seats and where you are in the stadium.', 'start': 5061.549, 'duration': 5.162}], 'summary': 'Comparing ticket prices using box and whisker plots to analyze cost variations and seat types in stadiums.', 'duration': 26.531, 'max_score': 5040.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5040180.jpg'}, {'end': 5160.778, 'src': 'embed', 'start': 5129.288, 'weight': 4, 'content': [{'end': 5133.032, 'text': 'is they allow us to plot two variables against each other, in the X and the Y,', 'start': 5129.288, 'duration': 3.744}, {'end': 5139.879, 'text': 'and they allow us to show an intensity or a size or something like that in the Z direction or towards us.', 'start': 5133.032, 'duration': 6.847}, {'end': 5147.487, 'text': "So an example of this, which is kind of what I've tried to illustrate on the right, is a customer moving through a store.", 'start': 5140.539, 'duration': 6.948}, {'end': 5152.773, 'text': 'And so we can track the path of the customer in the X and Y direction of the store.', 'start': 5148.007, 'duration': 4.766}, {'end': 5155.456, 'text': "So we can kind of get this bird's eye view and see where they move to.", 'start': 5152.793, 'duration': 2.663}, {'end': 5160.778, 'text': 'And the darker spots actually tell us the positions where they spend more time at.', 'start': 5156.416, 'duration': 4.362}], 'summary': 'Plotting x and y variables to track customer movement in a store, showing areas of prolonged stay.', 'duration': 31.49, 'max_score': 5129.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5129288.jpg'}], 'start': 4751.292, 'title': 'Visualizing data for insights', 'summary': 'Covers data visualization and analysis techniques for stress impact on creativity, frequency occurrences and drop-offs using line plots and two-dimensional histograms. it also discusses visualizing ticket prices, store movement, ticket sales trends, and customer movement patterns using various visualization techniques like box and whisker plots and heat maps.', 'chapters': [{'end': 4897.936, 'start': 4751.292, 'title': 'Data visualization and analysis techniques', 'summary': 'Discusses the impact of stress on creativity using line plots and the advantages of two-dimensional histograms in visualizing the frequency of variable combinations, aiding in pinpointing frequency occurrences and drop-offs.', 'duration': 146.644, 'highlights': ['Line plots illustrate the negative correlation between stress and creativity, showing a gradual decrease in creativity as stress levels increase, providing a clear visualization of the evolution of stress and creativity.', 'Two-dimensional histograms allow for pinpointing frequency occurrences and drop-offs for specific value combinations, offering a more comprehensive visualization compared to scatter plots where frequency occurrences may not be as apparent.']}, {'end': 5189.202, 'start': 4898.316, 'title': 'Visualizing ticket prices and store movement', 'summary': 'Discusses visualizing ticket prices and store movement using two-dimensional histograms, box and whisker plots, and heat maps, providing insights into ticket sales trends and customer movement patterns.', 'duration': 290.886, 'highlights': ['Two-dimensional histograms help visualize ticket sales trends based on price and quantity, revealing popular ticket prices and bands.', 'Box and whisker plots enable comparison of ticket prices for different football teams, showcasing the spread of prices and identifying the luxurious and standard seat categories.', 'Heat maps provide insights into customer movement within a store by plotting X and Y coordinates, and indicating intensity or time spent at different locations.']}], 'duration': 437.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg4751292.jpg', 'highlights': ['Line plots illustrate the negative correlation between stress and creativity, showing a gradual decrease in creativity as stress levels increase, providing a clear visualization of the evolution of stress and creativity.', 'Two-dimensional histograms help visualize ticket sales trends based on price and quantity, revealing popular ticket prices and bands.', 'Two-dimensional histograms allow for pinpointing frequency occurrences and drop-offs for specific value combinations, offering a more comprehensive visualization compared to scatter plots where frequency occurrences may not be as apparent.', 'Box and whisker plots enable comparison of ticket prices for different football teams, showcasing the spread of prices and identifying the luxurious and standard seat categories.', 'Heat maps provide insights into customer movement within a store by plotting X and Y coordinates, and indicating intensity or time spent at different locations.']}, {'end': 5987.378, 'segs': [{'end': 5220.763, 'src': 'embed', 'start': 5189.202, 'weight': 4, 'content': [{'end': 5192.746, 'text': 'sometimes they stopped to look a little bit, but they just kind of continued moving on.', 'start': 5189.202, 'duration': 3.544}, {'end': 5198.21, 'text': "and so the three variables that we've shown here is we've shown their x position in the store,", 'start': 5193.627, 'duration': 4.583}, {'end': 5204.814, 'text': "we've shown their y position in the store and to the color, we've also shown the time that they spend at each position.", 'start': 5198.21, 'duration': 6.604}, {'end': 5208.576, 'text': "so that's what we can use heat maps for.", 'start': 5204.814, 'duration': 3.762}, {'end': 5214.68, 'text': 'and then another example of a heat map would, for example, be if you take a flashlight and you move it over the screen,', 'start': 5208.576, 'duration': 6.104}, {'end': 5220.763, 'text': "and really what you're showing is the amount of time that you've shown the flashlight onto a specific region.", 'start': 5214.68, 'duration': 6.083}], 'summary': 'Analyzing customer movement and engagement using heat maps.', 'duration': 31.561, 'max_score': 5189.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5189202.jpg'}, {'end': 5311.51, 'src': 'embed', 'start': 5286.794, 'weight': 2, 'content': [{'end': 5292.537, 'text': 'Or maybe there are really good teams that score a lot, and they also shoot a lot on target.', 'start': 5286.794, 'duration': 5.743}, {'end': 5296.98, 'text': 'And so all of these things were able to then compare over different groups.', 'start': 5292.858, 'duration': 4.122}, {'end': 5300.102, 'text': "And so that's what we can use multivariable bar plots for.", 'start': 5297.12, 'duration': 2.982}, {'end': 5308.428, 'text': 'If there are several variables, that would give us a better understanding of the system than just looking at the variables and one at a time.', 'start': 5300.122, 'duration': 8.306}, {'end': 5311.51, 'text': "But it'd also be really cool if we could compare all of them.", 'start': 5308.848, 'duration': 2.662}], 'summary': 'Multivariable bar plots compare multiple variables for better system understanding.', 'duration': 24.716, 'max_score': 5286.794, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5286794.jpg'}, {'end': 5415.942, 'src': 'embed', 'start': 5387.244, 'weight': 3, 'content': [{'end': 5390.146, 'text': 'so that we can also add in our depth perception.', 'start': 5387.244, 'duration': 2.902}, {'end': 5396.19, 'text': "Because right now, if we're looking at it, it may look three-dimensional, but really it's just a two-dimensional snapshot.", 'start': 5390.686, 'duration': 5.504}, {'end': 5405.295, 'text': 'And to get the best understanding if our scatter plot is located more towards us and more towards the left, or something like that,', 'start': 5396.27, 'duration': 9.025}, {'end': 5410.078, 'text': "or maybe it's just really high and close to us, or maybe it's really low and far away.", 'start': 5405.295, 'duration': 4.783}, {'end': 5415.942, 'text': 'to understand all of these things, we need to be able to rotate our scatter plot so that we can see it from different angles,', 'start': 5410.078, 'duration': 5.864}], 'summary': 'Enhance scatter plot visualization for improved depth perception and understanding of data points.', 'duration': 28.698, 'max_score': 5387.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5387244.jpg'}, {'end': 5586.084, 'src': 'embed', 'start': 5556.716, 'weight': 1, 'content': [{'end': 5558.957, 'text': "So it's very easy for us to automate things.", 'start': 5556.716, 'duration': 2.241}, {'end': 5565.538, 'text': "And also for doing reports, it's very easy to automatically create these reports.", 'start': 5560.157, 'duration': 5.381}, {'end': 5573.301, 'text': "All you have to do is set up your program to deal with the data that you're going to give it, and then it can automatically create reports every week.", 'start': 5565.598, 'duration': 7.703}, {'end': 5574.601, 'text': 'And the reports can be different.', 'start': 5573.381, 'duration': 1.22}, {'end': 5580.983, 'text': 'because you give it different data and it should still look the same, but the data, the values, can be different,', 'start': 5574.961, 'duration': 6.022}, {'end': 5586.084, 'text': "and so that will just automatically create all these reports for you and you don't have to do it all yourself.", 'start': 5580.983, 'duration': 5.101}], 'summary': 'Automate report creation with program to handle data, generating different reports weekly.', 'duration': 29.368, 'max_score': 5556.716, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5556716.jpg'}, {'end': 5881.057, 'src': 'embed', 'start': 5854.324, 'weight': 0, 'content': [{'end': 5862.828, 'text': "And so it's really easy to do data analysis with it, because all of the functions are there and we know exactly what we want to do,", 'start': 5854.324, 'duration': 8.504}, {'end': 5864.608, 'text': "but we don't have to write the code for all of it.", 'start': 5862.828, 'duration': 1.78}, {'end': 5874.693, 'text': 'So, if you wanted to look at correlations, we just say hey, pandas, do correlations, rather than having to code all the correlations for ourselves,', 'start': 5864.648, 'duration': 10.045}, {'end': 5875.673, 'text': 'coding that whole algorithm.', 'start': 5874.693, 'duration': 0.98}, {'end': 5881.057, 'text': "And that makes it really easy and really fast to get results and to get to where you're heading,", 'start': 5876.093, 'duration': 4.964}], 'summary': 'Pandas simplifies data analysis by providing built-in functions, making it fast and efficient.', 'duration': 26.733, 'max_score': 5854.324, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5854324.jpg'}], 'start': 5189.202, 'title': 'Data analysis techniques and programming importance', 'summary': 'Covers visualizing data analysis techniques including heat maps and multivariable bar plots, and discusses the importance of programming in data science, emphasizing automation and the use of pandas and matplotlib.', 'chapters': [{'end': 5471.372, 'start': 5189.202, 'title': 'Visualizing data analysis techniques', 'summary': "Covers techniques like heat maps for tracking customer movement in stores, multivariable bar plots for comparing different groups' performance, and adding extra dimensions to lower dimensional graphs like scatter plots and line graphs to enhance depth perception.", 'duration': 282.17, 'highlights': ['Heat maps are used for tracking customer movement in stores, showing the amount of time spent at specific positions, and are often used to track general people location as well.', "Multivariable bar plots are used to compare different groups' performance by plotting several variables together, providing a better understanding of the system than single variable analysis.", 'Adding extra dimensions to lower dimensional graphs like scatter plots and line graphs can enhance depth perception and provide a more comprehensive view of the data.']}, {'end': 5987.378, 'start': 5472.833, 'title': 'Importance of programming in data science', 'summary': 'Discusses the importance of programming in data science, highlighting the ease of automation, customization, and the use of external libraries such as pandas and matplotlib to streamline data analysis and visualization.', 'duration': 514.545, 'highlights': ['The ability to program allows for ease of automation, enabling quick prototyping and automatic report generation, reducing the need for manual repetitive tasks.', 'Programming facilitates customization in data analysis, empowering data scientists to explore different directions and easily make changes in code for deeper analysis and visualization.', 'The Pandas library provides efficient data management and analysis, allowing for quick data manipulation, statistical calculations, and streamlined data analysis without the need to write extensive code.']}], 'duration': 798.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/N6BghzuFLIg/pics/N6BghzuFLIg5189202.jpg', 'highlights': ['The Pandas library provides efficient data management and analysis, allowing for quick data manipulation, statistical calculations, and streamlined data analysis without the need to write extensive code.', 'The ability to program allows for ease of automation, enabling quick prototyping and automatic report generation, reducing the need for manual repetitive tasks.', "Multivariable bar plots are used to compare different groups' performance by plotting several variables together, providing a better understanding of the system than single variable analysis.", 'Adding extra dimensions to lower dimensional graphs like scatter plots and line graphs can enhance depth perception and provide a more comprehensive view of the data.', 'Heat maps are used for tracking customer movement in stores, showing the amount of time spent at specific positions, and are often used to track general people location as well.']}], 'highlights': ['The Pandas library provides efficient data management and analysis, allowing for quick data manipulation, statistical calculations, and streamlined data analysis without the need to write extensive code. (relevance: 9)', 'The ability to program allows for ease of automation, enabling quick prototyping and automatic report generation, reducing the need for manual repetitive tasks. (relevance: 8)', 'Line plots illustrate the negative correlation between stress and creativity, showing a gradual decrease in creativity as stress levels increase, providing a clear visualization of the evolution of stress and creativity. (relevance: 7)', 'Two-dimensional histograms help visualize ticket sales trends based on price and quantity, revealing popular ticket prices and bands. (relevance: 6)', 'Histograms demonstrate data distribution and concentration, aiding in understanding frequency and occurrence. (relevance: 5)', 'The role of a data scientist involves transforming raw data into valuable information, emphasizing the importance of analyzing, contextualizing, and applying the data. (relevance: 4)', 'Continuous data encompasses a range of values without limitations based on decimal place. (relevance: 3)', 'The median evenly splits the data and is not affected by outliers. (relevance: 2)', 'The mini-course provides a basic look into data science, its three main components, and aims to provide a better understanding of the topic. (relevance: 1)']}