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COMPARE UC Berkeley MEng IEOR (Fintech) Vs Columbia MAFN Vs UCLA MFE

UC Berkeley MEng IEOR (Fintech) Vs Columbia MAFN Vs UCLA MFE

  • UC Berkeley MEng IEOR (Fintech)

    Votes: 11 34.4%
  • Columbia MAFN

    Votes: 17 53.1%
  • UCLA MFE

    Votes: 4 12.5%

  • Total voters
    32
Hi Everyone, I need advice in choosing between UC Berkeley MEng IEOR (Fintech), Columbia MAFN, and UCLA MFE. I am open to both data science and quantitative finance positions after completing my degree. It is really hard for me to make the decision. I really appreciate if people could give their valuable insights! Thanks.

Background
  • Degree - MSc Physics + B.E. Mechanical from Tier I college in India
  • Experience - 2 months internship at an oceanographic research institute, 5 months internship at an entrepreneurship-focused research institute, 6 months internship in operations management at Amazon, worked for 3 months as equity research associate at a stock brokerage firm, working as Senior Analyst in the Complex Securities Valuation department of EY since 1st week of Jan,2022
  • Technical Skills - Good knowledge of R, Python, SQL, and MATLAB
  • Additional Info - FRM Level 1 Cleared, 10+ MOOCs from Coursera, Udemy, and EDX related to Finance & Data Science, Will (hopefully) clear FRM Level 2 by the time I join in Fall 2022
UC Berkeley MEng IEOR (Fintech)
Pros –
reputation of UC Berkeley, proximity to silicon valley (ideal for data science roles), 9 months capstone project with JP Morgan, great career service, small batch size of fintech (~40)
Cons – short duration of course (9 months), fintech concentration was introduced in 2017 (less industry connections)

Columbia MAFN
Pros –
ivy league, located in NY (great access to finance companies), well-established alumni network of MAFN graduates, ideal duration of course (15 months)
Cons – internal competition with MSOR and MFE programs, poor career service, larger batch size (>100)

UCLA MFE
Pros –
ideal duration of course (15 months), applied finance project
Cons – less flexibility in course selection, quant net ranking going down over the years
 
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I would ask, what are the skills you are most looking to acquire, and what are the roles that you would be most excited to pursue?

UCLA has a more generic course offering. You'd be good for standard quant roles in risk management, modeling, and quant research.

But if you like fintech, ML, and data science aspects of finance more then UC Berkeley would have more offerings.

Columbia MAFN is more like UCLA's MFE but more quantitatively rigorous. So if you like roles that would require a deeper bag of quant, like modeling exotic derivatives and stuff, then this is going to be a good choice.

Honestly, this is a good problem to have, all of these are pretty good choices.
 
Hi Everyone, I need advice in choosing between UC Berkeley MEng IEOR (Fintech), Columbia MAFN, and UCLA MFE. I am open to both data science and quantitative finance positions after completing my degree. It is really hard for me to make the decision. I really appreciate if people could give their valuable insights! Thanks.

Background
  • Degree - MSc Physics + B.E. Mechanical from Tier I college in India
  • Experience - 2 months internship at an oceanographic research institute, 5 months internship at an entrepreneurship-focused research institute, 6 months internship in operations management at Amazon, worked for 3 months as equity research associate at a stock brokerage firm, working as Senior Analyst in the Complex Securities Valuation department of EY since 1st week of Jan,2022
  • Technical Skills - Good knowledge of R, Python, SQL, and MATLAB
  • Additional Info - FRM Level 1 Cleared, 10+ MOOCs from Coursera, Udemy, and EDX related to Finance & Data Science, Will (hopefully) clear FRM Level 2 by the time I join in Fall 2022
UC Berkeley MEng IEOR (Fintech)
Pros –
reputation of UC Berkeley, proximity to silicon valley (ideal for data science roles), 9 months capstone project with JP Morgan, great career service
Cons – short duration of course (9 months), fintech concentration was introduced in 2017 (less industry connections)

Columbia MAFN
Pros –
ivy league, located in NY (great access to finance companies), well-established alumni network of MAFN graduates, ideal duration of course (15 months)
Cons – internal competition with MSOR and MFE programs, poor career service

UCLA MFE
Pros –
ideal duration of course (15 months), applied finance project
Cons – less flexibility in course selection, quant net ranking going down over the years
Could anyone help me in figuring out why the initial trend of this poll is going so heavily in favour of Columbia MAFN? A poll conducted two years ago went in favour of UCB MEng IEOR (Fintech) instead.

 
I would ask, what are the skills you are most looking to acquire, and what are the roles that you would be most excited to pursue?

UCLA has a more generic course offering. You'd be good for standard quant roles in risk management, modeling, and quant research.

But if you like fintech, ML, and data science aspects of finance more then UC Berkeley would have more offerings.

Columbia MAFN is more like UCLA's MFE but more quantitatively rigorous. So if you like roles that would require a deeper bag of quant, like modeling exotic derivatives and stuff, then this is going to be a good choice.

Honestly, this is a good problem to have, all of these are pretty good choices.
Hi darthvader, I want a program which is strong in machine learning and its application in finance. I am 50-50 about working as a data scientist at a top tech company/ fintech startup and quant at a top-tier hedge fund (Citadel, AQR, etc). Also, I have kept the option of entrepreneurship in fintech open incase I want to return to India after working in the US for a few years.
 
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Hi darthvader, I want a program which is strong in machine learning and its application in finance. I am 50-50 about working as a data scientist at a top tech company/ fintech startup and quant at a top-tier hedge fund (Citadel, AQR, etc). Also, I have kept the option of entrepreneurship in fintech open incase I want to return to India after working in the US for a few years.
In that case, I would advise you to narrow it down to Columbia and UC Berkeley.

I think every program now has good ML electives. At least, among these two, I would be surprised if there is a big difference in their offerings.

Secondly, my personal opinion is, MAFN gives you more flexibility to land both kinds of roles, quant and data science. Whereas IEOR would be worse in landing you quant roles.

Also, Columbia MAFN is a very known quantity in quant circles, so whatever you might have heard here might be slightly biased toward it.
 
In that case, I would advise you to narrow it down to Columbia and UC Berkeley.

I think every program now has good ML electives. At least, among these two, I would be surprised if there is a big difference in their offerings.

Secondly, my personal opinion is, MAFN gives you more flexibility to land both kinds of roles, quant and data science. Whereas IEOR would be worse in landing you quant roles.

Also, Columbia MAFN is a very known quantity in quant circles, so whatever you might have heard here might be slightly biased toward it.
Thanks a lot for your valuable insight. I would really appreciate if other people could chip in as well with their insights. I’m having a hard time making a decision.
 
UCB>Columbia MAFN>UCLA MFE . My reason for putting UCB above Columbia MAFN is that Columbia MAFN has competition from other colleges(NYU, Baruch) as well as from Columbia IEOR students. UCB has a dedicated career services office.
 
UCB>Columbia MAFN>UCLA MFE . My reason for putting UCB above Columbia MAFN is that Columbia MAFN has competition from other colleges(NYU, Baruch) as well as from Columbia IEOR students. UCB has a dedicated career services office.
Thanks for your insight !! I would highly appreciate more people to chime in with their viewpoints.
 
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