Just some background:
Did an undergrad math / econ degree at a top10, currently working as an analyst in risk management at a large investment bank.
Even though I have a math/econ background, my math classes were 'light' on quant finance stuff (no diff eq, no stochastic calculus, only 1 course in linear algebra / stats / econometrics, no time series analysis...). most of my higher level math classes were in stuff like manifolds, analysis, number theory (which were interesting, but not, in my opinion, as relevant to quant finance). To further compound things, I have virtually 0 programming experience.
Given this, I'm thinking of doing a Master's degree part time at Columbia to give me more of a true quant background, and help me move to a front office quant position. I have a few questions though:
1. I would be doing the program part time (firm pays for it); are part time programs easier / harder to into? Or the same?
2. Columbia has both a MA in Stats and MA in Mathematics of Finance program. I'm leaning towards stats because (a) it has less requirements, and therefore I can finish the program more quickly, (b) if I want to move out of finance, MA in stats would be looked upon more favorably, (c) you can take a lot of the Finance courses (stochastic calculus etc.) as electives. Are my assumptions wrong? I've searched for threads concerning the differences between the two programs but haven't had much luck in terms of getting answers.
3. How does the NYU program compare to Columbias?
4. I am by no means a math genius. I had a great undergrad gpa (3.95) but that was mostly due to my taking the easier math classes in college. Also, given my 0 programming background, would I get eaten alive in a masters program?
5. I've talked to several quants at my firm, and many of them have said that the day to day functions are much 'easier' than the stuff they learned in their MFE/phD programs. How true is this? Does it depend on which desk? (for example stat arbitrage seems to be, from a mathematical perspective, pretty simple).
Thanks again and this website is a godsend!
Did an undergrad math / econ degree at a top10, currently working as an analyst in risk management at a large investment bank.
Even though I have a math/econ background, my math classes were 'light' on quant finance stuff (no diff eq, no stochastic calculus, only 1 course in linear algebra / stats / econometrics, no time series analysis...). most of my higher level math classes were in stuff like manifolds, analysis, number theory (which were interesting, but not, in my opinion, as relevant to quant finance). To further compound things, I have virtually 0 programming experience.
Given this, I'm thinking of doing a Master's degree part time at Columbia to give me more of a true quant background, and help me move to a front office quant position. I have a few questions though:
1. I would be doing the program part time (firm pays for it); are part time programs easier / harder to into? Or the same?
2. Columbia has both a MA in Stats and MA in Mathematics of Finance program. I'm leaning towards stats because (a) it has less requirements, and therefore I can finish the program more quickly, (b) if I want to move out of finance, MA in stats would be looked upon more favorably, (c) you can take a lot of the Finance courses (stochastic calculus etc.) as electives. Are my assumptions wrong? I've searched for threads concerning the differences between the two programs but haven't had much luck in terms of getting answers.
3. How does the NYU program compare to Columbias?
4. I am by no means a math genius. I had a great undergrad gpa (3.95) but that was mostly due to my taking the easier math classes in college. Also, given my 0 programming background, would I get eaten alive in a masters program?
5. I've talked to several quants at my firm, and many of them have said that the day to day functions are much 'easier' than the stuff they learned in their MFE/phD programs. How true is this? Does it depend on which desk? (for example stat arbitrage seems to be, from a mathematical perspective, pretty simple).
Thanks again and this website is a godsend!