Course Preparation - PhD in Statistics

[statprospect]

Hi Everyone,

I am planning on beginning a PhD program in Statistics next fall, and am trying to figure out which courses to take next semester in preparation. I am trying to decide between the following two classes and would really appreciate any insights any of you might have.

Graph Theory: A Combinatorial View - An introductory course in graph theory with emphasis on its combinatorial aspects. Basic definitions, and some fundamental topics in graph theory and its applications. Topics include trees and forests graph coloring, connectivity, matching theory and others.

Complex Variables - Fundamental properties of the complex numbers, differentiability, Cauchy-Riemann equations. Cauchy integral theorem. Taylor and Laurent series, poles, and essential singularities. Residue theorem and conformal mapping.

I am also taking Fourier Analysis, a doctoral class on statistical computing (optimization, sampling, etc.), and a class on analysis and optimization.

Thanks!

Edited December 4, 2011 by statprospect

[Agradatudent]

Complex Variables, You will run into the i in statistics.

[cyberwulf]

It doesn't matter. These are both pretty far removed from what you will need/see in classes at even the top stat programs. Yes, it's good to know what "i" is, but only the most theoretical statisticians (who should more properly be called probabilists) have more than a passing familiarity with complex variables. I might even lean towards the graph theory course: one current "hot" area of statistics is social network analysis, which might use some of the concepts you'd be introduced to in that class.

Bottom line: Take what you find most interesting!

[dendrogirl]

I agree with cyberwulf.

I might suggest the complex variables class if you need some extra practice with calculus.

[statprospect]

Thanks for the advice! I guess graph theory it is.