TO The key ingredient to any successful data science project is high-quality code. From simple data analysis to complicated machine learning processes, code quality is always of utmost importance to ensure accuracy, efficiencyand maintainability of your project. Well-written code ensures that your work can be easily understood, modified, and extended by others, including yourself in the future. It minimizes the chances of errors and makes data and machine learning projects more efficient, effective, and robust. But it's not always easy to write high-quality code, right?
We've all seen low quality code before. And when I say seen, I really mean written!
You know the drill: You're tasked with performing a quick analysis and proof-of-concept modeling exercise. So you dump a set of data into a CSV file, open a notebook, create 42 cryptic cells that scream an error at you if you run them twice. You end up with a spaghetti soup in the form of a notebook, with countless cryptic function names, overwritten variables, indecipherable graphs and, ultimately, a whirlwind of confusion that explodes your brain or the memory of your EC2 instance.
But of course, the awesome POC model you built works pretty well, so where does it end? Production!
Then, God forbid, if something goes wrong, as it always does, a few months later you find yourself looking back at your job, trying to figure out exactly what you did and how it worked in the first place.
Yes, we've all been there, but not anymore!
In this multi-part manifesto, I will guide you through 4 concepts (which coincidentally start with the letter R) to help you create amazing code for your data projects. Hopefully, by creating codebases based on these four Rsyou can safeguard your machine learning pipelines and your sanity alike!
Note: For simplicity, I'm limiting the scope of the article to developing Python code for data projects, but the general concepts should be extensible to others…
