One of the biggest long-term productivity boosts for data science teams is the use of source control. For those with experience in software engineering, it is a no-brainer, but I still see data science teams using shared folders or email to collaborate on code.
Using source control is crucial for managing data science code because it allows developers to track changes made to the codebase over time, collaborate with other team members, and maintain a history of their work. With source control, data scientists can easily revert to previous versions of their code, track different experiments, identify when and why changes were made, and resolve conflicts that may arise when working in a team setting.
Here are the top 5 reasons why data scientists should use source control:
-
Version Control: Source control systems keep track of every change made to the codebase, providing a complete history of the development process. Additionally, it can be used as a backup solution, in case the code is lost locally.
-
Collaboration: Source control allows multiple developers to work on the same codebase simultaneously, making it easier to collaborate and share knowledge. Through the pull request system it allows for accountability, quality control and transparency within a team.
-
Experimentation: With source control, data scientists can experiment with new approaches without fear of losing their existing work. They can create branches to work on new features, and merge changes back into the main codebase when ready.
-
Integraion with CI/CD pipeline. This allows automatic code checks and when passed pushing container with the code to the container repository, ready for deployment.
This article reflects my personal views and opinions only, which may be different from the companies and employers that I am associated with.