Hello and welcome to see us five thirty three, introduction to data science.
This is the introduction video that's going to give you an overview of what it is going to we're going
to be talking about this semester and provide you with guidance for how to get started with the course.
Are learning outcomes for this video are to introduce the class subject.
What is data science for our purposes?
For you to understand the learning outcomes of the course, for you to understand how this class is structured at a high level,
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.
To get started, though, I want to talk about what data science is.
And there are many different people are going to have many different definitions of data science.
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
of data to provide quantitative insights on questions of scientific business or social interest.
There may be many different things to which we want to apply the science we may have maybe in a business context,
but we want to gain understanding about the effectiveness of our business processes.
We may want to evaluate a change to some aspect of how we carry out our business or how we manufacture, provide our products.
We may want to understand the impacts of some of our business decisions.
We may have a scientific question. What we are trying to produce generalizable knowledge to understand the world around us.
We may have a social interest,
particularly if we're deploying data science in the context of a nonprofit or of a government agency or of an educational institution.
What we're trying to understand, social dynamics of human behavior. We're trying to understand maybe the impact of a policy on on.
For example. We may have social interest where particularly if we are deploying.
We may have social interest, particularly if we are deploying data science in a nonprofit or a government agency
or an educational setting where we're trying to understand social dynamics,
we're trying to understand the effectiveness and the impact of policies and programs
and various other purposes to inform our social mission with quantitative insights.
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,
I find this quote from Sergios Mondo's Introduction to science and Technology Studies useful in a book.
He says, By itself, some piece of data has no meaning.
Data is only given meaning as evidence by the people who make use of it.
And this means that we can't just go find a bunch of data.
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.
We need to do work to frame our questions in a way that we can actually.
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,
which is one of the reasons why we're going to be using our own class time for
discussion and application exercises is because we need the data of the data,
gains its meaning and becomes evidence for particular conclusions of particular answers.
Because we go through the process of interpretation and we go through the process of presenting our
interpretations for other to others and discussing and debating how we came to the conclusions,
the conclusions. We came to the level of support they have from the data.
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.
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.
Someone gives you some data and you first need to be able to get your bearings and understand what data you have,
be able to assess whether or not it would answer particular questions and what questions you might be able to answer with it.
You need to be able to define a question that we can answer.
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.
But taking a goal that we have and turning it into a question that we can actually answer
through a data analysis is a process that takes work and we're going to be learning that.
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,
it's reproducible, so others can do the same analysis. And it also doesn't unnecessarily waste computing resources to come to the answers.
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.
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,
charts and graphics and the written argument to be able to communicate to other people,
your peers and the class myself in the future, your advisers, your supervisors at work,
what it is that you learn from the data and why your conclusions are a reliable and defensible interpretation of the data.
I want you to be able to identify weaknesses in data analysis.
No data analysis is perfect.
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,
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.
The result? Some weaknesses are fatal flaws, so we can no longer trust the results we get.
Other weaknesses, however, are.
Caveats that we need to acknowledge and account for when we apply the results, but they don't fundamentally undermine their validity.
I also want you to be able to reason about the ethical implications of your work.
There's a variety of different frameworks and perspectives that we can take on the
ethics of data science work that we're going to be talking about this semester.
But I want you to be able to think about and account for and assess the ethical implications of data science work.
And then finally, I want you to understand how various the broader picture of data science,
the various techniques and applications fit into a framework to give you a map of the of the space,
and particularly with the role this class plays in Boise State's graduate curriculum.
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,
such as machine learning or large scale data analysis or recommender systems or social
media mining together and develop a coherent picture of what it is to do data science.
Support these outcomes, there are a number of components of the class. The first is the videos and readings.
You're watching one of those videos right now. This is going to be the primary mechanism for content delivery.
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
clear up things that that are confusing or things you have questions about.
But our primary content delivery is going to be through these prerecorded lecture videos.
The website is your guide to all of this. Each week is going to have a page that lays out the videos,
the readings that you need to do in order to be prepared for class, prepared for the assignments and to learn the material.
Are in class, meetings are going to be focused on discussing and applying the materials, some of that will be open discussion,
some of that will be guided application exercises particularly carried out with your classmates.
We're going to be having some quizzes, particularly each Thursday.
There's going to be a quiz before class that assesses your understanding of the material and give both
you and I an initial check on how well your understanding it and whether you're ready to apply it.
In our work and in the assignment,
we're going to have assignments that come at a relatively steady pace throughout the semester, one every other week.
They give you more extended place to practice and develop your skills and demonstrate
your mastery of the material that we're going to be covering this semester. And then finally,
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.
As we go through the semester,
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.
You can get it in our class meetings, both from your peers in the class and from me.
You can get it at any time through the class forum on Piazza.
I strongly encourage you to ask questions and answer each other's questions on Piazza when you ask a question,
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.
Oftentimes, helping other people answer questions strengthens our own understanding of the material.
So I encourage you to make use of Piazza both to answer your get your questions answered
and to develop and practice your understanding of the material through helping each other.
I am going to be having office hours. I'm going to be having them physically in my office so long as health permits.
And I will also be able to have remote office hours by appointment.
I do ask that if you have any inquiries about the class that you direct them through Piazza.
If it's something that's just for me, perhaps about grades or something,
you can send a private message to the instructors on Piazza that's going to let me keep all
my course communications in one place so I don't accidentally lose something in my email.
And finally, you can use the Internet as a resource where there's a lot of documentation, resources.
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.
I encourage you to make use of public Q&A sites when you're a practicing data scientist after this class.
The Internet is at your disposal for getting your work done.
There's no reason not to go ahead and practice, make use of the resources that are available to you in this class.
I just ask that you do it in a way that's in keeping with the principles of academic integrity,
you can go to a public Q&A site and you can ask, I'm trying to do this thing.
I'm having a problem. And ask a specific question about the problem that you're getting stuck on.
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?
Demonstrate your learning of the material and how you go about identifying where it is that you're stuck and
asking a question that's going to help uncover the missing piece that you need in order to move forward.
So it's all of that. This class is designed to provide flexibility in learning and to use each modality to its best advantage.
There are tradeoffs. I'm not going to pretend like there's no trade off the how I've designed this class.
But with this design, we can do the content delivery me lecturing in a way where you can go back and watch videos later.
You can catch up. If you have to miss some material, you can speed up the video.
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.
Where we're actually engaging with the material together and talking to each
other about it and these things together with the various parts of the class,
I hope are going to allow us to achieve our learning outcomes for the semester. I hope you have a great semester.
I hope you learn a lot. I also expect to learn from you every time I teach.
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.
So let's learn together.