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Depending on the role, data science is generally either [data analysis (with very little modelling) + business understanding + communication and presentation skills], OR it's [statistics + software development]. There can be some deviation and mixing between the two, but to help with the latter:

- linear algebra

- calculus

- software development - best practices, version control, design patterns etc.

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Can you elaborate on the third point with any resources for Best practices and Design patterns? I looked into Amazon and came across a few books for both. Many people have commented that Data Science should move towards Software best practices, etc. As I am a statistics major, This is a gap that I would have to bridge. Thanks.

Look up The Pragmatic Programmer and Clean Code - they're decent books and I'm sure other people on HN have even better recommendations.

Also, you don't necessarily need to cover something completely in order to get started. And these will be useful mostly if your work is in some way part of a software product instead of being some "offline" analysis, model build or forecast. Just learning to use version control, to collaborate with other developers and to run tests goes a long way.

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