MS stats profile evaluation

[mima]

Hi all, I'm a third-year student at a lower-tier Ivy (so not Harvard, Yale, or Princeton). I'm also a domestic female, if that means anything.

 

GPA: 3.5 in major and overall (evenly split between A-'s and B+'s )

 

Math: Standard stats major stuff, i.e.

linear algebra, multivariable calculus, ODEs, math stats, real analysis I

 

CS: Intro to CS, data systems and algorithms (B+ and A- respectively)

 

Research: 1 summer in a biostat project on campus, 1 summer doing NSF-funded biostat research at similarly ranked uni

 

Where would I be competitive for master's programs? I'm more interested in applied stats rather than pure stats (especially machine learning). CMU is one place that I'm interested in, but since I don't have that much CS experience/middling grades I'm not sure if I'd be competitive there.

 

I'm also trying to decide whether to take PDEs next year or theory of computing. Which would be more useful to a student interested in applied stats/math?

[pumpkinspiced latte]

Are you interested in Applied Math, Stats, Biostats, or CS? With my extremely limited experience I believe you'll be competitive just about everywhere for M.S. in Stats/Biostats. Perhaps not so much for CS anywhere. Well, contingent on your GRE score and subject score. PhD is a different story.

 

I think you'll be more competitive at CMU Stats than CMU CS.

 

It really depends on how PDE/computing theory is taught at your school and how much you'd enjoy learning about the topics... I'd take theory of computing just because I'm more interested in that, but that's just me.

[ANDS!]

If your only option is PDE's and Theory of Computing - I would go with the PDE.  IF those two are your only option (because there are a ridiculous number of better courses to take if you're trying to get competitive).

[cyberwulf]

If Theory of Computing is about closures, lambda calculus, and other theoretical aspects of programming languages, then it won't help you for an MS in stats. If it's about running time analysis of algorithms, big-Oh and little-Oh notation, etc. then it might be marginally useful.

 

I took PDE as an undergrad, and have never used it since in my statistical career, but I think it would be pretty fundamental if you're thinking of going in the direction of applied math.