# drivendataorg/cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Repository: https://github.com/drivendataorg/cookiecutter-data-science
Canonical: https://ross.abutalabs.com/products/cookiecutter-data-science
Homepage: https://cookiecutter-data-science.drivendata.org/
Language: Python
License: MIT
License Family: permissive
Topics: cookiecutter-data-science, cookiecutter, cookiecutter-template, data-science, machine-learning, ai
Last push: 2026-08-07T22:09:29+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 40, longevity 100
- inputs: {"age_days": 3960, "days_push": 26, "days_rel": 405, "gap_med": 13, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10022, forks 2636 (observed 2026-08-28T04:10:39.286908+00:00)

## What it is
Cookiecutter Data Science (CCDS) is a Python CLI tool that scaffolds standardized, flexible data science project structures with best practices baked in. It extends the cookiecutter templating utility with a `ccds` command that generates a directory layout for data, models, notebooks, and documentation.

## Use cases
- scaffold a new data science project
- standardize project structure across a data science team
- set up folders for raw and processed data, models, and notebooks
- bootstrap a machine learning repo with best practices
- create a shareable template for analysis projects
- initialize a project with Makefile, docs, and license

## When to choose
- starting a new data science or ML project from scratch
- you want a community-standard, opinionated project layout
- you need consistent structure for sharing and collaborating on analyses

## When to avoid
- you already have an established project structure or monorepo conventions
- you need a full MLOps pipeline rather than a project skeleton
- your project is not data-science oriented

## Facets
- artifact type: cli-tool
- maturity: active
- function: cli, boilerplate, developer-tools, data-science
- domain: data-science, machine-learning, developer-tools
- platform: python, cli, cross-platform
- tags: cookiecutter, project-template, project-scaffolding, best-practices, reproducibility, command-line

## Member repositories
- drivendataorg/cookiecutter-data-science (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:39.286908+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:19:55.591589+00:00, confidence not recorded.
  - readme: https://github.com/drivendataorg/cookiecutter-data-science (fetched 2026-08-28T04:10:39.286908+00:00, sha 4d642324c055)
  - homepage: https://cookiecutter-data-science.drivendata.org/ (fetched 2026-08-29T08:19:42.853514+00:00, sha 0868170ef8af)
  - registry_pypi: https://pypi.org/pypi/cookiecutter-data-science/json (fetched 2026-08-29T08:19:42.856347+00:00, sha d13d98d54180)
- Data as of 2026-08-30T08:39:29.467469+00:00.