Handbook

Data science approaches (blueprint)

**Purpose:** Deeper, **project-agnostic** guides for data science methodologies and process frameworks. Each approach describes its structure, phases, and mapping to the lifecycle.

Data science approaches (blueprint)

Purpose: Deeper, project-agnostic guides for data science methodologies and process frameworks. Each approach describes its structure, phases, and mapping to the lifecycle.

Audience: Teams adopting Data science & machine learning; project-specific ML decisions stay in docs/architecture/ and docs/adr/.

Methodology selection

Pick a process scaffold first (goals, data reality, deployment), then layer operations (MLOps) when models must live in production under change control. CRISP-DM fits most greenfield analytics and ML projects; MLOps is not a replacement for CRISP-DM but the engineering wrapper around deployment and monitoring. Experiment management and A/B testing address how you learn during development and how you prove impact in production — they complement, rather than substitute for, a phase model. If you are unsure where to start, read crisp-dm.md for phase definitions, then mlops.md when the path to production is real rather than hypothetical.

Core knowledge: DATA-SCIENCE.md — ML lifecycle, statistics, evaluation, MLOps, responsible AI.

Bridge: Data Science ↔ SDLC ↔ PDLC bridge — how data science maps to delivery and product lifecycles.

flowchart TB subgraph Process["Process frameworks"] CRISP[CRISP-DM\nphases & iteration] MLO[MLOps\npipelines & prod] end subgraph Learning["Learning & proof"] EXP[Experiment management] AB[A/B testing] end CRISP --> EXP EXP --> MLO MLO --> AB CRISP -.->|deployment feedback| MLO

The diagram is illustrative: in practice, experiment tracking starts early (often during Data Preparation / Modeling in CRISP-DM terms), and A/B tests may run only after a first deployment — but the dependencies (sound process → disciplined experiments → reliable production → measured impact) stay the same.

Approach	Focus	When to use	Guide
CRISP-DM	Six-phase cross-industry standard process for data mining and ML projects	Default starting point for most ML projects; industry-proven; tool-agnostic	`crisp-dm.md`
MLOps	Operationalizing ML — CI/CD for models, automated retraining, model monitoring	When ML models run in production and require ongoing management	`mlops.md`
Experiment management	Systematic approach to running, tracking, and analyzing ML experiments	During model development; comparing approaches; hyperparameter optimization	DATA-SCIENCE.md §3
A/B testing	Controlled online experimentation to measure model impact on product metrics	When validating model impact in production; PDLC P5 outcome measurement	DATA-SCIENCE.md §3

Related techniques: For encoding, feature stores, and evaluation metrics used inside these phases, see ../techniques/feature-engineering.md and ../techniques/model-evaluation.md.

Keep project-specific model documentation in docs/product/ and experiment logs in docs/development/, not in this file.

Canonical source

Edit https://github.com/autowww/blueprints/blob/main/disciplines/data/data-science/approaches/README.md first; regenerate with docs/build-handbook.py.