People ask me about getting started with data science all the time. So I came up with three paths for self study: Easy, Medium and Hard.

Continue reading to hear about each path and see my book recommendations. https://abs.twimg.com/emoji/v2/... draggable="false" alt="🧵" title="Thread" aria-label="Emoji: Thread">https://abs.twimg.com/emoji/v2/... draggable="false" alt="👇" title="Rückhand Zeigefinger nach unten" aria-label="Emoji: Rückhand Zeigefinger nach unten">

EASY PATH. On this path, you will get lots of practical skills but only an intuitive sense of the theory. I think the easiest way to start is to read a good data science book and get your hands dirty. I like to recommend "R for Data Science" for that.

MEDIUM PATH. This path gives you some theory with an eye toward applications. A lot of books at this difficulty level suck. The formatting and writing are often bad and the authors are clearly phoning it in. I like "Think Stats" because it& #39;s not like that.

HARD PATH. This is the hardest but the most complete path. First, you should master at least one college class worth of material in:
- Calculus
- Linear Algebra
- Probability Theory
- A statistical programming language like R

Here are a few books I like

Next I would try one of these books to get a solid foundation in statistics. Both are challenging! (I like Casella & Berger personally.)

I& #39;m sure there are many other ways of learning stats on your own so please don& #39;t feel offended if none of this reflects your experiences. I& #39;m partly writing this as a reference for people who want to hear my personal take.