This is kind of broad. Narrow it down a bit:
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?
ISLR from Hastie and Co is a great intro to Data Science. Math is not too daunting and the examples are very nice. The authors have generously provided a free version of the book for download.
R for Data Science [1] is well-worth reading. Even if you're not planning on writing much R in the future, the book is a solid introduction to principles & practices, starting from an accessible level.
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] https://r4ds.had.co.nz/