Standard and/or advanced curriculum preparing for a grad school in Applied Math I was wondering what would be a list of Applied Math courses (sort of like a road-map for an Applied Math student interested in a Applied Math PhD) which preferably contains absolutely essential courses, heavily suggested courses, and useful/nice miscellaneous ones from either Math or Stats or CS departments?
Also, I was wondering about how much Applied Math grad schools value more pure math undergrad courses as opposed to more applied ones... Supposing I have covered all of my basics such as real analysis, algebra, topology, complex analysis, linear algebra, ode, pde, probability, numerical analysis. Would they prefer me taking Stochastic Processes and, say, Scientific Computing to two graduate Differential Geometry courses or would they not care as much?
Thanks!
 A: I have a masters degree in Applied Math with a bachelors in Actuarial Science, so hopefully I can share some good insights with you. 
To answer your first question; you have to decide which track you're interested in:
1. Do you want to eventually pursue a PhD?
2. Do you want to pursue a career in industry that does not heavily rely on computer science?
3. Do you want to pursue Math in the Computer Science field; may be end-up working as a data scientist?
For question 1: I will advice you to take advanced classes for Numerical Analysis, Statistics, Differential Equations and Topology.
As for 2: Focus heavily on Numerical Analysis, Statistics, Probability, Advanced Calculus and may be if you're interested; Advanced differential equations.
As for 3: Take CS classes such as Design and Analysis of Algorithms, Computation Theory, Operating Systems and Computer Architecture, Advanced Calculus, Numerical Analysis, Statistics and Probability.
I did the courses in 2 and 3. Currently working as an Actuarial Analyst/Data Scientist in Health Care. Hopefully this helped a bit. Good luck!
