Data Science is tough to break in. It is a multi-disciplinary field, mostly revolving around math, stats, and programming. If you are working in some other field, and want to switch, or if you’re picking a major and want to revisit the subjects you were intoxicated by in the prior years, this is the article for you.
Okay, you know there will be math. And it might sound intimidating, but behind those fancy terms like eigendecomposition and partial derivatives lie understandable topics — that is, if you invest enough time. I will share with you the list of resources I used when starting, and resources I still use daily. They aren’t sorted in any particular order, so feel free to pick one that best suits your time frame and learning style. Okay, ready to start? Here we go.
Author: Dario Radečić
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