1 00:00:05,400 --> 00:00:07,080 Welcome, everyone, to the course. 2 00:00:07,080 --> 00:00:12,180 In this very first lecture, we're going to have a brief overview of the topics we cover in this course. 3 00:00:12,840 --> 00:00:16,010 First off, we want to thank you so much for enrolling. 4 00:00:16,020 --> 00:00:20,160 And before you begin with this lecture, you should remember to check out the very first lecture in 5 00:00:20,160 --> 00:00:22,180 the course, which is right before this one. 6 00:00:22,200 --> 00:00:26,940 It's an article lecture, so it basically just has a bunch of links with important information like 7 00:00:26,940 --> 00:00:30,810 how to ask a question in the course as well as the link for things like the course slides. 8 00:00:30,810 --> 00:00:35,400 So definitely make sure to check out the first lecture before continuing on with the course. 9 00:00:35,960 --> 00:00:39,570 Okay, so what is the course curriculum and what do we cover in this course? 10 00:00:39,780 --> 00:00:45,510 This course focuses on the mathematics behind the key concepts for data science and analysis, allowing 11 00:00:45,510 --> 00:00:50,610 you to understand the intuition behind many methods and procedures that you'll see being conducted in 12 00:00:50,610 --> 00:00:52,170 the field of data science. 13 00:00:52,500 --> 00:00:59,400 Now, to do this, we start off with this curriculum, core data concepts, visualizing data, combinatorics, 14 00:00:59,400 --> 00:01:02,880 probability, joint distributions and data distributions. 15 00:01:03,030 --> 00:01:08,700 Then we move on to discussing the normal distribution sampling hypothesis, testing and regression. 16 00:01:08,700 --> 00:01:12,690 So we cover all of that in the curriculum and it's a lot of material. 17 00:01:12,720 --> 00:01:15,540 Let me give you a very brief overview of each section. 18 00:01:16,850 --> 00:01:19,880 So we start off the course with core data concepts here. 19 00:01:19,880 --> 00:01:24,860 We really just talk about very basic ideas about data, and we also make sure we start off with a level 20 00:01:24,860 --> 00:01:25,660 playing field. 21 00:01:25,670 --> 00:01:30,440 We're going to talk about measurements of data such as mean median mode and then measurements of dispersion 22 00:01:30,440 --> 00:01:32,390 like variance in standard deviation. 23 00:01:33,590 --> 00:01:35,600 Then we'll move on to visualizing data. 24 00:01:35,600 --> 00:01:40,340 The reason we also cover this earlier in the course is because we use a lot of visualizations when describing 25 00:01:40,340 --> 00:01:41,900 things like data distribution. 26 00:01:41,900 --> 00:01:46,670 So when learning about data distributions like the normal distribution or binomial distribution, it's 27 00:01:46,670 --> 00:01:48,830 important to understand what you're visually looking at. 28 00:01:48,830 --> 00:01:54,800 So we give you a tour of a bunch of different ways to visualize data, because communicating your results 29 00:01:54,800 --> 00:01:56,930 is a really important part of data science. 30 00:01:58,040 --> 00:02:01,630 Then we start moving on to the more math heavy parts of the course. 31 00:02:01,640 --> 00:02:03,170 So we start off with combinatorics. 32 00:02:03,170 --> 00:02:07,760 This is the study of counting, and this section helps you understand concepts like counting combinations 33 00:02:07,760 --> 00:02:12,950 and permutations of objects like the different number of ways to sort a deck of cards. 34 00:02:14,410 --> 00:02:19,210 Then we move on to probability and we talk a lot about the basics of probability in the section, but 35 00:02:19,210 --> 00:02:22,600 we also include things like conditional probability, such as Bayes Theorem. 36 00:02:22,660 --> 00:02:24,400 It's a pretty big section of the course. 37 00:02:25,280 --> 00:02:28,220 Then we move on to talking about things like distributions. 38 00:02:28,250 --> 00:02:32,990 Our very first discussion is about joint distributions, talking about the relationships between data 39 00:02:32,990 --> 00:02:33,350 sets. 40 00:02:33,350 --> 00:02:38,330 So we talk about here about concepts like covariance and the correlation coefficient. 41 00:02:39,460 --> 00:02:41,510 Then we move on to data distributions. 42 00:02:41,530 --> 00:02:45,130 We're going to start off with just an understanding of what is a data distribution like, What's the 43 00:02:45,130 --> 00:02:49,480 difference between a probability mass function versus a probability density function and how those relate 44 00:02:49,480 --> 00:02:51,570 to the idea of random variables. 45 00:02:51,580 --> 00:02:56,470 After that, we take you through a tour of the most popular and common data distributions such as Bernoulli 46 00:02:56,470 --> 00:03:00,340 distribution, Poisson uniform and a bunch of distributions. 47 00:03:00,340 --> 00:03:04,900 I should point out that we actually have a special section for the normal distribution, so we have 48 00:03:04,900 --> 00:03:08,680 an entirely separate section on the normal distribution due to its unique properties. 49 00:03:08,680 --> 00:03:12,460 And also it's one of the most common distributions that occur in real world data sets. 50 00:03:12,460 --> 00:03:14,110 So it's really important to understand this. 51 00:03:14,110 --> 00:03:19,210 And keep in mind this section in particular really focuses heavily on applying the statistics to real 52 00:03:19,210 --> 00:03:20,170 world applications. 53 00:03:20,170 --> 00:03:24,670 But I should mention pretty much all the sections, especially the questions at the end, focus on applying 54 00:03:24,670 --> 00:03:29,140 what you've learned to something in the real world that you would encounter as a practicing data scientist. 55 00:03:30,370 --> 00:03:34,000 Then we'll talk about sampling, where we focus on ideas like the Central Limit theorem, which are 56 00:03:34,000 --> 00:03:38,440 really critical for understanding how to apply statistical concepts to real world data sets. 57 00:03:38,980 --> 00:03:43,750 After this, we discuss hypothesis testing, which is super important for concepts like understanding 58 00:03:43,750 --> 00:03:48,950 terms like P values or significant levels or ideas like AB testing on a website. 59 00:03:48,970 --> 00:03:54,070 This all has to do with hypothesis testing and then we conclude the course with regression. 60 00:03:54,070 --> 00:03:58,630 So we touch on one of the most common statistical modeling techniques at the very end of the course, 61 00:03:58,630 --> 00:04:03,430 allowing you to use your skills to actually perform something like possible forecasting or modeling 62 00:04:03,430 --> 00:04:05,950 of outcomes based on inputs. 63 00:04:07,050 --> 00:04:07,530 All right. 64 00:04:07,530 --> 00:04:08,370 Let's get started. 65 00:04:08,400 --> 00:04:09,660 We'll see you at the next lecture.