Data Science Cookie Cutter

Why?

It is important to structure your data science project based on a certain standard so that your teammates can easily maintain and modify your project.

This repository provides a template that incorporates best practices to create a maintainable and reproducible data science project.

Tools used in this project

• hydra: Manage configuration files - article
• pdoc: Automatically create an API documentation for your project
• pre-commit plugins: Automate code reviewing formatting
• Poetry: Dependency management - article
• uv: Ultra-fast Python package installer and resolver
• pip: Traditional Python package installer

How to use this project

Install Cookiecutter:

pip install cookiecutter

Create a project based on the template:

cookiecutter https://github.com/khuyentran1401/data-science-template

You will be prompted to choose your preferred dependency manager:

• poetry: Modern Python package and dependency manager
• uv: Ultra-fast Python package installer and resolver
• pip: Traditional Python package installer

Book: Production-Ready Data Science

Want to learn more about building production-ready data science projects? Check out my upcoming book:

Production Ready Data Science: From Prototyping to Production with Python

The book will cover:

• Best practices for structuring data science projects
• Tools and techniques for reproducible research
• Deploying and monitoring machine learning models
• And much more!

Sign up now to receive the first 3 chapters for free! You'll also be notified when the full book becomes available.

Other Resources:

• Article
• Video