Documenting code, one commit at a time.
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 As we explore new modeling techniques and datasets, our repositories can quickly become cluttered with outdated experiments or obsolete research code.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.
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.
When working in Jupyter notebooks, it is
In the perovskitas-para-celdas-solares project, which focuses on exploring materials for solar energy efficiency, keeping research data organized is a recurring challenge. As datasets grow in size and complexity, maintaining a clean directory structure is essential for reproducibility and team collaboration.
When working with Jupyter notebooks for data
Transitioning from theoretical models to data-driven experimentation requires a robust environment for processing complex material science data. We have initiated the perovskitas-para-celdas-solares project to explore the efficiency parameters of perovskite materials in solar cell applications, leveraging Jupyter as our primary research and development environment.
