Week 0 — Pre-Class Welcome¶

Hello, and welcome to CS 533! This page isn’t really a course week — it’s a quick orientation so you can know what to expect in our class, and things to do if you want to start preparing ahead.

Element	Length
🎥 0 - Welcome	7m12s

This week has 0h7m of video and 0 words of assigned readings.

Welcome Video¶

Welcome to class!

Video (7m12s)

Edit subtitles on Amara

Slides

Hello and welcome to CS533.
Whether this is your first class at Boise State or your 30th, I'm very pleased to welcome you to Introduction to Data Science.
I'm Michael Ekstrand, your professor for this fall.
In this video, I wanted to give you a little bit of an introduction to what to expect this fall,
how I've structured the class to make use of the different modalities we have available to us,
how to prepare ahead if you're looking to to get a jump start on the semester,
and to talk a little bit about how to succeed in a semester,
that's going to look more normal than the last couple of semesters, hopefully.
But still, we have a lot of lingering public health risks that we need to to be prepared for and be flexible for.
There are several components of this class and the overall structure may be a little bit different than what you might be used to from other classes.
There are a couple of reasons for this. One is to give the flexibility needed in order to complete the semester successfully
no matter what curveballs may come our way, to enable you to get to get caught up if you have to miss class for a little while,
and also to make use of each of the different ways that we can interact in a way that captures the unique potential of that particular interaction method.
So to start with, I'm not actually going to be lecturing in class very much.
We're going to talk a lot more about the course details, the first class meeting on Tuesday,
but the primary content delivery is going to be through online video lectures and readings.
The course website is going to contain all of those. So for each week, you can scroll through and you can see that week's readings.
You can get access to my lecture slides. You can watch the videos and rewatch them at your own pace.
When we're in the classroom, we're going to be using this time and this space for things that can really only be done in a classroom environment.
We're going to be focusing on engaging with the material together through a combination of discussion,
small lectures to clear up additional material you may have questions on,
group activities that you're going to work on with your classmates to apply and practice and develop your knowledge of the material,
as well as taking our exams and having some time for collaborative problem solving on the assignments.
Science of all kinds, data science or other analysis, is a social process.
It's done by people in communication with other people. And so we're going to use this time to practice that,
engage with the material of the class and the problems that we're applying to together as a group
to collaboratively deepen our understanding of data science and the things to which we apply it.
These online lectures and our in-person engagement are going to be supplemented with a set of
assignments throughout the semester to enable you to develop more extended practice with the material,
concepts and techniques that we cover and to demonstrate your ability to apply them to more significant projects,
both with code and with writing and visuals.
We also have two mid-term exams and a final for you to demonstrate your understanding of key class concepts.
These are structured so that the workload throughout the semester should be fairly even the assignments come at a pace every other week,
we don't have a big culminating project at the end that you're going to have to do a significant amount of extra work.
You should have even and predictable load between the assignments, the exams and our various class and learning activities throughout the semester.
There's a lot of moving pieces here that you need to keep track of at this class.
So I want to give you a few tips for success both throughout the class and also in preparing for it.
The first is to read the syllabus. There's a lot of information in the syllabus about the course structure, about the course policies,
including various policies to help us plan ahead for the unexpected this semester.
Also, keep up on the videos and readings. The first week of content is going to be fully available before class even starts.
If you want to start working ahead, you can you can peek ahead to the content from last year and providing a link to that here.
If you want, make sure you check the assignments and deliverables this year before you work off of old instructions.
But most of the material that I'm doing is going to be very, very similar to what we did with the fully online course last year.
Thursday, we're going to be having quizzes on each week's reading material and videos.
So work ahead, understand the material and be prepared in order to take the quiz.
That's going to help both you and I assess your understanding in the material as we start working into applying it.
I also encourage you to make use of the features of the video interface.
Panopto allows you to change the playback speed, If I talk too quickly or too slowly for you
You can also download the videos to watch them on your own device in another context.
And I also recommend that you get started on the assignments early.
They're going to be up at the beginning of the two week period, at the end of which there do you can get started right away.
And we're going to be giving time early in the second week of the assignment for collaborative problem solving and discussion on the assignment.
And you'll be able to take the best advantage of that if you've already gotten started.
I'm here at the Idaho Anne Frank Human Rights Memorial to conclude this video,
because as we will see as we get into the material, many of the applications of data science have a profound human impact.
We may be doing data science to understand human behavior, the effects of human activity.
We may be doing data science to make decisions or recommendations that are going to affect people.
People will have to implement many of the decisions that result from our analysis, even when the analysis themselves are not about human subjects.
And one of the goals of my teaching and my research is to use technology, data science, and computing for human flourishing.
And so one of the things that I want to equip you with in this class,
besides the technical, mathematical and the conceptual underpinnings in order to carry out the work of data science
is tools to be able to understand and reason about its human impact,
to do data science in a way that respects human dignity and advances justice.
And I also want this class itself to work for the humans it affects: you.
So I have a design for how the class is going to work,
but there's also an ongoing conversation if you have concerns other in advance of the class or how,
as the class progresses about what's happening, if there are things that are not working for you, I want to hear about those.
You can email me, you can post a message on Piazza. Can't guarantee I can fix everything.
Some things I may need to take into account for the next iteration of this class.
But I want it to work for you and I want it to work for the students who are going to come after you.
So with that, I look forward to seeing you in class. And let's learn some data science.

Course Design¶

This is a flipped classroom course. Online videos — like the one you just watched — along with various readings are the primary content delivery mechanism. We’ll use our course time for engaging with and practicing the material together.

There are several reasons for this design. First, it uses our time together in the classroom for things that can only, or at least optimally, be done in a classroom environment; pushing the lectures out to video makes more time for collaborative practice and engagement. Second, having the content online will make it easier to review the material for assignments and exams, and to catch up if you miss time due to illness or other concerns.

On Thursday of each week, there will be an in-class quiz over the lectures and readings. Some weeks, it may be over a portion of the material; in such cases, I will clearly mark the cutpoint for the quiz.

Syllabus and Schedule¶

Two key documents for the semester:

The syllabus describes the course structure, requirements, and policies.
The schedule does what it says.

Workload¶

You will need to plan your time in order to succeed in this class.

There are 7 real assignments (A1–7), each intended to take approximately 8–10 hours over 2 weeks. They may take you more or less time, depending on how quickly the material and requirements click for you. Assignments will come at a steady pace, though — there is no big end-of-term project.
Each week has approximately 75–90 minutes of video content, plus readings. To help you plan, I provide the length of each video and most of the readings (reading lengths denoted in words).
You may need to spend some additional time studying and practicing material, although our in-class time will hopefully help with a lot of that.

Study and practice are necessary to be a competent data scientist. There are no shortcuts to expertise. My goal with this class is to give you a solid, well-practiced foundation for the core technologies and techniques you will need in the rest of your data science education and work.

Reading Ahead¶

If you want to get started early, some things you can do:

Install the software
Begin Week 1
Start reading the textbooks
Review the Fall 2020 site for an idea of what’s coming

Feedback¶

I welcome your feedback throughout the course on what’s working for you and things that might need adjustment. Please send me a message on Piazza or e-mail me.

CS 533 Fall 2021