Hi all, I was wondering what math classes will capture an ad com's attention and make them think "wow, this kid has what it takes to handle the vigorous math required for the first year."
I've been on this forum for a few months and the consensus seems to be that calc sequence, LA/DE, prob n stats, and RA are pretty much the bare minimum necessary but not sufficient, to get into a top PhD program. I'm now going to throw out some classes and hope someone that has been through the grinder will share some wisdom on which classes should an UG like myself should take (both from the mathematical loftiness and grad school utilization perspectives).
-Mathematical modeling (optimization, Newton/Lagrange methods and Kuhn-Tucker)
-Nonparametric statistics (Kolmogorov-Smirnov type stuff)
-Time series analysis (Box-Jenkins autoregressive models)
-Nonlinear dynamics and chaos
-Graph theory (trees, Euler paths and Hamiltonian circuits)
-Complex variables (complex numbers, harmonic functions, doing calc with these)
-Multivariate statistical analysis (canonical correlation, clustering, discriminate/factor analysis, multidimensional scaling, component analysis)
-Stochastic processes (Markov chains, branching processes, Poisson process)
-Statistical decision theory (Bayesian)
-Probability and Mathematical statistics I & II
-Topology
-Fourier analysis (DE 2.0)
-Analysis and dynamics of DE's (dynamics of linear and nonlinear ODE's)
-Numerical analysis I & II (Euler, Runge-Kutta, Picard, Newton, ODE's and PDE's, lots of mathlab programming)
Btw I'm a rising senior in a mediocre state school but I'm planning on probably staying an extra year to pick up the math major, in the fall I will be taking adv metrics, regional econ, mathematical econ, discrete math(proofs), calc 3, and mathematical modeling(optimization) as well as doing TA for an econ prof that I am close to, with a little bit of RA on the side. Thank you for your time. ;)