Don’t Miss Module 00
This overview is designed to orient you to the class. Please watch the videos from this playlist and work your way through the notes. Although the module-level playlists are embedded in the course, you can find the full-course video playlist here. In addition, you can find the slides for this module here. Currently, there are seven videos in this playlist. The average video length is 12 minutes, 27 seconds. The total length of the playlist is 1 hour, 27 minutes, 10 seconds.
Data Science for Psychologists (DS4P) introduces on the principles of data science, including:
- data wrangling,
- modeling,
- visualization, and
- communication.
This class links those principles to psychological methods and open science practices by emphasizing exploratory analyses and description rather than confirmatory analyses and prediction. Through the semester, we will work our way through Wickham and Grolemund’s R for Data Science text and develop proficiency with tidyverse. This class emphasizes replication and reproducibility. DS4P is a practical skilled-based class and should be helpful to students aiming for academia and those interested in industry. Applications of these methods can be applied to a full range of psychological areas, including perception (e.g., eye-tracking data), neuroscience (e.g., visualizing neural networks), and individual differences (e.g., valence analysis).
0.1 Big Ideas
This class covers the following broad five areas:
- Reproducibility;
- Replication;
- Robust Methods;
- Resplendent Visualizations; and
- R Programming.
0.2 Structure and Organization
Everything in this course is written in R. Yes! Even the slides and the course notes. The course notes are written in R Markdown and rendered with bookdown. The slides are written in R Markdown and rendered with xaringan. The labs and homework assignments are written in R Markdown and rendered with rmarkdown.
Now that’s a pretty cool feature … but it has actual tangible benefits for you! For one, it means that if you want to learn how to do something, you can just look at the source code for the slides or the course notes and see how I did it! But how? Well, all of the course materials are hosted on GitHub. You can access the source code for the slides, course notes, labs, and homework assignments directly from GitHub. This means that you can see how I made the slides and course notes, and you can even make your own edits to them if you want to! This is a great way to learn how to do things in R and to see how I approached different problems. You can also see how the code evolved over time by looking at the commit history on GitHub. For example, you can see that I added in a link to the history of this file. This is a great way to learn how to use Git and to see how I worked through different problems or revisions of the course materials.
0.2.1 GitHub Organization
You can think of the GitHub organization as the “home” for all of the course materials.
Everything in this course lives on GitHub. What it looks like:
- “The syllabus website” ➡️ smasongarrison.github.io/syllabi
- “The course notes site” ➡️ DataScience4Psych.github.io/DataScience4Psych (you’re looking at them right now)
- “The slides” ➡️ DataScience4Psych.github.io/slides/
- “GitHub Organization” ➡️ github.com/DataScience4Psych
What this means is that you can access all of the course materials from the course notes website. The course notes website is your home base. From there, you can click links to access the slides and labs. The slides are where the in-class content lives, and the labs are where the out-of-class content lives. You can also access all of these materials directly from GitHub if you prefer, but I find it easier to navigate through the course notes website.
There are often embedded slides that you can follow along with. You can open them in their own window if they are too small or do not appear below. This link to them is here.
To navigation within the slides, you can use the left and right arrows on your keyboard. It’s a little trickery on an ipad or phone as sometimes you’ll end up swiping to the next section of the book. If you have trouble with it or your screenreader doesn’t respond as you’d hope, please reach out! The source code for the slides is available here, and you are welcome to make edits to it if you think of ways to make it more accessible or easier to navigate.
0.3 Course Modality
I have taught this class in practically every modality available.
Pragmatically, the face-to-face portions of the class are – well – face-to-face. Or that was the idea anyway… however, during the first semester I taught this course, a few members of the class were on the other side of the planet. Accordingly, I pivoted all the planned in-class activities and labs so that the entire class could complete their degrees on-time. So obviously this last-minute pivot was a little messy, but I think it turned out ok… So again, technically, this class was blended, but effectively, it can be completed from anywhere at any time. It had to be.
0.3.1 Successful Asynchronous Learning
I’ve created a video highlighting how to be a successful asynchronous learner.
Much of this information comes from Northeastern University’s Tips for Taking Online Classes
0.4 Learning to Code in the Age of AI
I have thought a lot about how to approach teaching coding in the age of AI. I have a few thoughts on this, which I share in the slide deck below. I’ve also written a book chapter with two of my former students on this topic, which you can soon find online.
S. Mason Garrison, Holland K. Tyson, and Xuanyu Lyu (2026). Pragmatic AI Integration in R Teaching: From Frustration to Facilitation. In I. Katzarska-Miller, M. Jackson, & M. Fortner. (Eds.), Integrating generative AI in psychology courses (pp. x–xx ). Society for the Teaching of Psychology.