How to Structure a Data Science Project for Maintainability

How to Structure a Data Science Project for Maintainability

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.But what kind of standard should you follow? Wouldn’t it be convenient if you had a template to create an ideal structure for your data science project?

Get Started

To download the template, start with installing Cookiecutter:

pip install cookiecutter

Bash

Create a project based on the template:

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

The tools used in this template are:

Poetry: A tool that manages Python dependencies

hydra: A tool that manages configuration files

pre-commit plugins: A tool that automates code reviewing and formatting

pdoc: A tool that automatically creates API documentation for your project

In the next few sections, we will learn the functionalities of these tools.

Install Dependencies

Poetry is a Python dependency management tool and is an alternative to pip. Poetry allows you to:

Store the flexible versions of packages in the “pyproject.toml” file, ensuring that your project can adapt to newer releases.

Store the exact version numbers of each package and its dependencies in the “poetry.lock” file, ensuring the reproducibility of dependencies.

Efficiently removes packages and their associated dependencies.

Efficiently resolve dependencies and addresses any conflicts promptly.

Package your project in several lines of code.