Projects Open resource Since 2019
Open teaching materials
Roughly a hundred notebooks, slide decks and datasets covering Python, numerical methods, Bayesian ML, NLP and actuarial data science, all free to reuse.
Start here
Two written pieces sit in front of the notebooks, because every cohort loses its first session to the same two questions:
- Why Python?: where the language sits today, why scientific computing converged on it, and what it costs you in speed, memory and packaging.
- Installing Python: conda versus pip, one environment per project, getting Jupyter to point at the right kernel, and what to do when it breaks.
What is in there
Five institutions’ worth of course material, accumulated since 2019 and kept online deliberately: EPITA (Python, numerical algorithms, Bayesian ML), the Institut des Actuaires (text mining, active learning, mortality modelling), Cnam (fairness in insurance), ISFA and ENSEA Abidjan (actuarial data science), and tutorial material from ENSAE.
Most of it is notebook-first. The lectures exist to explain why the notebook does what it does, not the other way round.
Reuse
Everything is free to use for teaching. If you adapt a session, a link back is appreciated, and I would genuinely like to hear what you changed, because that is usually where the material was weak.