Data scientists usually have to write code to prototype software, be it to preprocess and clean data, engineer features, build a model, or deploy a codebase into a production environment or other use case. The evolution of a codebase is important for a number of reasons which is where version control can help, such as:collaborating with other code developers (due diligence in coordination and delegation)generating backupsrecording versionstracking changesexperimenting and testingand working with agility.In this bite episode of the DataCafé we talk about these motivators for version control and how it can strengthen your code development and teamwork in building a data science model, pipeline or product.Further reading:Version control via Wikipedia https://en.wikipedia.org/wiki/Version_control git-scm via https://git-scm.com/"Version Control & Git" by Jason Byrne via Slideshare https://www.slideshare.net/JasonByrne6/version-control-git-86928367"Learn git" via codecademy https://www.codecademy.com/learn/learn-git"Become a git guru" via Atlassian https://www.atlassian.com/git/tutorialsGitflow workflow via Atlassian https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow"A successful git branching model" by Vincent Dressian https://nvie.com/posts/a-successful-git-branching-model/ Branching strategies via GitVersion https://gitversion.net/docs/learn/branching-strategies/Recording date: 21 April 2022Send us a textThanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

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