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	8m14s

This week has 0h8m of video and 0 words of assigned readings.

Welcome Video#

Welcome to class!

Video (8m14s)

Slides

Hello and welcome to CS 533.
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,
and in this video I wanted to give you an introduction to what to expect this fall,
how I've structured the class to make use of the different modalities available to us.
How to prepare ahead, if you're looking 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 the 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.
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.
We're going to use our synchronous time, when we're meeting together in real time,
whether it's in-person in this classroom or online via Zoom
to do those things which are really best and sometimes only done with synchronous,
real time communication.
We're going to be discussing the material.
We're going to be doing application exercises in small groups
with your classmates to practice and solidify your understanding.
There will be opportunity to ask questions, and I might do little mini lectures
in order to clear up some of your understanding.
We're going to be having short team tests to allow you
together check and improve your understanding
of the material that you've seen in the online video lectures.
That's going to be on Thursdays.
Tuesdays are going to be a remote online synchronous time that's unstructured.
I'll be signed into the course Zoom and you can sign in and ask questions.
It'll basically be open Q&A and discussion on the material that we're working through
and a little bit of extended group office hours.
Science and data science are social processes.
As we'll see as we get into the material next week,
Data only turns into meaning as people give it meaning,
and that happens in communication with others.
We communicate our results to our colleagues,
whether they are our fellow students, whether they're our colleagues in the workplace.
We communicate them to our graduate advisors.
We communicate them to our supervisors.
We communicate them to the broader world through the form of scientific papers
or white papers and technical reports in commercial settings.
Together we develop and we vet our interpretations
and the meaning that we're deriving from the data
as we see whether others are convinced, as we take their feedback and incorporate it.
So we're going to be trying to use the time that we have together to do that kind of thing so we can practice,
so we can develop the set of skills that we need in order to not only do data science,
but communicate our results persuasively and understand and incorporate the feedback
that's going to make them more rigorous, more reliable, and more persuasive in the future.
These online lectures and our in-person engagement are going to be supplemented with
a set of assignments throughout the semester to allow 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 midterm 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 an 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 in 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.
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 of 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.
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 before they're due
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.
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 analyses,
even when the analyses themselves are not about human subjects.
One of the goals of my teaching and my research is to use technology -
data science, computing - for human flourishing.
And so one of the things that I want to equip you with in this class,
besides the technical and the 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 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 that 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, either in advance of the class,
or 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 me 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 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 virtual or physical classroom for things that can only, or at least optimally, be done in a synchronous 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

The course web site is pre-populated with the content from last year so you can look ahead for what to expect; this year will be quite similar, but I am also making some revisions. Each page has an alert banner that I will remove once I have reviewed and revised that page for this year.

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 2022

Week 0 — Pre-Class Welcome

Contents