Teaching introduction to statistics and statistical computing

I taught a graduate-level introduction to applied statistics and statistical computing this past Spring. The course design iterated on a class Mako developed in 2017. Very nearly all of the course materials are available open access through the Community Data Science Collective wiki and I wanted to make sure to share them more widely with this post. I’ve also been reflecting a bit on how the course went and thought I’d share those thoughts here in case anyone wants to adopt the course in the future.

First off, the course uses the OpenIntro Statistics (3rd edition) textbook as the core of the course readings and assignments. If you’re not familiar with OpenIntro and you want to learn or teach applied statistics from a general, social scientific perspective, you should check it out! All of the data, code, and LaTeX used to produce the textbook is licensed freely for reuse and the site also hosts video lectures, lecture notes, homework assignments, a discussion forum and more.

Alongside the OpenIntro materials, I worked together with Jeremy Foote (who was the TA for the course before he left to be new faculty at Purdue) to develop a bunch of tutorials in RMarkdown to help students complete the problem set assignments. We also posted worked solutions to the problem sets (also in RMarkdown). These replicated and expanded on screencasts Mako had recorded for his course.

The classroom sessions focused on discussion and problem solving. Basically, students came to each session knowing that I expected them to have completed the problem sets. I then did my best to answer any questions people had and assigned individuals (in some cases using a randomization script in R to pick names!) to summarize their solutions and approaches to specific problems that seemed important to cover.

It was my first time teaching a course like this and I had a few reflections after completing the quarter and reading through the feedback from students.

A major challenge for a course like this is pitching the material to an appropriate level given that students (in the MTS and TSB programs here at Northwestern at least) arrive with such varied knowledge of the subject matter. I think I did okay on this front in some ways and not in others. It was especially challenging given the semi-flipped classroom approach.

In some weeks, there was just too much material to cover in adequate depth. In some others, I was insufficiently organized and concise to cover everything. Whatever the case, I would cut back a bit next time. (I’ve noticed that this is a common issue for me the first time I teach any class, but I still struggle to correct it.)

Whatever challenges and failures I may have introduced in the design or instruction of the course, the students produced a bunch of highly original and engaging final projects. I’m optimistic that some of these projects will wind up as published work soon. Nothing like brilliant, motivated students to help the professor feel better about his own shortcomings!

Nearly all of the course materials are available on the CDSC wiki. The exceptions are a few of the readings and supplementary materials that I didn’t have the rights or desire to post on the public web. If you’re looking for any of that, feel free to send me an email and I can see if it’s appropriate to share.

Also, OpenIntro just came out with the fourth edition of their statistics textbook! I haven’t had a chance to check it out yet, but I’m eager to see what kinds of changes they introduced.