Data Science Cookie Cutter for Prefect

Why Should You Use This Template?

This template is the result of my years refining the best way to structure a data science project so that it is reproducible and maintainable.

This template allows you to:

✅ Create a readable structure for your project

✅ Automatically run tests when committing your code

✅ Enforce type hints at runtime

✅ Check issues in your code before committing

✅ Efficiently manage the dependencies in your project

✅ Create short and readable commands for repeatable tasks

✅ Rerun only modified components of a pipeline

✅ Automatically document your code

✅ Observe and automate your code

Tools used in this project

• Poetry: Dependency management - article
• Prefect: Orchestrate and observe your data pipeline - article
• Pydantic: Data validation using Python type annotations - article
• pre-commit plugins: Automate code reviewing formatting - article
• Makefile: Create short and readable commands for repeatable tasks - article
• GitHub Actions: Automate your workflows, making it faster to build, test, and deploy your code - article
• pdoc: Automatically create an API documentation for your project

Project structure

.
├── data            
│   ├── final                       # data after training the model
│   ├── processed                   # data after processing
│   ├── raw                         # raw data
├── docs                            # documentation for your project
├── .flake8                         # configuration for flake8 - a Python formatter tool
├── .gitignore                      # ignore files that cannot commit to Git
├── Makefile                        # store useful commands to set up the environment
├── models                          # store models
├── notebooks                       # store notebooks
├── .pre-commit-config.yaml         # configurations for pre-commit
├── pyproject.toml                  # dependencies for poetry
├── README.md                       # describe your project
├── src                             # store source code
│   ├── __init__.py                 # make src a Python module
│   ├── config.py                   # store configs 
│   ├── process.py                  # process data before training model
│   ├── run_notebook.py             # run notebook
│   └── train_model.py              # train model
└── tests                           # store tests
    ├── __init__.py                 # make tests a Python module 
    ├── test_process.py             # test functions for process.py
    └── test_train_model.py         # test functions for train_model.py

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

Resources

• Article
• Quick start