drivendataorg/cookiecutter-data-science

Health v2 · maintenance only

Adoption not part of the score

10086 stars · 2632 forks observed · 2026-10-12

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

cli-tool · maturity active

cli boilerplate developer-tools data-science data-science machine-learning developer-tools python cli cross-platform cookiecutter project-template project-scaffolding best-practices reproducibility command-line

Member repositories

For agents

markdown · JSON · MCP: product_card(name="drivendataorg/cookiecutter-data-science")