Course selection for prospective statistics applicant

[bayesically]

Hi all! I’m struggling to choose courses to take this year before applying to statistics graduate schools for 2021 admission.

I’m in the 3rd year of a 4 year BSc+MSc maths+stats degree. The first two years were a foundation of undergraduate pure/applied maths and some probability/statistics.

I have to take 8 courses, and right now I’m looking to do:

Measure and Integration, Functional Analysis, Probability Theory, Stochastic Processes, Time Series, Statistical Theory, Statistical Learning, and Data Science.

Courses I’m considering:

Statistical Modelling (mostly on GLMs) and Markov Processes (another analysis/probability type course)

I’ve got 99th percentile on the subject GRE.

Do you think it would be better on an application to take perhaps another stats course (modelling) or another maths course (markov)? Or is my current course selection a decent balance of maths and stats as is?

Thanks!

[bayessays]

Once you already havw linear algebra, real analysis/measure theory, mathematical probability and statistics, and perhaps some numerical analysis/optimization courses, it won't make a huge difference what exact courses you choose as electives. If the stats courses are completely applied where you don't do a lot of math, then I would lean towards math but you already have so much math that you'll be good even if you never take another class.

[Bayequentist]

In addition to numerical analysis/optimization, I think you should take a legit CS class, like Data Structures & Algorithms or Parallel Programming.