Python
Practical code for the work — pandas, scikit-learn, numpy, used the way practitioners actually use them.
8 articles, newest first.
- From notebook to production without a rewrite Notebooks are where analysis is born, not where it should live. A low-drama path: extract functions, add tests for data logic, parameterise, and schedule a script.
- SQL or pandas? Push the heavy lifting to the database A practical rule for splitting work between the warehouse and your laptop: aggregate where the data lives, iterate where your tools live.
- Schema checks: make your pipeline refuse bad data Upstream will change without telling you. Explicit schema and expectation checks at pipeline boundaries turn silent corruption into loud, early failures.
- Reproducibility: the checklist nobody follows until it burns them Seeds, pinned environments, data snapshots, and one-command reruns — the practical minimum for results you can regenerate in six months.
- Pandas cleaning patterns that scale past the toy example Method chaining, assign, vectorized string ops, coercion audits, and validation asserts — a working style for cleaning code you can still read in six months.
- Gradient boosting is still the default for tabular data Why LightGBM/XGBoost keep beating neural networks on tables, the five hyperparameters that matter, and the standard mistakes — including early stopping done wrong.
- The bootstrap: uncertainty for people who hate formulas Resample your data with replacement, recompute the statistic, read the spread — honest confidence intervals for medians, ratios, AUCs, and anything else.
- Monte Carlo methods: answering hard questions by rolling dice When the maths is too hard, sample instead. What Monte Carlo is from first principles, why error falls as one over root n, and where it earns its keep in real work.