I'd also argue that it's beneficial to have a decent understanding of R when working with data, no matter what tool you favour. It's widely used, and so interesting things are written with it, and you're cutting yourself off from knowledge if you don't have at least a basic understanding.
1. Are you interested in
- analytics-type data science where you're focused more on product/business analytics, decision making, A/B tests etc.
or
- applied machine learning kind of data science?
2. Have you taken undergraduate-level calculus (single and multivariable), linear algebra, probability theory or any statistics?
3. Are you already a software developer and if yes, what do you work on?
4. The books that you found challenging - which books and what was challenging about them?