https:/.../52f6698f-a7dd-4943-86eb-ad7501790760-fc4269eb-1399-4a33-9140-ad8c0105606b.mp4?invocationId=08872c6d-6303-ec11-a9e9-0a1a827ad0ec

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Hello and welcome to see us five thirty three, introduction to data science.

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This is the introduction video that's going to give you an overview of what it is going to we're going

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to be talking about this semester and provide you with guidance for how to get started with the course.

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Are learning outcomes for this video are to introduce the class subject.

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What is data science for our purposes?

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For you to understand the learning outcomes of the course, for you to understand how this class is structured at a high level,

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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.

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To get started, though, I want to talk about what data science is.

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And there are many different people are going to have many different definitions of data science.

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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

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of data to provide quantitative insights on questions of scientific business or social interest.

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There may be many different things to which we want to apply the science we may have maybe in a business context,

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but we want to gain understanding about the effectiveness of our business processes.

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We may want to evaluate a change to some aspect of how we carry out our business or how we manufacture, provide our products.

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We may want to understand the impacts of some of our business decisions.

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We may have a scientific question. What we are trying to produce generalizable knowledge to understand the world around us.

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We may have a social interest,

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particularly if we're deploying data science in the context of a nonprofit or of a government agency or of an educational institution.

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What we're trying to understand, social dynamics of human behavior. We're trying to understand maybe the impact of a policy on on.

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For example. We may have social interest where particularly if we are deploying.

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We may have social interest, particularly if we are deploying data science in a nonprofit or a government agency

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or an educational setting where we're trying to understand social dynamics,

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we're trying to understand the effectiveness and the impact of policies and programs

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and various other purposes to inform our social mission with quantitative insights.

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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,

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I find this quote from Sergios Mondo's Introduction to science and Technology Studies useful in a book.

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He says, By itself, some piece of data has no meaning.

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Data is only given meaning as evidence by the people who make use of it.

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And this means that we can't just go find a bunch of data.

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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.

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We need to do work to frame our questions in a way that we can actually.

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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,

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which is one of the reasons why we're going to be using our own class time for

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discussion and application exercises is because we need the data of the data,

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gains its meaning and becomes evidence for particular conclusions of particular answers.

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Because we go through the process of interpretation and we go through the process of presenting our

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interpretations for other to others and discussing and debating how we came to the conclusions,

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the conclusions. We came to the level of support they have from the data.

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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.

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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.

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Someone gives you some data and you first need to be able to get your bearings and understand what data you have,

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be able to assess whether or not it would answer particular questions and what questions you might be able to answer with it.

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You need to be able to define a question that we can answer.

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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.

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But taking a goal that we have and turning it into a question that we can actually answer

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through a data analysis is a process that takes work and we're going to be learning that.

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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,

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it's reproducible, so others can do the same analysis. And it also doesn't unnecessarily waste computing resources to come to the answers.

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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.

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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,

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charts and graphics and the written argument to be able to communicate to other people,

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your peers and the class myself in the future, your advisers, your supervisors at work,

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what it is that you learn from the data and why your conclusions are a reliable and defensible interpretation of the data.

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I want you to be able to identify weaknesses in data analysis.

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No data analysis is perfect.

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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,

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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.

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The result? Some weaknesses are fatal flaws, so we can no longer trust the results we get.

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Other weaknesses, however, are.

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Caveats that we need to acknowledge and account for when we apply the results, but they don't fundamentally undermine their validity.

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I also want you to be able to reason about the ethical implications of your work.

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There's a variety of different frameworks and perspectives that we can take on the

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ethics of data science work that we're going to be talking about this semester.

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But I want you to be able to think about and account for and assess the ethical implications of data science work.

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And then finally, I want you to understand how various the broader picture of data science,

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the various techniques and applications fit into a framework to give you a map of the of the space,

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and particularly with the role this class plays in Boise State's graduate curriculum.

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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,

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such as machine learning or large scale data analysis or recommender systems or social

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media mining together and develop a coherent picture of what it is to do data science.

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Support these outcomes, there are a number of components of the class. The first is the videos and readings.

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You're watching one of those videos right now. This is going to be the primary mechanism for content delivery.

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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

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clear up things that that are confusing or things you have questions about.

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But our primary content delivery is going to be through these prerecorded lecture videos.

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The website is your guide to all of this. Each week is going to have a page that lays out the videos,

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the readings that you need to do in order to be prepared for class, prepared for the assignments and to learn the material.

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Are in class, meetings are going to be focused on discussing and applying the materials, some of that will be open discussion,

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some of that will be guided application exercises particularly carried out with your classmates.

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We're going to be having some quizzes, particularly each Thursday.

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There's going to be a quiz before class that assesses your understanding of the material and give both

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you and I an initial check on how well your understanding it and whether you're ready to apply it.

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In our work and in the assignment,

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we're going to have assignments that come at a relatively steady pace throughout the semester, one every other week.

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They give you more extended place to practice and develop your skills and demonstrate

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your mastery of the material that we're going to be covering this semester. And then finally,

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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.

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As we go through the semester,

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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.

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You can get it in our class meetings, both from your peers in the class and from me.

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You can get it at any time through the class forum on Piazza.

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I strongly encourage you to ask questions and answer each other's questions on Piazza when you ask a question,

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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.

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Oftentimes, helping other people answer questions strengthens our own understanding of the material.

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So I encourage you to make use of Piazza both to answer your get your questions answered

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and to develop and practice your understanding of the material through helping each other.

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I am going to be having office hours. I'm going to be having them physically in my office so long as health permits.

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And I will also be able to have remote office hours by appointment.

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I do ask that if you have any inquiries about the class that you direct them through Piazza.

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If it's something that's just for me, perhaps about grades or something,

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you can send a private message to the instructors on Piazza that's going to let me keep all

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my course communications in one place so I don't accidentally lose something in my email.

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And finally, you can use the Internet as a resource where there's a lot of documentation, resources.

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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.

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I encourage you to make use of public Q&A sites when you're a practicing data scientist after this class.

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The Internet is at your disposal for getting your work done.

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There's no reason not to go ahead and practice, make use of the resources that are available to you in this class.

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I just ask that you do it in a way that's in keeping with the principles of academic integrity,

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you can go to a public Q&A site and you can ask, I'm trying to do this thing.

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I'm having a problem. And ask a specific question about the problem that you're getting stuck on.

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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?

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Demonstrate your learning of the material and how you go about identifying where it is that you're stuck and

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asking a question that's going to help uncover the missing piece that you need in order to move forward.

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So it's all of that. This class is designed to provide flexibility in learning and to use each modality to its best advantage.

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There are tradeoffs. I'm not going to pretend like there's no trade off the how I've designed this class.

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But with this design, we can do the content delivery me lecturing in a way where you can go back and watch videos later.

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You can catch up. If you have to miss some material, you can speed up the video.

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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.

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Where we're actually engaging with the material together and talking to each

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other about it and these things together with the various parts of the class,

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I hope are going to allow us to achieve our learning outcomes for the semester. I hope you have a great semester.

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I hope you learn a lot. I also expect to learn from you every time I teach.

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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.

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So let's learn together.