statistics phd admissions question, trying to figure out the best courses to take for my app. mainly stats courses vs math courses

so my school offers:

• statistical inference - an APPLIED methodology course which builds off of the basics (mle etc). apparently goes through computation and software to get MLE estimates and such

I've taken a proof-based math stat course (hogg/casella) and I'm not great with computation on exams so I'm kind of not too hot on taking this course

I could also take

• time series: non-measure theoretic but other than that not really sure what the content covers. not applied however

• topology: math obviously, preq for functional analysis

Anyone have any insight into what admissions would think looks better? My main hang-up is that the stat inference actually sounds useful but more annoying vs an easier (math-y is easier), not as useful but potentially (??? this is my main question) looks better.

For some context, I did poorly in undergrad bc I was burnt out from high school so my math grades are poor. Trying to offset some of that by taking high level math (confident I can get an A in any of the above).