Cookiecutter Data Science
Standardized project template for data science teams
CCDS v2 is a solid upgrade for data science teams wanting a reproducible project scaffold. It's opinionated enough to enforce best practices, yet flexible across environment managers and cloud backends. If you need a quick, standardized start for a Python data science project, this is it. Compare with ad-hoc layouts or project templates from DVC or Kedro.
- Data scientists starting new Python projects
- Teams wanting a consistent, reproducible project structure
- Educators teaching data science best practices
- Open-source data science projects needing a standard layout
- Non-Python data science stacks (R, Julia)
- Users preferring a graphical user interface for project setup
- Ad-hoc, single-notebook analyses
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Skip Cookiecutter Data Science if you work in a non-Python stack (R, Julia), prefer a GUI over CLI, or need built-in experiment tracking and pipeline orchestration.
CCDS is free and open source. No hidden costs. It fits any team size or budget, as the only investment is time to learn the template. Compare with DVC (free) or Kedro (free) which offer more features but also free.
In short
Cookiecutter Data Science — Standardized project template for data science teams. Best for Data scientists starting new Python projects, Teams wanting a consistent, reproducible project structure, Educators teaching data science best practices. Free to use.
Viability Score
How likely is Cookiecutter Data Science to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Standardized directory structure for data science projects
- Interactive project setup via `ccds` command-line tool
- Supports environment managers: virtualenv, conda, pipenv, uv, pixi, poetry
- Supports dependency file formats: requirements.txt, pyproject.toml, environment.yml, Pipfile, pixi.toml
- Cloud storage integration: Azure, AWS S3, GCS
- Testing frameworks: pytest, unittest
- Linting and formatting: ruff, flake8+black+isort
- Documentation generation with mkdocs
- Open-source license selection: MIT, BSD-3-Clause, or none
- Configurable Python version (default 3.10)
- Pre-built Makefile with commands: make data, make train
- Separates data into raw/interim/processed folders
- Notebooks directory for exploratory analysis
- Optional source code scaffold for Python package
- Installable via pipx or pip
About Cookiecutter Data Science
Cookiecutter Data Science (CCDS) is a logical, flexible project structure for data science and ML projects. It provides a standard directory layout separating data, code, notebooks, and documentation, helping teams organize work reproducibly from day one. Built on the Cookiecutter templating utility, CCDS v2 introduces a dedicated `ccds` CLI and Python package for interactive setup. It supports multiple environment managers (virtualenv, conda, pipenv, uv, pixi, poetry), dependency file formats (requirements.txt, pyproject.toml, environment.yml, Pipfile, pixi.toml), testing frameworks (pytest, unittest), linting (ruff, flake8+black+isort), docs (mkdocs), and cloud storage backends (Azure, AWS S3, GCS). Designed for data scientists and teams who want best practices without lock-in. Free and open source, maintained by DrivenData.
Behind the Verdict
Cookiecutter Data Science fills a simple but crucial gap: every data science project starts the same way, yet most teams reinvent the folder structure each time. CCDS gives you a battle-tested layout with sensible defaults (raw/interim/processed data, notebooks, source code, docs) and lets you pick your tools — environment manager, testing framework, linter, cloud storage — through an interactive CLI. The v2 update modernizes the experience with a dedicated `ccds` command and support for uv, pixi, and pyproject.toml. It's not a full platform: no pipeline orchestration, experiment tracking, or deployment. For those needs, pair it with something like DVC, MLflow, or Airflow. Best for teams that value reproducibility and want a consistent starting point across dozens of projects. Less useful for solo analysts who prefer a single-notebook workflow or non-Python stacks. The open-source license (MIT/BSD) means you can fork and customize. If you find the structure too rigid, you can create your own Cookiecutter template. Overall, a low-risk, high-upside standardisation tool for any Python data science team.
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Real-world workflow fit
Concrete scenarios for the personas Cookiecutter Data Science actually fits — and what changes day-one when you adopt it.
You have a new analysis to start. Run `ccds` in your terminal, answer a few prompts (project name, cloud storage, environment manager, etc.), and a full project folder is created with data/, notebooks/, src/, docs/, Makefile, and README.
Outcome: Within 2 minutes you have a reproducible project scaffold with version control, virtual environment, and folder conventions — saving 30 minutes of manual setup.
You want all team members to use the same layout. You create a custom Cookiecutter template based on CCDS and ask everyone to use `ccds` when starting a new project.
Outcome: All team projects follow the same structure, reducing context-switching and making it easier to share code between projects.
You're teaching a data science class and want students to submit projects with a consistent format. You provide the CCDS template, and students generate their projects with `ccds`.
Outcome: Students spend less time on folder setup and more on analysis, and grading is easier because every project has the same structure.
Use Cases
- Start a new data science project with a standardized folder structure
- Set up reproducible environments and dependency management with conda or uv
- Integrate cloud storage for datasets directly from project scaffolding
- Adopt testing and linting best practices from the beginning
- Generate project documentation with mkdocs automatically
- Collaborate with a team using a consistent project layout across repositories
Limitations
- Cookiecutter Data Science is a project template, not an end-to-end platform.
- It does not include built-in data pipeline orchestration or model deployment.
- Users must still configure cloud storage and CI/CD separately.
- The template assumes Python-centric workflows.
as of 2026-07-05
Where the pricing makes sense
The company stage and team size where Cookiecutter Data Science's pricing actually pencils out — and where peers do it cheaper.
CCDS is free and open source. No hidden costs. It fits any team size or budget, as the only investment is time to learn the template. Compare with DVC (free) or Kedro (free) which offer more features but also free.
Setup time & first value
How long it actually takes to get something useful out of Cookiecutter Data Science — broken out by persona, not the marketing-page minute.
New user: ~2 minutes to install via pipx and run `ccds` interactively. Team: ~1 hour to customize template if needed. No learning curve beyond basic terminal usage.
Switching to or from Cookiecutter Data Science
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ad-hoc project folders: run `ccds` in a parent directory, then copy your existing code into the appropriate subdirectories (raw/processed/notebooks/src).
- →From v1 template: re-run `ccds` to generate a new v2 project and migrate your code manually; no automated migration tool.
- ↗To a custom structure: simply copy the code out of the CCDS directories into your new layout; no lock-in.
- ↗To DVC or Kedro: CCDS structure can be a starting point; you can add DVC pipelines or Kedro nodes incrementally.
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