How to not fool yourself as a data scientist in the Age of AI. Where Bots End, You BegiN.

The Problem: Data science students often have incredible technical skills in computation and statistics but still have trouble critically solving problems with data.

The Solution: Students learn to think differently about solving data problems, by using scientific reasoning and avoiding common cognitive and statistical pitfalls.


We aim for: 1) Fewer mechanical use of methods and more engagement in the thrill of discovery; 2) fewer statistical errors and biases through more rigorous testing; 3) more critical thinking while analyzing data; and 4) a better integration of descriptive statistics with explanations.

Teaching Materials

This section serves as the access point to the open teaching materials Julia Koschinsky and Jay Cordes are developing or engaged with over the next year (2023). We integrate scientific reasoning in two data science contexts: Statistical pitfalls and geospatial methods.


Resources are emerging that integrate scientific reasoning with data science education. But they are hard to find, in disparate locations. We bring together our favorite resources on this site. Explore courses, modules, books, blogs, podcasts op-eds, movies, and cartoons.

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