Housekeeping in Data Science

Maintaining a data science portfolio is more than just stacking Jupyter notebooks. As we explore new modeling techniques and datasets, our repositories can quickly become cluttered with outdated experiments or obsolete research code. Recently, I performed a audit of my data-science-portfolio to streamline its structure.

The Challenge

Over time, I accumulated

The data-science-portfolio project focuses on organizing and streamlining analytical workflows. As a repository for various data experiments, maintaining clean and reproducible code is essential. One of the most effective ways to ensure this is by adopting a robust pipeline pattern to structure data processing steps.

The Problem with Linear Scripts

When working in Jupyter notebooks, it is