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Start with a flexible project structure
The Cookiecutter Data Science project describes its approach as “a logical, reasonably standardized but flexible project structure for doing and sharing data science work.” Its current layout is a useful starting point—not a rule every project must follow. See the official project structure documentation for its conventions.
project/
├── README.md
├── pyproject.toml # or another dependency/configuration choice
├── data/
│ ├── raw/ # original inputs; preserve where possible
│ ├── interim/ # intermediate transformations
│ ├── processed/ # analysis/model-ready outputs
│ └── external/ # third-party datasets, if used
├── notebooks/ # exploration and analysis narrative
├── references/ # data dictionary, sources, and context
├── reports/
│ └── figures/
├── models/ # saved models, if the project creates them
├── src/ # reusable code, organized by task/domain
└── tests/ # add when usefulThis is an adaptable synthesis of the template, not a claim that every folder is required. In Cookiecutter Data Science v2, the source-code directory uses the selected module name, and optional paths depend on setup choices. The repository lists Python 3.10 or later as a requirement; check its current v2 documentation if you use the template, since version-specific options can change.
1. Define the outcome and audience
Before creating folders, write a short opening for the README. State the problem, who will use the result, what the project will produce, and how you will judge success. A clear audience and outcome help determine whether the deliverable should be a notebook, report, reusable package, model, or deployed workflow.
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This is also a project-planning task, not merely documentation. A 2022 survey by Iñigo Martinez, Elisabeth Viles, and Igor G. Olaizola covered 237 data science professionals. In that survey, 25% of participants said they followed a data science project methodology. The study identified precise descriptions of stakeholder needs, communication of results to end-users, and team collaboration and coordination as its top three success factors. These findings describe the surveyed participants; they do not guarantee outcomes for every project. Read the survey study.
2. Create a repository and commit its baseline
Choose a project name and, if you are creating a Python package or using Cookiecutter Data Science, a valid module name. Create the initial structure, initialize Git, and commit the baseline before substantial analysis begins. The initial commit gives collaborators a shared starting point and makes later changes easier to review.
For team projects, push the repository to a shared remote and use branches and pull requests if they fit your team’s workflow. The template’s usage guide recommends initializing Git, committing the structure, and pushing to a shared repository when collaborating.
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3. Choose and document the environment
Use a project-specific environment rather than relying on whatever happens to be installed on one computer. Record the dependencies and setup steps in the configuration or dependency files appropriate to your language and tooling. Then test the instructions from a clean environment so a colleague can follow them.
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Cookiecutter Data Science v2 offers configuration choices for environment management, dependency files, testing, linting and formatting, and documentation. The exact choices depend on how you configure the template. Its guide describes creating and activating an environment before installing requirements; document the equivalent sequence for your project rather than assuming contributors know it.
Keep database credentials out of tracked files. The template guide suggests storing credentials in a local .env file. Ensure that file is excluded from version control, and document the required variable names without publishing secret values.
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4. Decide how data enters and moves through the project
Organize data by state and purpose so it is clear which files are original inputs and which were produced by processing. Where feasible, preserve raw inputs and create separate intermediate and processed outputs. This makes transformations easier to understand and reduces the risk of accidentally treating a derived file as source data.
- Static files: Put original inputs in
data/raw/when that fits the project. Keep transformations in separate locations such asdata/interim/anddata/processed/. - Recurring downloads: Use a script to retrieve changing files, and avoid overwriting the original raw data. Record how and when data was obtained where that information matters for reproducing the analysis.
- Database or remote data: Keep credentials outside version control and document the extraction logic, including the inputs another user needs to reproduce it.
- Third-party datasets: Use a separate location such as
data/external/if it helps distinguish them from project-generated files.
There is no universal data-management layout: the right choices depend on whether data is local, repeatedly downloaded, database-backed, or remote. Avoid committing large or sensitive datasets simply because a data/ folder exists; decide how those inputs will be accessed and documented.
5. Use notebooks for exploration
Put exploratory analyses in notebooks/. A notebook can combine code, results, and explanatory text, making it a useful record of questions asked and decisions made. Give notebooks clear names and add brief narrative context so a reader can tell what each analysis does.
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The Cookiecutter guide illustrates phase-based notebook naming, but explicitly allows teams to choose their own convention. Prioritize names that make the work’s purpose or sequence clear rather than adopting a naming scheme for its own sake.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Move repeatable logic into source modules
When code becomes stable or is needed from multiple notebooks and scripts, move it into importable source modules. Depending on the project, reusable functions might handle data loading, feature creation, training, prediction, or visualization. This avoids copying the same implementation into multiple notebooks and gives the shared logic one place to maintain.
Keep exploratory decisions and narrative in notebooks when they help explain the analysis; put repeatable operations and reusable logic in source code. The boundary can evolve as a project matures. The goal is clarity and reuse, not turning every quick investigation into a package.
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7. Make results, references, and run instructions easy to find
Put generated analysis and figures in a discoverable output location, such as reports/ and reports/figures/. Keep useful context—such as data dictionaries, source notes, and project references—in references/ or another clearly documented location.
In the README, provide a concise run path: how to set up the environment, obtain or locate inputs, run the analysis, and find its outputs. A Makefile or another task runner is optional. Add one when it makes common tasks clearer or less error-prone; do not add it just to make the tree look more complete.
8. Review the project as it grows
Use commits to record meaningful changes. For collaborative work, review changes before merging when practical. Add tests or other checks in proportion to the project’s risk and intended reuse. A one-off exploration may need fewer automated checks than a recurring workflow or code used by others.
Data science code can run without errors and still produce an incorrect result. Code review can help catch mistakes that a successful execution alone will not reveal. Treat tests, review, and documentation as tools for confidence—not guarantees that an analysis is correct.
How to adapt the structure
Choose the smallest layout that makes the project understandable and reproducible, then add structure as real needs appear. Consider these factors before adding folders or tooling:
- Scale and lifespan: A short-lived, one-off analysis may need only a README, data organization, and a notebook. Maintained or reused work benefits more from modules, tests, and explicit setup instructions.
- Data access: Static local files, recurring downloads, database extracts, and remote data have different storage and documentation needs.
- Reproducibility: If someone else must recreate the result, document the environment, inputs, and steps that produce the outputs.
- Collaboration: Shared work needs a repository and practices that make changes understandable and reviewable.
- Deliverable: A report, notebook, reusable package, model artifact, or deployed workflow may call for different output and source-code organization.
For a broader perspective on workflow choices, Sara Stoudt, Valeri N. Vasquez, and Ciera C. Martinez write in “Principles for data analysis workflows” that their guidance is not intended as a strict rulebook, but as support for reproducible, sound data-intensive analysis.
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