9:59:59.000,9:59:59.000
1[br]00:00:04,700 --> 00:00:08,900[br]Hello and welcome to see us five thirty three, introduction to data science.

9:59:59.000,9:59:59.000
2[br]00:00:08,900 --> 00:00:12,650[br]This is the introduction video that's going to give you an overview of what it is going to we're going

9:59:59.000,9:59:59.000
3[br]00:00:12,650 --> 00:00:17,960[br]to be talking about this semester and provide you with guidance for how to get started with the course.

9:59:59.000,9:59:59.000
4[br]00:00:17,960 --> 00:00:21,650[br]Are learning outcomes for this video are to introduce the class subject.

9:59:59.000,9:59:59.000
5[br]00:00:21,650 --> 00:00:24,290[br]What is data science for our purposes?

9:59:59.000,9:59:59.000
6[br]00:00:24,290 --> 00:00:30,650[br]For you to understand the learning outcomes of the course, for you to understand how this class is structured at a high level,

9:59:59.000,9:59:59.000
7[br]00:00:30,650 --> 00:00:39,890[br]we'll be supplementing this with discussion in class and then also to know where to get help throughout the semester as you're in the course.

9:59:59.000,9:59:59.000
8[br]00:00:39,890 --> 00:00:44,030[br]To get started, though, I want to talk about what data science is.

9:59:59.000,9:59:59.000
9[br]00:00:44,030 --> 00:00:49,460[br]And there are many different people are going to have many different definitions of data science.

9:59:59.000,9:59:59.000
10[br]00:00:49,460 --> 00:00:59,730[br]But one of the things that. But the definition I've been using as I've built out this class is that data science is the use

9:59:59.000,9:59:59.000
11[br]00:00:59,730 --> 00:01:06,150[br]of data to provide quantitative insights on questions of scientific business or social interest.

9:59:59.000,9:59:59.000
12[br]00:01:06,150 --> 00:01:13,420[br]There may be many different things to which we want to apply the science we may have maybe in a business context,

9:59:59.000,9:59:59.000
13[br]00:01:13,420 --> 00:01:18,570[br]but we want to gain understanding about the effectiveness of our business processes.

9:59:59.000,9:59:59.000
14[br]00:01:18,570 --> 00:01:26,040[br]We may want to evaluate a change to some aspect of how we carry out our business or how we manufacture, provide our products.

9:59:59.000,9:59:59.000
15[br]00:01:26,040 --> 00:01:30,990[br]We may want to understand the impacts of some of our business decisions.

9:59:59.000,9:59:59.000
16[br]00:01:30,990 --> 00:01:38,310[br]We may have a scientific question. What we are trying to produce generalizable knowledge to understand the world around us.

9:59:59.000,9:59:59.000
17[br]00:01:38,310 --> 00:01:40,170[br]We may have a social interest,

9:59:59.000,9:59:59.000
18[br]00:01:40,170 --> 00:01:47,670[br]particularly if we're deploying data science in the context of a nonprofit or of a government agency or of an educational institution.

9:59:59.000,9:59:59.000
19[br]00:01:47,670 --> 00:02:00,640[br]What we're trying to understand, social dynamics of human behavior. We're trying to understand maybe the impact of a policy on on.

9:59:59.000,9:59:59.000
20[br]00:02:00,640 --> 00:02:13,880[br]For example. We may have social interest where particularly if we are deploying.

9:59:59.000,9:59:59.000
21[br]00:02:13,880 --> 00:02:19,640[br]We may have social interest, particularly if we are deploying data science in a nonprofit or a government agency

9:59:59.000,9:59:59.000
22[br]00:02:19,640 --> 00:02:23,720[br]or an educational setting where we're trying to understand social dynamics,

9:59:59.000,9:59:59.000
23[br]00:02:23,720 --> 00:02:28,700[br]we're trying to understand the effectiveness and the impact of policies and programs

9:59:59.000,9:59:59.000
24[br]00:02:28,700 --> 00:02:35,110[br]and various other purposes to inform our social mission with quantitative insights.

9:59:59.000,9:59:59.000
25[br]00:02:35,110 --> 00:02:43,780[br]To give you some more about how I go about thinking about this and some more of the background of how I've been designing the class,

9:59:59.000,9:59:59.000
26[br]00:02:43,780 --> 00:02:51,310[br]I find this quote from Sergios Mondo's Introduction to science and Technology Studies useful in a book.

9:59:59.000,9:59:59.000
27[br]00:02:51,310 --> 00:02:55,990[br]He says, By itself, some piece of data has no meaning.

9:59:59.000,9:59:59.000
28[br]00:02:55,990 --> 00:03:01,330[br]Data is only given meaning as evidence by the people who make use of it.

9:59:59.000,9:59:59.000
29[br]00:03:01,330 --> 00:03:04,990[br]And this means that we can't just go find a bunch of data.

9:59:59.000,9:59:59.000
30[br]00:03:04,990 --> 00:03:15,610[br]And, oh, we have the answer. We have to do work to get our data into a form where it can actually provide answers to the questions we care about.

9:59:59.000,9:59:59.000
31[br]00:03:15,610 --> 00:03:23,610[br]We need to do work to frame our questions in a way that we can actually.

9:59:59.000,9:59:59.000
32[br]00:03:23,610 --> 00:03:31,770[br]Answer them with the data that we have available or that we can obtain, and this is a human process and it's also a social process,

9:59:59.000,9:59:59.000
33[br]00:03:31,770 --> 00:03:37,200[br]which is one of the reasons why we're going to be using our own class time for

9:59:59.000,9:59:59.000
34[br]00:03:37,200 --> 00:03:44,820[br]discussion and application exercises is because we need the data of the data,

9:59:59.000,9:59:59.000
35[br]00:03:44,820 --> 00:03:50,310[br]gains its meaning and becomes evidence for particular conclusions of particular answers.

9:59:59.000,9:59:59.000
36[br]00:03:50,310 --> 00:03:56,370[br]Because we go through the process of interpretation and we go through the process of presenting our

9:59:59.000,9:59:59.000
37[br]00:03:56,370 --> 00:04:02,250[br]interpretations for other to others and discussing and debating how we came to the conclusions,

9:59:59.000,9:59:59.000
38[br]00:04:02,250 --> 00:04:06,030[br]the conclusions. We came to the level of support they have from the data.

9:59:59.000,9:59:59.000
39[br]00:04:06,030 --> 00:04:15,900[br]So we're going to be practicing that a lot in this class of going from data to evidentiary meaning as a human process and pursuit of all of this.

9:59:59.000,9:59:59.000
40[br]00:04:15,900 --> 00:04:24,180[br]There are a number of learning outcomes for this course. The first one is that I want you to be able to explore a data set.

9:59:59.000,9:59:59.000
41[br]00:04:24,180 --> 00:04:29,550[br]Someone gives you some data and you first need to be able to get your bearings and understand what data you have,

9:59:59.000,9:59:59.000
42[br]00:04:29,550 --> 00:04:35,520[br]be able to assess whether or not it would answer particular questions and what questions you might be able to answer with it.

9:59:59.000,9:59:59.000
43[br]00:04:35,520 --> 00:04:39,960[br]You need to be able to define a question that we can answer.

9:59:59.000,9:59:59.000
44[br]00:04:39,960 --> 00:04:48,200[br]So this is a. This is not an easy process when you're talking about quite a bit more about it in the next two videos.

9:59:59.000,9:59:59.000
45[br]00:04:48,200 --> 00:04:53,720[br]But taking a goal that we have and turning it into a question that we can actually answer

9:59:59.000,9:59:59.000
46[br]00:04:53,720 --> 00:04:59,990[br]through a data analysis is a process that takes work and we're going to be learning that.

9:59:59.000,9:59:59.000
47[br]00:04:59,990 --> 00:05:09,350[br]I want you to be able to then actually carry out your analysis in a way that is documented so other people can understand what you did,

9:59:59.000,9:59:59.000
48[br]00:05:09,350 --> 00:05:18,710[br]it's reproducible, so others can do the same analysis. And it also doesn't unnecessarily waste computing resources to come to the answers.

9:59:59.000,9:59:59.000
49[br]00:05:18,710 --> 00:05:27,730[br]You make efficient use of the resources we have available to us. And also then we can scale to being doing analysis on very large data sets.

9:59:59.000,9:59:59.000
50[br]00:05:27,730 --> 00:05:35,320[br]It's not enough to just do an analysis, I want you to be able to present the results of your analysis both through visuals,

9:59:59.000,9:59:59.000
51[br]00:05:35,320 --> 00:05:42,130[br]charts and graphics and the written argument to be able to communicate to other people,

9:59:59.000,9:59:59.000
52[br]00:05:42,130 --> 00:05:48,910[br]your peers and the class myself in the future, your advisers, your supervisors at work,

9:59:59.000,9:59:59.000
53[br]00:05:48,910 --> 00:05:56,650[br]what it is that you learn from the data and why your conclusions are a reliable and defensible interpretation of the data.

9:59:59.000,9:59:59.000
54[br]00:05:56,650 --> 00:06:00,640[br]I want you to be able to identify weaknesses in data analysis.

9:59:59.000,9:59:59.000
55[br]00:06:00,640 --> 00:06:03,990[br]No data analysis is perfect.

9:59:59.000,9:59:59.000
56[br]00:06:03,990 --> 00:06:10,710[br]There are going to be weaknesses and downsides and we'll have to make tradeoffs when we make decisions of how we analyze the data,

9:59:59.000,9:59:59.000
57[br]00:06:10,710 --> 00:06:19,480[br]but also not all weaknesses are created equal. I want you to be able to assess whether the impact a weakness has on the correctness and utility.

9:59:59.000,9:59:59.000
58[br]00:06:19,480 --> 00:06:25,530[br]The result? Some weaknesses are fatal flaws, so we can no longer trust the results we get.

9:59:59.000,9:59:59.000
59[br]00:06:25,530 --> 00:06:29,640[br]Other weaknesses, however, are.

9:59:59.000,9:59:59.000
60[br]00:06:29,640 --> 00:06:37,650[br]Caveats that we need to acknowledge and account for when we apply the results, but they don't fundamentally undermine their validity.

9:59:59.000,9:59:59.000
61[br]00:06:37,650 --> 00:06:42,600[br]I also want you to be able to reason about the ethical implications of your work.

9:59:59.000,9:59:59.000
62[br]00:06:42,600 --> 00:06:46,560[br]There's a variety of different frameworks and perspectives that we can take on the

9:59:59.000,9:59:59.000
63[br]00:06:46,560 --> 00:06:49,920[br]ethics of data science work that we're going to be talking about this semester.

9:59:59.000,9:59:59.000
64[br]00:06:49,920 --> 00:06:57,450[br]But I want you to be able to think about and account for and assess the ethical implications of data science work.

9:59:59.000,9:59:59.000
65[br]00:06:57,450 --> 00:07:03,900[br]And then finally, I want you to understand how various the broader picture of data science,

9:59:59.000,9:59:59.000
66[br]00:07:03,900 --> 00:07:11,580[br]the various techniques and applications fit into a framework to give you a map of the of the space,

9:59:59.000,9:59:59.000
67[br]00:07:11,580 --> 00:07:16,560[br]and particularly with the role this class plays in Boise State's graduate curriculum.

9:59:59.000,9:59:59.000
68[br]00:07:16,560 --> 00:07:22,890[br]I want you I want to give you a framework that you can use to relate what you're going to learn in other classes,

9:59:59.000,9:59:59.000
69[br]00:07:22,890 --> 00:07:27,480[br]such as machine learning or large scale data analysis or recommender systems or social

9:59:59.000,9:59:59.000
70[br]00:07:27,480 --> 00:07:34,790[br]media mining together and develop a coherent picture of what it is to do data science.

9:59:59.000,9:59:59.000
71[br]00:07:34,790 --> 00:07:39,590[br]Support these outcomes, there are a number of components of the class. The first is the videos and readings.

9:59:59.000,9:59:59.000
72[br]00:07:39,590 --> 00:07:44,990[br]You're watching one of those videos right now. This is going to be the primary mechanism for content delivery.

9:59:59.000,9:59:59.000
73[br]00:07:44,990 --> 00:07:51,200[br]I'm not planning to lecture live in class. I'll be doing a little bit of lecture style things here and there as we need to

9:59:59.000,9:59:59.000
74[br]00:07:51,200 --> 00:07:55,610[br]clear up things that that are confusing or things you have questions about.

9:59:59.000,9:59:59.000
75[br]00:07:55,610 --> 00:08:00,800[br]But our primary content delivery is going to be through these prerecorded lecture videos.

9:59:59.000,9:59:59.000
76[br]00:08:00,800 --> 00:08:05,780[br]The website is your guide to all of this. Each week is going to have a page that lays out the videos,

9:59:59.000,9:59:59.000
77[br]00:08:05,780 --> 00:08:12,760[br]the readings that you need to do in order to be prepared for class, prepared for the assignments and to learn the material.

9:59:59.000,9:59:59.000
78[br]00:08:12,760 --> 00:08:19,120[br]Are in class, meetings are going to be focused on discussing and applying the materials, some of that will be open discussion,

9:59:59.000,9:59:59.000
79[br]00:08:19,120 --> 00:08:24,790[br]some of that will be guided application exercises particularly carried out with your classmates.

9:59:59.000,9:59:59.000
80[br]00:08:24,790 --> 00:08:27,860[br]We're going to be having some quizzes, particularly each Thursday.

9:59:59.000,9:59:59.000
81[br]00:08:27,860 --> 00:08:33,250[br]There's going to be a quiz before class that assesses your understanding of the material and give both

9:59:59.000,9:59:59.000
82[br]00:08:33,250 --> 00:08:38,730[br]you and I an initial check on how well your understanding it and whether you're ready to apply it.

9:59:59.000,9:59:59.000
83[br]00:08:38,730 --> 00:08:41,560[br]In our work and in the assignment,

9:59:59.000,9:59:59.000
84[br]00:08:41,560 --> 00:08:47,110[br]we're going to have assignments that come at a relatively steady pace throughout the semester, one every other week.

9:59:59.000,9:59:59.000
85[br]00:08:47,110 --> 00:08:51,550[br]They give you more extended place to practice and develop your skills and demonstrate

9:59:59.000,9:59:59.000
86[br]00:08:51,550 --> 00:08:55,990[br]your mastery of the material that we're going to be covering this semester. And then finally,

9:59:59.000,9:59:59.000
87[br]00:08:55,990 --> 00:09:06,680[br]we have two midterm exams that a final exam to give an additional check of your conceptual knowledge of what it is that we're doing in this class.

9:59:59.000,9:59:59.000
88[br]00:09:06,680 --> 00:09:08,450[br]As we go through the semester,

9:59:59.000,9:59:59.000
89[br]00:09:08,450 --> 00:09:13,950[br]I expect you're probably going to have questions and you're going to need some help and there's a variety of places you can get it.

9:59:59.000,9:59:59.000
90[br]00:09:13,950 --> 00:09:19,760[br]You can get it in our class meetings, both from your peers in the class and from me.

9:59:59.000,9:59:59.000
91[br]00:09:19,760 --> 00:09:23,630[br]You can get it at any time through the class forum on Piazza.

9:59:59.000,9:59:59.000
92[br]00:09:23,630 --> 00:09:29,330[br]I strongly encourage you to ask questions and answer each other's questions on Piazza when you ask a question,

9:59:59.000,9:59:59.000
93[br]00:09:29,330 --> 00:09:35,510[br]I'm probably not going to answer it immediately. I'm going to give some time in order to see if others have an answer first.

9:59:59.000,9:59:59.000
94[br]00:09:35,510 --> 00:09:41,150[br]Oftentimes, helping other people answer questions strengthens our own understanding of the material.

9:59:59.000,9:59:59.000
95[br]00:09:41,150 --> 00:09:46,790[br]So I encourage you to make use of Piazza both to answer your get your questions answered

9:59:59.000,9:59:59.000
96[br]00:09:46,790 --> 00:09:50,990[br]and to develop and practice your understanding of the material through helping each other.

9:59:59.000,9:59:59.000
97[br]00:09:50,990 --> 00:09:56,180[br]I am going to be having office hours. I'm going to be having them physically in my office so long as health permits.

9:59:59.000,9:59:59.000
98[br]00:09:56,180 --> 00:10:00,710[br]And I will also be able to have remote office hours by appointment.

9:59:59.000,9:59:59.000
99[br]00:10:00,710 --> 00:10:04,670[br]I do ask that if you have any inquiries about the class that you direct them through Piazza.

9:59:59.000,9:59:59.000
100[br]00:10:04,670 --> 00:10:09,080[br]If it's something that's just for me, perhaps about grades or something,

9:59:59.000,9:59:59.000
101[br]00:10:09,080 --> 00:10:13,020[br]you can send a private message to the instructors on Piazza that's going to let me keep all

9:59:59.000,9:59:59.000
102[br]00:10:13,020 --> 00:10:18,170[br]my course communications in one place so I don't accidentally lose something in my email.

9:59:59.000,9:59:59.000
103[br]00:10:18,170 --> 00:10:23,180[br]And finally, you can use the Internet as a resource where there's a lot of documentation, resources.

9:59:59.000,9:59:59.000
104[br]00:10:23,180 --> 00:10:31,860[br]Many of our readings are going to come from various places on the Internet. There's many more sites where you can search for solutions and help also.

9:59:59.000,9:59:59.000
105[br]00:10:31,860 --> 00:10:37,140[br]I encourage you to make use of public Q&A sites when you're a practicing data scientist after this class.

9:59:59.000,9:59:59.000
106[br]00:10:37,140 --> 00:10:39,960[br]The Internet is at your disposal for getting your work done.

9:59:59.000,9:59:59.000
107[br]00:10:39,960 --> 00:10:47,600[br]There's no reason not to go ahead and practice, make use of the resources that are available to you in this class.

9:59:59.000,9:59:59.000
108[br]00:10:47,600 --> 00:10:54,920[br]I just ask that you do it in a way that's in keeping with the principles of academic integrity,

9:59:59.000,9:59:59.000
109[br]00:10:54,920 --> 00:10:59,240[br]you can go to a public Q&A site and you can ask, I'm trying to do this thing.

9:59:59.000,9:59:59.000
110[br]00:10:59,240 --> 00:11:05,100[br]I'm having a problem. And ask a specific question about the problem that you're getting stuck on.

9:59:59.000,9:59:59.000
111[br]00:11:05,100 --> 00:11:12,660[br]I don't want you to just copy copying a piece of the assignment description pasted in a question answer site and ask, how do I do this?

9:59:59.000,9:59:59.000
112[br]00:11:12,660 --> 00:11:19,410[br]Demonstrate your learning of the material and how you go about identifying where it is that you're stuck and

9:59:59.000,9:59:59.000
113[br]00:11:19,410 --> 00:11:26,520[br]asking a question that's going to help uncover the missing piece that you need in order to move forward.

9:59:59.000,9:59:59.000
114[br]00:11:26,520 --> 00:11:34,030[br]So it's all of that. This class is designed to provide flexibility in learning and to use each modality to its best advantage.

9:59:59.000,9:59:59.000
115[br]00:11:34,030 --> 00:11:39,180[br]There are tradeoffs. I'm not going to pretend like there's no trade off the how I've designed this class.

9:59:59.000,9:59:59.000
116[br]00:11:39,180 --> 00:11:46,920[br]But with this design, we can do the content delivery me lecturing in a way where you can go back and watch videos later.

9:59:59.000,9:59:59.000
117[br]00:11:46,920 --> 00:11:51,330[br]You can catch up. If you have to miss some material, you can speed up the video.

9:59:59.000,9:59:59.000
118[br]00:11:51,330 --> 00:12:02,400[br]If I talk to slow. And we're going to focus our in class time on things that can only be done through Synchronoss discussion.

9:59:59.000,9:59:59.000
119[br]00:12:02,400 --> 00:12:06,270[br]Where we're actually engaging with the material together and talking to each

9:59:59.000,9:59:59.000
120[br]00:12:06,270 --> 00:12:10,560[br]other about it and these things together with the various parts of the class,

9:59:59.000,9:59:59.000
121[br]00:12:10,560 --> 00:12:16,020[br]I hope are going to allow us to achieve our learning outcomes for the semester. I hope you have a great semester.

9:59:59.000,9:59:59.000
122[br]00:12:16,020 --> 00:12:20,070[br]I hope you learn a lot. I also expect to learn from you every time I teach.

9:59:59.000,9:59:59.000
123[br]00:12:20,070 --> 00:12:28,050[br]It's a learning experience for me as well. And I learn more about the subject and about how to teach and communicate about it from my students.

9:59:59.000,9:59:59.000
124[br]00:12:28,050 --> 00:12:34,900[br]So let's learn together.

9:59:59.000,9:59:59.000