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These are the user uploaded subtitles that are being translated: 1 00:00:00,840 --> 00:00:04,580 Hello welcome to the course on Dollar Signs. 2 00:00:04,590 --> 00:00:06,720 I'm super excited to have you here. 3 00:00:06,810 --> 00:00:09,080 And we're going to have a fantastic journey together. 4 00:00:09,090 --> 00:00:14,880 We're going to dive into four separate rounds of dollar signs and each one will feel like the convention 5 00:00:14,890 --> 00:00:15,200 . 6 00:00:15,420 --> 00:00:19,740 And in this quick tutorial I just wanted to introduce myself quickly and give you an outline of the 7 00:00:19,740 --> 00:00:20,370 course. 8 00:00:20,550 --> 00:00:25,260 So my name is Tor Maiko and I have been in this field for about five years. 9 00:00:25,320 --> 00:00:32,250 I have worked for companies as a scientist and for consulting as a scientist I worked at a stranger 10 00:00:32,640 --> 00:00:38,370 and I had exposure to many different industries and developed lots of skills and experiences which I 11 00:00:38,370 --> 00:00:43,010 wanted to share with you in this course and moving onto the course outline. 12 00:00:43,010 --> 00:00:47,370 I'm just going to jump behind my computer and I'll walk you through the different sections of the course 13 00:00:47,560 --> 00:00:50,130 so that you know how to navigate there. 14 00:00:50,400 --> 00:00:54,740 All right back behind the computer and here I've got the course as we see it. 15 00:00:54,840 --> 00:00:58,300 So I'm going to zoom in a little bit so we can see everything a bit better. 16 00:00:58,530 --> 00:01:02,150 And at the top here we've got the get excited section that's where we are right now. 17 00:01:02,220 --> 00:01:08,400 Then we have that what is data science section which is a bit of an extended introduction into data 18 00:01:08,400 --> 00:01:13,310 science and it tells you about the different areas or domains of data science. 19 00:01:13,350 --> 00:01:19,110 Why data science is going to just grow in demand and is going to be a very popular profession in the 20 00:01:19,110 --> 00:01:22,800 future and why you want to be on this wave early. 21 00:01:22,950 --> 00:01:26,670 And also there's an important story here which is called course pathways. 22 00:01:26,670 --> 00:01:31,730 Now this tutorial is important because it will allow you to create your own learning experience based 23 00:01:31,750 --> 00:01:34,850 of the sections in this course. 24 00:01:34,900 --> 00:01:37,270 And let me explain in a bit more detail what I mean here. 25 00:01:37,410 --> 00:01:44,040 So here we've got Section 3 part one installation there is four parts to the course and they are quite 26 00:01:44,040 --> 00:01:48,390 substantial So they have lots and lots of lectures and you might want to go through them in the order 27 00:01:48,390 --> 00:01:53,550 that they laid out or you might want to combine them into your own learning experience based on what 28 00:01:53,610 --> 00:01:57,430 you are after in terms of learning data science. 29 00:01:57,570 --> 00:01:59,890 So here we've got Tablo eras. 30 00:01:59,940 --> 00:02:05,550 We're using Tablo because it allows us to do visual data mining a very interesting topic which we cover 31 00:02:05,550 --> 00:02:06,370 off. 32 00:02:06,750 --> 00:02:12,840 Then we get an introduction here this introduction to Tablo then I explain how to use Tablo for data 33 00:02:12,840 --> 00:02:13,770 mining. 34 00:02:13,860 --> 00:02:18,840 Then I will show you how to do advanced data mining in Tablo we actually talk about statistical significance 35 00:02:18,840 --> 00:02:22,710 of your data mining based on chi square tests. 36 00:02:22,710 --> 00:02:27,330 Then Part two is modeling so we won't go through all the sections right now but because there's just 37 00:02:27,330 --> 00:02:32,910 a lot of them but basically we refresh our stats in this section and then we learn how to build model 38 00:02:32,910 --> 00:02:33,570 step by step. 39 00:02:33,570 --> 00:02:39,070 So we go through simple linear regression multiple linear regression logistic regression building a 40 00:02:39,070 --> 00:02:45,150 robust demographic segmentation model then we'll learn how to assess the model then we learn how to 41 00:02:45,150 --> 00:02:47,440 draw insights from your model. 42 00:02:47,520 --> 00:02:49,830 We will learn about model maintenance. 43 00:02:49,950 --> 00:02:56,240 We will learn modeling tips and tricks and then we will move on to data preparation. 44 00:02:56,240 --> 00:03:03,330 So this is part three and here this is also a very big part here we're going to learn what business 45 00:03:03,330 --> 00:03:08,790 intelligence tools are and we'll set up all the software for this part of the course then we will talk 46 00:03:08,790 --> 00:03:11,190 about data wrangling before the load. 47 00:03:11,250 --> 00:03:16,750 We'll talk about how to use Sosias for uploading your daughter. 48 00:03:16,860 --> 00:03:20,020 Then we will talk about how to handle errors. 49 00:03:20,460 --> 00:03:24,050 And this is going to add some real Listen to what we're doing. 50 00:03:24,050 --> 00:03:28,910 So there'll be very realistic errors that you will find in real life scenarios. 51 00:03:29,090 --> 00:03:35,580 And here we will learn how to program in school for our science so some basics of skill programming 52 00:03:35,580 --> 00:03:37,690 which we'll need in further sections. 53 00:03:37,690 --> 00:03:43,450 Then we will learn how to perform data wrangling after we have loaded data into a database. 54 00:03:43,770 --> 00:03:49,590 Then we will learn how to handle errors during the last phase of our extractions from load process and 55 00:03:49,590 --> 00:03:53,390 there will be also another very realistic daughter said here. 56 00:03:53,430 --> 00:03:56,070 So you will learn everything about cleaning data. 57 00:03:56,280 --> 00:03:57,920 And finally part for communication. 58 00:03:57,930 --> 00:04:04,050 So this is the part where we talk about how to work with people in the section we talk about how to 59 00:04:04,050 --> 00:04:08,470 work with people and extract information from them to using your data science projects. 60 00:04:08,520 --> 00:04:12,600 And in this section we learn how to present data science projects. 61 00:04:12,750 --> 00:04:16,590 And I actually give you some case studies of my own presentations. 62 00:04:16,740 --> 00:04:20,610 And finally at the end we've got some homework solutions because there will be homework throughout the 63 00:04:20,610 --> 00:04:21,600 course. 64 00:04:21,600 --> 00:04:24,280 So that's how this course works. 65 00:04:24,330 --> 00:04:29,940 Make sure that you check out that tutorial that I mentioned on creating your own pathway because it 66 00:04:29,940 --> 00:04:36,090 will also help you understand how you can structure these sections to gain the maximum benefit in your 67 00:04:36,090 --> 00:04:37,060 specific situation. 68 00:04:37,200 --> 00:04:40,790 And I look forward to seeing inside the course and until next I'm happy analyzing 7605