As a CS student, I'd really make sure your stats knowledge is solid. Perhaps take a class that covers stochastic finance (Black-Scholes, etc.) if available.

I learned the hard way that it is quite difficult to break into finance as a non-student, so do everything you can now to land that first gig. Good luck!

Some starting resources:

-Ernie Chan's books and blog (https://epchan.blogspot.com/)

-QuantStart has great starter material and a new book, although I haven't read it (https://www.quantstart.com/)

-"Inside the Black Box" (Narang) I've seen referenced a good bit but felt as though it leaned toward order execution and rather boring

-"Dark Pools" (Scott Patterson) a great story about the rise of algorithmic trading

-"Flash Boys' (Michael Lewis) offers a nice follow up (HFT), but considered a bit sensationalist

EDIT: If you're planning on using Python (a solid bet)...

-Python for Data Analysis (Wes McKinney) - Great, quick book for Pandas by former AQR (and now Two Sigma?) guy.

-Yves Hilpisch books: "Python for Finance" is introductory while "Derivative Analytics in Python" is quite math heavy.

I went to the Quantopian conference for their basic training on algorithmic trading. This blog post was pretty much what they covered, intro to pandas and a simple strategy. There is a lot of educational material on their site too (which is what you ended up getting in the paid training).

My biggest thing with the Python for Finance books - I know Python, I want to learn finance. All these books are the inverse of that, for people who know finance and want to learn Python. There is a good site for quantitative economics [1] that has tutorials in Python and Julia. I would love a mathematics of finance book that had the examples in Python.

1. https://lectures.quantecon.org/py/