Documenting code, one commit at a time.
When working on complex projects like perovskitas-para-celdas-solares, it is easy to focus exclusively on logic and data pipelines. However, maintainability relies just as heavily on how well the codebase explains itself to the next developer. Recently, I focused on refining the project's documentation, shifting from a collection of notes to a structured
Building a robust fraud detection system requires balancing real-time data ingestion with complex analytical processing. In the project pipeline-deteccion-fraudes, we have recently implemented the first version of a modular pipeline designed to handle incoming transaction streams and apply intelligent filtering logic.
At the core of this system is the
Modern data science workflows often suffer from "notebook sprawl," where fragmented code makes experiment reproducibility nearly impossible. In the telecom-x-alura-latam-segunda-parte project, we focused on refactoring our churn analysis into a structured pipeline.
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
