The SOLID Principles: S and O for Data Science
Applying the Single Responsibility and Open/Closed principles to build robust ML pipelines.
Read more →Documentation: Code That Explains Itself
Why comments lie, why docstrings rule, and how to write clear documentation for your data science projects.
Read more →Logging vs. Printing: Why print() is Not Enough
Upgrade your debugging game. Learn why logging is superior to print statements for production code.
Read more →Defensive Programming: Expect the Unexpected
How to write resilient data pipelines that fail fast and fail gracefully using assertions and error handling.
Read more →Design Patterns for Data Science: Factory & Strategy
Stop writing if/else spaghetti. Use design patterns to make your experiments swappable and scalable.
Read more →Configuration Management: The Art of Separation
Stop hardcoding paths and secrets. Use config files to make your code portable and secure.
Read more →The DRY Principle in Data Science
If you copy-paste code three times, it should be a function.
Read more →Modular Programming for Data Scientists
Break giant scripts into small, single-purpose functions located in separate .py modules.
Read more →PEP 8, Linting, and Code Formatting
Adhere to style guides and use linters to ensure code readability and consistency across the team.
Read more →Type Hinting in Python for Robust Data Pipelines
Use type hints to make code self-documenting and catch errors early.
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