# drivendata/cookiecutter-data-science

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9,919 stars · 2,640 forks · Python · MIT

## Links

- GitHub: https://github.com/drivendata/cookiecutter-data-science
- Homepage: https://cookiecutter-data-science.drivendata.org/
- awesome-repositories: https://awesome-repositories.com/repository/drivendata-cookiecutter-data-science.md

## Description

This project is a Cookiecutter scaffold and data science project template that automates the creation of a consistent folder hierarchy for analytical projects. It provides a standardized directory structure and project skeleton designed to organize data science workflows to support reproducibility and collaboration.

The framework establishes a standardized data pipeline layout and a reproducible research framework. It separates data ingestion, cleaning, and modeling stages to ensure a clear audit trail of transformations and consistent research results.

The project further organizes work by providing a convention-based directory hierarchy for raw data, processed datasets, notebooks, and source code. This approach supports collaborative machine learning and structured data pipeline organization.

## Tags

### Development Tools & Productivity

- [Template-Based Project Scaffolders](https://awesome-repositories.com/f/development-tools-productivity/project-templates/template-based-project-scaffolders.md) — Generates a predefined directory hierarchy by substituting user-defined variables into boilerplate files.
- [Data Science Project Templates](https://awesome-repositories.com/f/development-tools-productivity/data-science-project-templates.md) — Creates a standardized directory structure and template to bootstrap reproducible data science projects. ([source](https://github.com/drivendata/cookiecutter-data-science#readme))
- [Pipeline Directory Structuring](https://awesome-repositories.com/f/development-tools-productivity/data-transformation-pipelines/pipeline-directory-structuring.md) — Organizes raw data and processed outputs into a logical hierarchy to maintain a clear audit trail of transformations.
- [Cookiecutter Templates](https://awesome-repositories.com/f/development-tools-productivity/project-scaffolding-config-code-generation/project-scaffolding-configuration/project-scaffolding/cookiecutter-templates.md) — Provides a template for the Cookiecutter CLI to automate the creation of consistent folder hierarchies.

### Artificial Intelligence & ML

- [Workflow Reproducibility](https://awesome-repositories.com/f/artificial-intelligence-ml/research-papers/research-reproductions/workflow-reproducibility.md) — Creates a consistent project layout that allows researchers to easily replicate results and understand the data pipeline.
- [Collaborative Machine Learning Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/collaborative-machine-learning-workflows.md) — Establishes a shared directory convention so multiple team members can contribute to the same codebase without confusion.

### Part of an Awesome List

- [Project Organization Standards](https://awesome-repositories.com/f/awesome-lists/devtools/javascript-standards-and-style/project-organization-standards.md) — Sets up a standardized folder structure to keep datasets, notebooks, and source code organized throughout research.
- [Reproducible Research](https://awesome-repositories.com/f/awesome-lists/productivity/reproducible-research.md) — Implements a structured layout for organizing raw data, processed datasets, and source code for reproducibility.
- [Project Templates](https://awesome-repositories.com/f/awesome-lists/devtools/project-templates.md) — Logical and reproducible project directory structure for data science.

### Data & Databases

- [Data Pipeline Layouts](https://awesome-repositories.com/f/data-databases/data-pipeline-layouts.md) — Provides a directory convention for separating data ingestion, cleaning, and modeling stages.

### Software Engineering & Architecture

- [Directory-Based Organization](https://awesome-repositories.com/f/software-engineering-architecture/directory-based-organization.md) — Provides a convention-based directory hierarchy to organize data, notebooks, and source code for reproducibility.
- [Project Structure Standardization](https://awesome-repositories.com/f/software-engineering-architecture/project-structure-standardization.md) — Ensures a consistent project layout across different research environments to improve collaboration. ([source](https://github.com/drivendata/cookiecutter-data-science#readme))
- [Template Variable Substitutions](https://awesome-repositories.com/f/software-engineering-architecture/application-lifecycle-management/configuration-management/environment-variable-management/environment-variable-injection/environment-variable-substitution/configuration-variable-substitutions/template-variable-substitutions.md) — Maps user inputs to specific placeholders throughout the generated folder structure via configuration.

### Programming Languages & Runtimes

- [Jinja2 Template Renderers](https://awesome-repositories.com/f/programming-languages-runtimes/jinja2-template-renderers.md) — Uses Jinja2 template rendering to dynamically inject project names and metadata during initialization.