Firstly hello everyone, it's my first post and it's nice to come upon a site that is super relevant to me at the moment!

I'm coming up to my last semester of a statistics/math degree where I'll be taking stochastic modelling (introductory/intermediate) and generalized linear models - and I'm tossing up between taking possibly if i'm allowed a masters subject (like bayesian models or a math oriented machine learning course) - this is all in R. If I don't take one of these subjects, my last one would be a numerical computing subject which focuses stochastic simulation, direct methods for linear systems, fitting linear and nonlinear models, and time stepping methods - all done in matlab. I don't really know which subject to take and how they translate into quant finance area. Would it be best to go more broad with my education (numerical solutions) or deeper (bayesian or math ML)?

I would like to possibly do a honours year next year in stats - where I'd focus on a trading based project, and it'd help give me time so I can work on my maths work for answering some of these market making interview questions etc. A concern of mine is that I'm not really developing my python skills. I have done a few subjects in C, a broad ML subject in python, and have been doing my stats subjects in R. I definitely prefer python over R, as R's syntax is pretty gross imo; also python feels like a real language (I like loops). Is R used much in quant finance/market making? Or is it generally exclusively python and C++? It seems like a lot of the market making firms are orientated this way. I do know R now has packages that include all the ML methods, and can interface with C++/C; much like how python can. I'm not sure if I should even be worrying about this as I should be able to pick up more advanced python now that I've had an introduction.

I suppose I'd just like opinions on my subject choices, and also the use of R in quant finance? I don't think my thoughts are super well thought out atm, and I guess that just comes from a lack of clarity as to what is actually used in industry. I'm based in Australia btw if that is a factor.

Thanks for your opinions,

Mal