Python for researchers — replace SPSS/SAS, reproducible analysis, LaTeX-ready figures
Your co-author can't open your SPSS file. Your reviewer can't rerun your SAS macro. Your student can't afford either. Python fixes all three.
Reproducibility is the crisis in every quantitative field, and the fix has one shape: analysis lives as code, not clicks. A Jupyter notebook that anybody with Python can run, cell by cell, is the reviewer's dream and the peer-review process's actual expectation in 2026. SPSS syntax files nominally do this, SAS macros too — but neither runs on your student's laptop, on Colab, on Kaggle, or on the referee's phone.
Data Science track (104 lessons): pandas (data manipulation), numpy (linear algebra), scipy.stats (parametric + nonparametric tests, mixed models, bootstrap), statsmodels (regression tables reviewers actually want), matplotlib + seaborn (publication-grade figures with a stable style guide). Foundations for the syntax. Every lesson runs in browser via Skulpt / Pyodide — first 15 free.