Documenting code, one commit at a time.
When working on the data-science-portfolio project, I found that direct access to data sources from analytical logic was creating a tangled mess of dependencies. Every time the underlying data storage shifted, I had to rewrite chunks of analysis code. This is a common pain point: your business logic becomes tightly coupled to the persistence layer, making the project fragile
In my recent work on the data-science-portfolio project, I focused on improving how data assets are organized and retrieved. As the repository grows, managing file access and data ingestion directly within analysis scripts can quickly lead to tightly coupled, fragile code.
Onboarding new developers to a data-heavy project often hits a wall before they even write their first line of code: 'How do I actually get the dataset locally?' In the pipeline-deteccion-fraudes project, we realized that vague instructions were causing unnecessary friction for contributors trying to replicate our fraud detection environments.
