Reduce the incidental issues in your programming environment so you can focus on the important data science problems.
Consider the following situation: you’re trying to practice your soccer skills, but each time you take to the field, you encounter some problems: your shoes are on the wrong feet, the laces aren’t tied correctly, your socks are too short, your shorts are too long, and the ball is the wrong size. This is a ridiculous situation, but it’s analogous to that many data scientists find themselves in due to a few common, easily solvable issues:
- Failure to manage library dependencies
- Inconsistent code style
- Inconsistent naming conventions
- Different development environments across a team
- Not using an integrated development environment for code editing
All of these mistakes “trip” you up, costing you time and valuable mental resources worrying about small details. Instead of solving data science problems, you find yourself struggling with incidental difficulties trying to set up your environment or get your code to run. Fortunately, the above issues are simple to fix with the right tooling and approach. In this article, we’ll look at best practices for a data science programming environment that will give you more time and concentration for working on the problems that matter.