MSc in CS or Applied Math for Quant Research ?

[jabran]

Hi,

I am considering doing an MSc with an aim to apply for Quant Research positions afterwards in prop/market making/HF and the question is which one of the MSc is likely to land me more interviews for QR, a CS degree (with ML heavy content) or Applied math ?

In terms of content, I think both kind of degrees are relevant to quant trading. When I look at jobs spec of Quant research positions for top firms, they do prefer candidates to have ML experience and it seems ML is extremely important for the problem they are trying to solve. But when I look at a lot of quant researchers on linkedin, they have math degree and are world class mathematicians and less of them have CS degrees and hence why I am really confused. So the dilemma is, if I do applied math, it will lack ML content which is extremely important and if I do CS, it won't be as math heavy and will miss out on things like PDEs, SDEs, markov etc which seems to be the kind of math used in all the market making literature (whether thats used in real world, I don't know).

Whichever degree I chose, if it lacks certain skills that are used in real world, I will learn on my own but for now, I just need to optimise for getting interviews. So any insight would be really appreciated.

Thanks !

 

[jabran]

In terms of reputation of school/program, do you think the following ranking order is reasonable (I put Columbia before Stanford since I think applied is better than CS but again I might be wrong in thinking that) ?

Columbia - Applied Mathematics Master's Degree ONLINE ($75k)
Stanford - Computer Science MS Degree
Johns Hopkins - Applied and Computational Mathematics, Master of Science ($49k)
Georgia Tech - ONLINE MASTER OF SCIENCE IN COMPUTER SCIENCE ($6.5k)

 

[jarryds]

Hi,

I am considering doing an MSc with an aim to apply for Quant Research positions afterwards in prop/market making/HF and the question is which one of the MSc is likely to land me more interviews for QR, a CS degree (with ML heavy content) or Applied math ?

In terms of content, I think both kind of degrees are relevant to quant trading. When I look at jobs spec of Quant research positions for top firms, they do prefer candidates to have ML experience and it seems ML is extremely important for the problem they are trying to solve. But when I look at a lot of quant researchers on linkedin, they have math degree and are world class mathematicians and less of them have CS degrees and hence why I am really confused. So the dilemma is, if I do applied math, it will lack ML content which is extremely important and if I do CS, it won't be as math heavy and will miss out on things like PDEs, SDEs, markov etc which seems to be the kind of math used in all the market making literature (whether thats used in real world, I don't know).

Whichever degree I chose, if it lacks certain skills that are used in real world, I will learn on my own but for now, I just need to optimise for getting interviews. So any insight would be really appreciated.

Thanks !

Machine learning is just a fancy application of statistics which IMO fits better under applied math. With that being said, every program is different. I have talked to people who picked up super naive perspectives on ML from CS degrees. I have talked to other CS guys who completed rigorous coursework with impressive portfolios of class projects. Instead of judging the programs off of their name, I would focus on the courses they offer.

There are a handful of programs that probably offer a combination of applied math and CS. Kind of seems like you are missing the point of grad school here - take what you find more interesting and fill the gaps where you see fit.