Pretty well everything we do in data science, machine learning and deep learning has to do with the manipulation of matrices. Linear algebra is everywhere, Ok there will also be a bit of calculus too but you will find linear algebra all over the shop.
If you don’t take a peek behind the veil the functions you call in python or R will have an element of magic to them. Don’t be afraid to check out the source code, or even better yet it pays to have some idea of what’s happening under the hood. At least at an intuitive level.
I think you would like to be able to do some simple examples matrix operations with pen and paper, even better is some simple code in Octave or even python or R for a toy dataset. This will help immensely. Armed with a bit of knowledge of logs, exponentials and some calculus you should be just about right for anything that gets thrown your way.
Here are some resources I have found pretty handy:
1. If you are less comfortable head to Khan Academy and check out this series:
2. If you are more comfortable head to MIT and look at Gilbert Strang’s brilliant lectures:
3. Now, this is a new one, and you know it is going to be good because it is by Rachel Thomas and Jeremy Howard - haven’t looked at it yet, but you can bet this Saturday evening is going to be wild! Looks to be really practical!
The beautiful thing about linear algebra is that every programming language will have a linear algebra library of some type for you to play around with, but octave/ matlab will feel closer to the math of linear algebra.