1 00:00:00,181 --> 00:00:05,933 [MUSIC]. 2 00:00:05,933 --> 00:00:09,208 Okay, in this segment I want to talk about logistics of the course. 3 00:00:09,208 --> 00:00:13,498 So how we've organized this course is a guided tour of important trends, along 4 00:00:13,498 --> 00:00:18,074 with a deep dive into specific topics. And then there's a set of hands-on 5 00:00:18,074 --> 00:00:21,380 assignments that are intended to deliver specific skills and experiences. 6 00:00:21,380 --> 00:00:23,151 And that's perhaps the most important part. 7 00:00:23,151 --> 00:00:24,404 Okay. And so overall the course is not, you 8 00:00:24,404 --> 00:00:26,798 know, the challenge here was to design a course that would be broad enough to 9 00:00:26,798 --> 00:00:30,194 cover the topics that we want. And also inclusive enough that we didn't 10 00:00:30,194 --> 00:00:32,570 sort of have to dial it in for a very specific cohort. 11 00:00:32,570 --> 00:00:38,600 But the challenge then is that it's going to be very difficult for some people and 12 00:00:38,600 --> 00:00:45,300 others may find it, some aspects of it certainly routine. 13 00:00:45,300 --> 00:00:47,780 I'd be surprised if anybody finds the whole thing routine. 14 00:00:47,780 --> 00:00:50,290 If so, then I'd be surprised they took this course. 15 00:00:50,290 --> 00:00:53,439 [LAUGH] Okay, so the prerequisites here are pretty light, since we are trying to 16 00:00:53,439 --> 00:00:57,720 cast such a wide net. So some prior programming experience in 17 00:00:57,720 --> 00:01:01,560 some language is going to be, really critical. 18 00:01:01,560 --> 00:01:04,687 then, you know, we're going to use terminology from the basic college 19 00:01:04,687 --> 00:01:08,005 statistics or the advanced high school statistics. 20 00:01:08,005 --> 00:01:11,840 So when I talk about linear aggression, you should know what that means. 21 00:01:11,840 --> 00:01:15,272 You should also be able to sort of, look at some visualization of data and be able 22 00:01:15,272 --> 00:01:19,170 to understand what it's telling you. Okay. 23 00:01:19,170 --> 00:01:21,407 And then perhaps the toughest one has statistics. 24 00:01:21,407 --> 00:01:25,065 Perhaps the number one is to have some exposure to databases and databases 25 00:01:25,065 --> 00:01:28,429 concepts. And you know, if you're just starting out 26 00:01:28,429 --> 00:01:31,851 in college, that's not always an easy proficiency to have gained or an 27 00:01:31,851 --> 00:01:36,666 experience to have gained. But you know, it's not, the, the, we're 28 00:01:36,666 --> 00:01:40,642 going to couch a lot of the discussion in terms of databases. 29 00:01:40,642 --> 00:01:44,668 And in the relationships to databases, and so some idea of what that means, what 30 00:01:44,668 --> 00:01:49,570 they are, is going to be helpful. Okay. 31 00:01:49,570 --> 00:01:52,498 So, to that end, one assignment will involving writing SQL, and if you've 32 00:01:52,498 --> 00:01:56,478 never written SQL before but you understand databases a little bit. 33 00:01:56,478 --> 00:01:59,320 You will probably be able to power through the assignment. 34 00:01:59,320 --> 00:02:01,562 If you're an expert in SQL, there are some parts of it that might still be 35 00:02:01,562 --> 00:02:04,834 interesting to you. And two assignments will be required, or 36 00:02:04,834 --> 00:02:08,550 will involve writing Python. One optional, sorry, one optional 37 00:02:08,550 --> 00:02:12,030 assignment will involve sort of processing big data using Amazon Web 38 00:02:12,030 --> 00:02:15,444 Services. And here, you know, one of the reasons 39 00:02:15,444 --> 00:02:19,524 it's optional is that because of the varying skill sets, but another reason is 40 00:02:19,524 --> 00:02:24,991 that you'll have to pay out of pocket for the cloud resources. 41 00:02:24,991 --> 00:02:28,891 And the reason for that is there's, you know, 60,000 students who signed up for 42 00:02:28,891 --> 00:02:33,000 the course and we can't sort of pay for all of them. 43 00:02:33,000 --> 00:02:36,124 The good news is it will cost sort of less than $10 or so. 44 00:02:36,124 --> 00:02:38,490 Okay. And is optional, so if you don't feel 45 00:02:38,490 --> 00:02:41,846 comfortable with that, you don't have to do it. 46 00:02:41,846 --> 00:02:44,738 Alright. Then another assignment will involve, all 47 00:02:44,738 --> 00:02:48,770 right, in competing in a kaggle .com project, of a kaggle, participating in a 48 00:02:48,770 --> 00:02:53,060 kaggle.com competition using whatever you want. 49 00:02:53,060 --> 00:02:55,790 And so, this may or may not involve any programming. 50 00:02:55,790 --> 00:02:59,950 You a lot of valid assignments, you know, you can, you can certainly compete by 51 00:02:59,950 --> 00:03:03,919 using Excel and other kinds of Gooey tools. 52 00:03:03,919 --> 00:03:06,318 Okay. This last bullet probably isn't true so 53 00:03:06,318 --> 00:03:10,740 lets just ignore that actualyl. So learning objective here is i really 54 00:03:10,740 --> 00:03:14,708 want people to come out of this course being able to talk intelligently about 55 00:03:14,708 --> 00:03:20,346 the landscape of data science concept tools algorithms technologies. 56 00:03:20,346 --> 00:03:25,170 And this will be sort of a spring board to dive deeper into particular areas. 57 00:03:25,170 --> 00:03:28,280 So, for example machine learning. This is not a machine learning course, 58 00:03:28,280 --> 00:03:33,200 but you can dive deeper into machine learning by taking this course. 59 00:03:33,200 --> 00:03:36,224 This is not a database course, but you can dive deeper by taking this course, 60 00:03:36,224 --> 00:03:38,570 and so on. Okay? 61 00:03:38,570 --> 00:03:41,700 And I also wanted to deliver some hands on experience manipulating data. 62 00:03:41,700 --> 00:03:45,220 I don't know levels of people that don't have any programming experience and 63 00:03:45,220 --> 00:03:48,410 provides some specific experiences for those of you that do have some 64 00:03:48,410 --> 00:03:52,805 programming experiences. For example, the first Python assignment 65 00:03:52,805 --> 00:03:56,420 will involve competing some Cinnamon analyses using some twitter data. 66 00:03:56,420 --> 00:03:59,441 So, if you already know Python, the learning Python won't be much of a 67 00:03:59,441 --> 00:04:03,760 contribution to that assignment. But Perhaps this is the first time you've 68 00:04:03,760 --> 00:04:07,078 been able to work with the live twitter stream, okay? 69 00:04:07,078 --> 00:04:09,792 And so the end result of this is that we hope you'll be sort of an advanced 70 00:04:09,792 --> 00:04:12,750 beginner in a variety of data science topics. 71 00:04:12,750 --> 00:04:15,416 And as I've said, you know, the tough, the tough part here is sort of how to do 72 00:04:15,416 --> 00:04:17,953 something more than just superfiicial access given that data science 73 00:04:17,953 --> 00:04:21,595 encompasses such a broad area of, as we've discussed. 74 00:04:21,595 --> 00:04:25,225 As we think we put together pretty good program but you'll, you'll have to be 75 00:04:25,225 --> 00:04:27,560 there, to judge that. Okay. 76 00:04:27,560 --> 00:04:31,760 Alright, so, the risk of belaboring this, of the course the velocity here is been 77 00:04:31,760 --> 00:04:35,180 that the skills needed by the data scientist span a variety of areas, 78 00:04:35,180 --> 00:04:41,920 statistics programming, databases distributed systems, visualization. 79 00:04:41,920 --> 00:04:46,672 But the traditional organization of these topics is sort of vertical and is not 80 00:04:46,672 --> 00:04:52,230 ideal for becoming sort of introductory in Data Science, right. 81 00:04:52,230 --> 00:04:55,350 So in order to get introductory level knowledge in all these areas what you end 82 00:04:55,350 --> 00:04:58,278 up having to do is take an introductory course in seven different areas or 83 00:04:58,278 --> 00:05:01,255 something. So a lot of different courses. 84 00:05:01,255 --> 00:05:06,736 Okay, and so our goal is to try to expose and simplify the links between these 85 00:05:06,736 --> 00:05:10,015 different areas. Okay. 86 00:05:10,015 --> 00:05:15,235 As opposed to sort of narrowing our attention on what makes them unique, 87 00:05:15,235 --> 00:05:18,375 okay? Right. 88 00:05:18,375 --> 00:05:24,265 Alright, so you know, after taking this course you will not be an expert in, 89 00:05:24,265 --> 00:05:28,066 statistics. You will not be an expert of machine 90 00:05:28,066 --> 00:05:32,110 learning certainly, you will not emerge an expert in databases and or even NoSQL. 91 00:05:32,110 --> 00:05:35,820 nor will you sort of have programing preferences in all of these language. 92 00:05:35,820 --> 00:05:38,500 However, you will use all these tools, you will understand the basic concepts of 93 00:05:38,500 --> 00:05:41,220 all these tools and you will have applied. 94 00:05:41,220 --> 00:05:48,882 Not many of these tools, okay. The assignments well there, there is a 95 00:05:48,882 --> 00:05:52,350 will have on-line short quizzes during the lectures of which you've already seen 96 00:05:52,350 --> 00:05:55,767 some these finger exercises quizzes they will be a set of the full length offline 97 00:05:55,767 --> 00:06:00,728 assignments as I mentioned. And some of these assignments will be 98 00:06:00,728 --> 00:06:05,200 graded by some of the programming assignments will be graded automatically. 99 00:06:05,200 --> 00:06:10,075 some of the assignments that don't lend themselves to autograding will be 100 00:06:10,075 --> 00:06:16,480 assessed using the peer assessment tools. So an example of that is you're going to 101 00:06:16,480 --> 00:06:19,665 write up a description of your Kaggle solution in addition to submitting your 102 00:06:19,665 --> 00:06:23,930 score for the Kaggle competition. And other students are going to sort of 103 00:06:23,930 --> 00:06:26,826 grade whether, whether it's comprehensive or not. 104 00:06:26,826 --> 00:06:30,162 Okay. So, here's my background in one slide. 105 00:06:30,162 --> 00:06:33,572 So, I have a Bachelor's degree in Industrial and Systems Engineering from 106 00:06:33,572 --> 00:06:36,470 Georgia Tech. But, you know, all of the problems in 107 00:06:36,470 --> 00:06:40,020 Industrial Engineering tended to be about optimization and automation, which seem 108 00:06:40,020 --> 00:06:43,781 to require software. So, I sort of got more interested in 109 00:06:43,781 --> 00:06:48,755 Computer science so I, spent a couple years consulting with some big firms. 110 00:06:48,755 --> 00:06:52,843 Somerget does oil feed services, oil, oil field services, and Siebel does customer 111 00:06:52,843 --> 00:06:57,940 relationship management software. And you, probably have heard of Microsoft 112 00:06:57,940 --> 00:07:01,710 and Verizon and Deloitte as a managing consulting firm. 113 00:07:01,710 --> 00:07:04,770 And then I sent back to grad school, and got a PhD in Computer Science from 114 00:07:04,770 --> 00:07:08,034 working with oceanographers on query systems for large scale oceangraphic 115 00:07:08,034 --> 00:07:11,408 models. And then I spent a couple years working 116 00:07:11,408 --> 00:07:15,580 directly with oceanographers as kind of a data architect. 117 00:07:15,580 --> 00:07:18,163 And before coming to the University of Washington where now I lead a group in 118 00:07:18,163 --> 00:07:20,500 Scalable Data Analytics for the University of Washington eScience 119 00:07:20,500 --> 00:07:23,198 Institute. And also I'm an affiliate assistant 120 00:07:23,198 --> 00:07:25,648 professor position in computer science engineering. 121 00:07:25,648 --> 00:07:30,814 And so, there's a bit of a mix of very practical, kind of applied work, as well 122 00:07:30,814 --> 00:07:35,480 as my research agenda. And so I think that this data science 123 00:07:35,480 --> 00:07:38,880 trend that's occurring is sort of, strikes close to home with me. 124 00:07:38,880 --> 00:07:41,100 I think it's a, I think it's a great time for it.