Eugene Yan • 7/19/2020

Why You Need to Follow Up After Your Data Science Project

This article discusses why data scientists must follow up after completing a project, highlighting problems like dead-end code, incorrect pipeline sequences, and poor git version control for Jupyter notebooks. It details the challenges of collaborating on notebooks and suggests solutions like converting notebooks to .py files or using tools like nbdime and jupytext. The article emphasizes the benefits of documentation and sharing work for productivity and reproducibility.

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#git #version control #productivity