Thanks, Mom! :D

For Python you may use this book.
Check this out : http://www.kevinsheppard.com/images/0/09/Python_introduction.pdf

Hi Gobinath!

Yes, I think Linear Algebra is pretty necessary (an understanding of transforming matrices), as well as a solid footing in Calculus, and a strong Statistics background.

The answer to your question is dependent on what area of Data Science you plan to focus on. The most advanced math I have done was in my Machine Learning and Optimization classes, where we were learning the calculus behind statistical distributions, and setting up matrices full of partial derivatives. However, I don’t plan to be advancing the state of the art of ML or Optimization algorithms, so I’m not so worried that I struggled with both of those.

I have done well in my Statistics classes, and have bought some books to solidify my understanding of Bayesian statistics. Most of the advanced math I’ve learned has been in graduate classes, but if you already have the calculus and statistics foundation, you could learn more online. If you are like me and want to understand enough to know what already-built tools and algorithms are doing, but aren’t planning to develop new algorithms, you probably don’t need graduate-level math courses to succeed in the field.

I’m not an expert on this, and just explaining my basic understanding of what is needed, but again, it will vary based on your focus. The book “Doing Data Science” gives an overview of the type of skills you should know, and points at lots of references on where to learn more. Check it out if you haven’t already!

Renee