Core focus
- Practical statistics: hypothesis tests I can actually defend in a meeting.
- SQL window functions for cohort and retention analysis.
- Feature engineering for tabular ML, before reaching for a model.
Books and courses I keep open
- Practical Statistics for Data Scientists (rereading).
- StatQuest videos for anything I half-understand.
- Kaggle notebooks for whichever technique I studied that week.
What I'm resisting
The urge to jump to deep learning before the fundamentals feel boring. Boring is the goal.