COMPARE CMU MSCF vs UC Berkeley Statistics MA

CMU MSCF vs UC Berkeley Statistics MA

  • CMU MSCF

    Votes: 13 61.9%
  • UC Berkeley Statistics MA with a third semester extension

    Votes: 8 38.1%

  • Total voters
    21
Hi everybody here. I am now struggling in choosing between CMU MSCF and UC Berkeley Statistics MA (both 1.5 yrs)

I am an undergrad who major in finance and data science, with several internships in small buyside doing alpha research and building machine learning models and a data science internship at a big tech company. I wish to pursue my career at buyside as quant researcher or data scientist. I am also welcomed to be a data scientist in a tech firm, but my first preference is to a quant.

During my undergrad, I have taken all the prerequisites for MFE program, one graduate-level stochastic calculus course and some course in derivatives(including options, fix-income products, credit products, structured products and so on). And I think I still need more training on statistics and machine learning.

CMU MSCF
pros:

1. Rigorous training in sto cal, financial data science, etc.
2. Career services that make good internship and full-time employment statistics
cons:
1. I don't really want to do derivative pricing/ derivative sales. And I think I have had barely enough knowledge of stochastic calculus for a buy-side job (plz correct me if I am wrong). However, those courses are all required ones in MSCF. Meanwhile, I wish to take more CS/STAT/ML courses but it is hard to take more than one course from SCS department because the schedule for MSCF is somewhat fixed with many required courses and less flexibility.
2. Expensive

UC Berkeley Statistics
pros:

1. More training on statistics and machine learning. (4 required courses in probability, statistical inference, linear models, capstone and two electives required, and I am able to choose up to 10 electives if time permits)
2. Flexibility in selecting courses all over Berkeley if I extend to the third semester
3. Quite cheap if I work as a GIS

cons:
1. Most of the graduates have gone to data science positions in tech firms. Rarely do they find a job in financial institutions except risk management positions
2. Literally no career services on finance and I am afraid of getting no interviews/OAs. (But virtual interviews are hosted so I think location is not a great problem?)
3. Fewer chances to go to investment banks as a derivative quant

I really appreciate if anyone could give me some advice. Thank you!!!
 
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