---
_db_id: 86
content_type: topic
ready: true
title: Clean Code for Data Science
---

Code is read more often than it is written. Notebooks, especially, are made to be looked at. Assist yourself and others by keeping your code and notebooks neat!

## Naming conventions

Naming conventions and other good python practices are listed in the [PEP 8 Style Guide](https://www.python.org/dev/peps/pep-0008/).

Also see these [code quality best practices](https://github.com/Umuzi-org/code-quality-best-practices).

## The Data Science Workflow

For Data Science workflow best practices, please familiarise yourself with the [cookiecutter data science project](https://drivendata.github.io/cookiecutter-data-science/#why-use-this-project-structure)
and read this description of how to organise your projects using atom and Jupyter on [Medium](https://medium.com/@rrfd/cookiecutter-data-science-organize-your-projects-atom-and-jupyter-2be7862f487e)