ai-ml-data-science

# Data Science Engineering Suite Use this skill for reproducible data-science work from problem framing through evaluation and handoff. The center of gravity is not "pick the fanciest model." It is framing the decision, building train-serve-safe features, and producing decision-ready evidence. ## ASCII Flow ```text data question | v problem framing target + unit of analysis + leakage risks + decision/use case | v data work source checks + EDA + feature logic + split strategy + baseline | v model/evidence train or analyze + validate + interpret + quantify uncertainty | v handoff report, notebook, model candidate, or production path to MLOps ``` ## Quick Reference | Need | Default Direction | |------|-------------------| | reproducible Python workflow | `uv` plus scripts or git-friendly notebooks (marimo for reactive/diffable notebooks) | | fast local analysis | DuckDB plus Polars (v1.x stable API as of 2026; pre-1.0 API-churn concerns no longer apply) | | data contracts | Pandera or GX Core at dataset boundaries | | tabular baseline | linear or logistic model plus tree-based candidate | | feature engineering | explicit train-serve-safe transforms | | unlabeled text corpus | embed -> UMAP -> HDBSCAN -> c-TF-IDF; LLM labels once per topic, never per document | | tuning | Optuna only after the baseline is stable | | evaluation | slices, threshold, calibration, uncertainty | | handoff | model card, evaluation report, failure modes, monitoring expectati...