Cookiecutter Data Science is live · free · last verified 14 Nov 2026 · viability healthy
Cookiecutter Data Science
Cookiecutter Data Science (CCDS) scaffolds a standardized, reproducible Python data science project with one `ccds` command.
Pick CCDS v2 if your team keeps re-arguing folder names and where 'final_v3.csv' belongs. The `ccds` wizard captures the environment and tooling choices most Python teams actually make — uv, pixi, poetry or conda, pytest, ruff, mkdocs, and dataset storage config for azure, s3 or gcs — and costs nothing. It gives you structure only: no orchestration, no scheduling, no experiment tracking, so pair it with DVC or Kedro if you need those. If you want a batteries-included pipeline framework instead,
Verdict generatedVerified 5h ago · liveness 70/100 · cite: rightaichoice.com/tools/cookiecutter-data-science
- Python data scientists starting a new project who want a sane default layout
- Small teams standardizing project structure across repos
- Educators teaching reproducible data science organization
- Open-source projects wanting a recognized community directory convention
- Non-Python stacks such as R or Julia
- Anyone who needs a GUI or point-and-click project creator
- Projects expecting built-in orchestration, scheduling or experiment tracking
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Skip Cookiecutter Data Science if you need a GUI project wizard, work in R or Julia, or want orchestration and experiment tracking built into the scaffold rather than added later with DVC or Kedro.
CCDS costs nothing to use: it is an open-source template from DrivenData, so the only spend is the time your team puts into answering the wizard and pruning folders you don't need. Compare it against paid frameworks such as Kedro-based managed tooling or an MLOps suite, where the licence line item covers orchestration you would otherwise assemble yourself.
In short
Cookiecutter Data Science — Cookiecutter Data Science (CCDS) scaffolds a standardized, reproducible Python data science project with one `ccds` command. Best for Python data scientists starting a new project who want a sane default layout, Small teams standardizing project structure across repos, Educators teaching reproducible data science organization. Free to use.
What is Cookiecutter Data Science?
Cookiecutter Data Science — Cookiecutter Data Science (CCDS) scaffolds a standardized, reproducible Python data science project with one `ccds` command. Best for Python data scientists starting a new project who want a sane default layout, Small teams standardizing project structure across repos, Educators teaching reproducible data science organization. Free to use.
Cookiecutter Data Science is a free, open-source project template from DrivenData that gives Python data science and machine learning work a single, recognizable directory layout. Version 2 replaces the raw cookiecutter invocation with a `ccds` command-line tool: install the cookiecutter-data-science package (pipx recommended, pip also works) and run `ccds` from the parent directory where you want the project. The wizard asks for project name, repo name, module name, author, description and Python version, then walks you through dataset storage (none, azure, s3, or gcs with a bucket and AWS profile prompt), environment manager (virtualenv, conda, pipenv, uv, pixi, poetry, or none), dependency file (requirements.txt, pyproject.toml, environment.yml, Pipfile, pixi.toml), a PyData package bundle (none or basic), testing framework (none, pytest, unittest), linting and formatting (ruff, or flake8+black+isort), license (none, MIT, BSD-3-Clause), docs (mkdocs or none), and whether to include the source code scaffold. What lands on disk is a Makefile with convenience targets like `make data` and `make train`, a data directory split into external, interim, processed and raw, notebooks named by the number, creator's initials, description convention (for example `1.0-jqp-initial-data-exploration`), models, references, reports and figures folders, and, if you opt in, a module with config.py, dataset.py, features.py, modeling/train.py, modeling/predict.py and plots.py. You can pass your own template URL as the first argument to `ccds` to use a different cookiecutter template. It is scaffolding only. CCDS stores nothing for you — you point it at your own S3 bucket, Azure storage or GCS bucket, or none at all — and it runs only in a terminal with no GUI. Teams that need orchestration, scheduling or experiment tracking bring their own (DVC, Kedro, an MLOps suite). Non-Python stacks are out of scope. v2 requires Python 3.10+, and the v1 template remains reachable through plain
Key features
- `ccds` interactive CLI wizard for new project setup
- Standardized directory structure: data, notebooks, models, reports, src, references, docs
- Data folders split into raw, interim, processed and external
- Environment manager selection: virtualenv, conda, pipenv, uv, pixi, poetry, none
- Dependency file options: requirements.txt, pyproject.toml, environment.yml, Pipfile, pixi.toml
- Dataset storage selection for azure, s3 or gcs with bucket prompt, or none
- PyData package bundle option: none or basic
- Testing framework selection: none, pytest, unittest
- Linting and formatting: ruff, or flake8+black+isort
- Docs generation with mkdocs or none
- Open-source license choice: no license file, MIT, or BSD-3-Clause
- Configurable Python version number at setup (v2 requires Python 3.10+)
- Generated Makefile with convenience targets such as `make data` and `make train`
- Optional source code scaffold: config.py, dataset.py, features.py, modeling/train.py, modeling/predict.py, plots.py
- Notebooks naming convention: number, creator's initials, description
Behind the verdict
Starting a new data science project is often a debate about where files should live. Cookiecutter Data Science settles that before you write a line of code. Version 2's `ccds` wizard turns the old cookiecutter prompt-soup into a guided setup that knows the tools Python teams actually use: uv, pixi, poetry, conda or virtualenv; pytest or unittest; ruff or the flake8+black+isort stack; mkdocs for docs; and bucket prompts for S3, Azure or GCS. We'd reach for it whenever a small team wants every repo to share the same data/notebooks/models/reports skeleton. The generated Makefile with `make data` and `make train` targets, plus the numbered notebook naming convention (`1.0-jqp-initial-data-exploration`), gives newcomers a map. The optional source scaffold — config.py, dataset.py, features.py, modeling/train.py and predict.py, plots.py — is a gentle nudge toward separating data ingestion, feature building and training instead of one giant notebook. Where it bites: CCDS is a starting point, not a runtime. It stores no data, schedules nothing, tracks no experiments and provides no UI. If your project needs a pipeline DAG, a data catalog or a scheduler, you're layering something like Kedro or DVC on top. Non-Python shops (R, Julia) are out of scope entirely. The closest alternative is Kedro, which is a full pipeline framework rather than a folder template. CCDS is lighter and less opinionated about how you build pipelines; Kedro gives you the whole engine. For a quick, no-cost way to stop re-litigating project layout, CCDS is hard to beat.
Limitations
- CCDS v2 requires Python 3.10+ and installs the cookiecutter-data-science package, which adds the `ccds` command on top of cookiecutter.
- It is a scaffolding utility, not a hosted service: it stores nothing for you, so you supply your own Azure, S3 or GCS bucket and bring your own data tooling.
- The template targets Python only, setup is terminal-only with no GUI, and the generated conventions are opinionated enough that teams with established layouts will modify or delete much of the structure.
- Nothing in the output handles orchestration, scheduling, experiment tracking or model registry.
- The docs list conda installation as coming soon, so the conda path is not yet available.
as of 2026-10-26
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Is Cookiecutter Data Science still active?
Cookiecutter Data Science is live · free · last verified 14 Nov 2026 · viability healthy.
Viability score
How well maintained and how widely used is Cookiecutter Data Science? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about Cookiecutter Data Science?
What people actually say about Cookiecutter Data Science — is it worth it?
We scanned public community sources for Cookiecutter Data Science on Sep 22, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 2 of the posts we fetched could be positively tied to Cookiecutter Data Science. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Cookiecutter Data Science — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 14 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Cookiecutter Data Science 11 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing tiers, our verdict, integrations, description, summary
- — changed switching in, setup time, switching out, summary, who should skip it, our verdict, integrations, use cases, limitations, who it suits, pricing tiers, features, description, pricing fit
- — changed summary, integrations, pricing tiers, our verdict, features, description
- — changed limitations, pricing page
- — changed SSL certificate
- — changed use cases, pricing tiers, description, summary, our verdict, who it suits, features, who should skip it, hidden costs, limitations, setup time, switching in, switching out, pricing fit
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Cookiecutter Data Science cost in 2026?
Cookiecutter Data Science: Free to use. Pricing last verified 14 Nov 2026.
Plans compared
Every published Cookiecutter Data Science tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | Any Python data scientist or small team starting a new project who wants a recognizable layout without a licence line itemAdds: Starting tier — the whole template: `ccds` wizard, directory structure, Makefile targets, env/dependency/testing/lint/docs choices, and custom template URLs |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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 costs nothing to use: it is an open-source template from DrivenData, so the only spend is the time your team puts into answering the wizard and pruning folders you don't need. Compare it against paid frameworks such as Kedro-based managed tooling or an MLOps suite, where the licence line item covers orchestration you would otherwise assemble yourself.
Has Cookiecutter Data Science's pricing changed?
No plan price for Cookiecutter Data Science has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Cookiecutter Data Science?
Pick Cookiecutter Data Science if you are Python data scientists starting a new project who want a sane default layout, Small teams standardizing project structure across repos, Educators teaching reproducible data science organization, Open-source projects wanting a recognized community directory convention.
Look elsewhere if you are Non-Python stacks such as R or Julia, Anyone who needs a GUI or point-and-click project creator, Projects expecting built-in orchestration, scheduling or experiment tracking, Ad-hoc single-notebook analyses where the full scaffold is overhead.
Skip it if Skip Cookiecutter Data Science if you need a GUI project wizard, work in R or Julia, or want orchestration and experiment tracking built into the scaffold rather than added later with DVC or Kedro.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Cookiecutter Data Science actually fits — and what changes day-one when you adopt it.
You pipx install cookiecutter-data-science, run `ccds` from the parent directory, pick uv as the environment manager, pyproject.toml as the dependency file, pytest and ruff, and point dataset storage at your s3 bucket.
Outcome: You get a repo with raw/interim/processed/external data folders, a Makefile with `make data` and `make train`, and a linting setup already wired, so day one is spent on data rather than on scaffolding.
You settle on one set of wizard answers (conda, environment.yml, flake8+black+isort, mkdocs, include the code scaffold) and have each team member run the same `ccds` flow for new work.
Outcome: Every new repo shares the same folder names, notebook naming convention and Makefile targets, which shortens code review and onboarding.
Students install the package with pipx, run `ccds`, and walk through the numbered notebook convention and the data/ split before writing any analysis code.
Outcome: The lesson lands on project organization using a layout students will meet again in industry repos, without any paid tooling.
Use cases
- Scaffolding a new Python data science repo with a layout reviewers already recognize
- Choosing an environment manager and dependency file format up front instead of retrofitting one later
- Wiring S3, Azure or GCS dataset storage paths into a project from the first commit
- Adopting pytest and ruff (or flake8+black+isort) conventions before the first pull request
- Generating an mkdocs docs folder alongside the code
- Standardizing folder names across several team repos so every project looks the same
- Moving from scattered notebooks to an importable package with config, dataset and modeling modules
- Teaching reproducible project organization in a course or onboarding session
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.
Single install plus wizard: pipx install cookiecutter-data-science, then `ccds` and roughly a dozen prompts (names, Python version, storage, environment manager, dependency file, testing, linting, license, docs, scaffold) — under ten minutes to a working repo for a solo data scientist. A team lead adding a shared answer set and a short README for colleagues should budget under an hour.
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 v1 template: keep using it via `pip install cookiecutter` then `cookiecutter https://github.com/drivendataorg/cookiecutter-data-science -c v1`, or start fresh with `ccds` and copy your code into the new layout
- →From a hand-rolled repo: run `ccds` and move existing notebooks into the numbered convention and data files into raw or processed
- →From a copied colleague's repo: recreate it through the wizard so the environment manager, dependency file and testing choices are explicit rather than inherited
- ↗To Kedro: map data/raw and data/processed onto Kedro's data catalog and move src modules into pipeline nodes
- ↗To a plain package: keep the generated module and drop the Makefile and docs folders if you only wanted the importable code
- ↗To v1: run the v1 cookiecutter command pinned with `-c v1` if you need the older template output
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Cookiecutter Data Science”, and we withheld 5: 5 did not mention Cookiecutter Data Science. Showing the 1 we can prove is about Cookiecutter Data Science.
Official links
What are the best Cookiecutter Data Science alternatives?
The closest alternatives to Cookiecutter Data Science we list are Deepnote, DataLab and Livedocs. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Deepnote | Freemium | Pricing not published | — |
| DataLab | Freemium | Pricing not published | — |
| Livedocs | Freemium | Pricing not published | — |
Alternatives to Cookiecutter Data Science
View all
Tools that pair well with Cookiecutter Data Science
Common stack mates teams adopt alongside Cookiecutter Data Science, with the specific reason each pairing earns its keep.
Deepnote
Deepnote is a collaborative cloud notebook for Python and SQL where humans and AI agents build analyses, dashboards, and data apps together.
DataLab
DataLab turns plain-English questions into runnable SQL, Python and R inside an AI data notebook you can review and share.
Livedocs
Livedocs is an AI data notebook where reactive cells re-run themselves and plain-English prompts write the SQL and Python.
Cookiecutter Data Science vs its closest rival
Cookiecutter Data Science's closest head-to-head is with Geologicai.
Our Cookiecutter Data Science vs Geologicai verdict written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Head-to-head comparisons
Frequently asked questions
Categories
Best-of guides
For makers of Cookiecutter Data Science
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