We read many data science education papers so that you don’t have to

Summary of findings from a a systematic literature review on data science education research

for professors
Author

Mine Dogucu

Published

May 17, 2025

I should first note that the title of this blog post is a joke. You should absolutely read about data science education research. I recently saw this cartoon by Tom Gauld and found it hillarious. So the title is my minor attempt in trying to be funny.

A paper that I cowrote with Sinem Demirci, Harry Bendekgey, Federica Zoe Ricci, and Catalina Medina has recently been accepted to be published in Journal of Statistics and Data Science Education. We conducted A Systematic Literature Review of Undergraduate Data Science Education Research. You are welcome to read the full paper including the methods, and results without which it might be difficult to understand what I have to say. In this blog post, I want to share a few summary points and key takeaways for those who may not have time to read the paper.

  1. Just like data science itself, data science education is an emerging field that is separate from statistics, computer science, math, and science education.

  2. We need empirical data in data science education research. Many publications we have read lacked data and research questions. Many were opinion pieces or many shared teaching experiences. These can be valuable contributions to the field but we also need evidence-backed studies with rigorous research design, especially teaching experiences can be studied in depth.

  3. Among the studies that utilize data, there were both quantitative, qualitative, and mixed sources of data.

  4. Although data science and data science education are interdisciplinary disciplines Computer Science could be dominating the field (see full paper to understand what I mean and the limitations of our study). We need more explicit visibility of statistics, mathematics, and other disciplines in data science education.

  5. We have classified studies based on their primary aim such as sharing a classroom activity or a course example design or testing a pedagogical approach. Our data is publicly available on OSF and GitHub. This dataset can especially help novice researchers in data science education find inspiration and examples from the existing literature.

If you liked this blog post, you can follow my YouTube channel. Ok, I accept, this was a really bad attempt at trying to be funny.

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