Expanding Programming Capabilities with Better Math

Let me list off a few areas and why you should learn them:

Algebra/Number Theory: There are a number of number useful theoretic algorithms out there and at some point you may need to use/modify an existing one. Knowing the theory behind why they work makes it significantly easier to memorize them (and rederive them if you happen to forget ;)). The classic of such an algorithm would be the Euclidean algorithm for finding the greatest common divisor of two numbers. More advanced algorithms like quadratic sieve, etc have many uses in cryptography.

Graph Theory: I hope I don't need to explain this one too much. Graphs are used to solve a many many many different problems in computer science. They are used to represent networks, relationships between objects, as well as a huge class of ad-hoc uses. Algorithms include shortest path, network flow, coloring, etc.

Linear Algebra: Again, there are many scientific uses of linear algebra. I think I can safely say that the primary use of linear algebra is to solve systems of equations. Complicated differential equations are solved using linear algebra. Knowing how to multiply huge matrices and solve huge systems of linear equations are at the heart of many computer simulations. More closely related to everyday computing, computer graphics (especially 3D graphics) are all based on linear algebra.

Calculus: This should also be self-explanatory. You won't get very far in doing anything related to math and science without knowing calculus. While the bane of college students, it really is everywhere in modern science.

Probability: There are a number of probabalistic algorithms that rely on probability to do their dirty work. Also simulations may also use randomness and probability to achieve good results. It's not terribly hard to learn the fundamentals of probability which is all you really need.

Statistics: Whether you're writing a program which calculates statistics and does statistical tests or you're using statistics yourself, it is an immensely helpful tool to decipher and interpret information. Really anyone, not just mathematicians and scientists, should at least have a basic handle of statistics.

Numerical Analysis: This is kind of a catch-all. This is what ties the math and the computer science together. Math works well and good on paper, but when you start discretizing and bounding real numbers, you will run into problems. This is what numerical analysis is for. It describes the different numerical problems you may face in running these mathematical algorithms as well as mathematical/computer science tricks you can use to avoid/fix them.