I graduated from UChicago's MSFM program in December 2021 and I'm an incoming Quantitative Trader of a Chicago-based prop trading firm. Overall, I loved this program! There is no way I'd be where I am without it. It thoroughly prepared me professionally (internship and job search) and technically (courses and projects). If you want to be a non-PhD quant, this is an awesome program!
I studied math, statistics, and computer science in undergrad from a Big Ten school. I was primarily interested in machine learning or a statistics PhD, but found quant finance very interesting and learned that a PhD is not necessary to be an applied quant. Having little finance background, I thought a quant program like this would be an awesome start. I applied to many schools, but my final choices were UChicago's FinMath program and Columbia's Mathematical Finance program. Both are awesome programs, but I haven't regretted my choice of UChicago for a second!
UChicago has an amazing reputation in econ, math, and statistics. The FinMath program also thoroughly used Python, Jupyter notebooks, and C++, which wasn't the case at every other quant master's program. They also are very up-to-date on machine learning and statistics which I liked, since this was my background. But a main differentiator was the career development office (CDO). CDO THOROUGHLY prepares EVERY student for interviews. Whether it's networking, preparing for technical interviews, or just organizing your search process, they are excited to help you individually. UChicago's Project Lab is also a differentiator; this was an awesome opportunity to get some quant finance on your resume and is built into the program. This was useful for me since I was new to finance.
This program is rigorous, but I don't think it would have the reputation it does without rigor. However, I would not say it was overwhelming or excessive. Fall quarter is definitely the most intense, but this is mostly due to the interview process. The remaining quarters can be as intense as you want; I took lots of courses in the winter and spring quarters so my final quarter would be quite light and I liked this. This program is not easy, but I think it's very fair and teaches useful and interesting things.
Career Development Office (CDO)
One of the best parts of the program. Every aspect of the interview process (general landscape, technical prep, networking, etc.) is formally and informally covered by CDO. I couldn't have done well in the interview process without them! Surprised to hear some other quant program's career offices do so little. FinMath CDO starts working with you before the program even starts. They start by perfecting resumes and cover letters. They also suggest thoroughly reviewing the Green Book ("A Practical Guide to Quantitative Finance Interviews") before you arrive; this is crucial to do! During the program, they offer tons of workshops on technical interview prep, behavioral interview prep, and networking. They also are open for 1-on-1 meetings whenever you want. They also have a rich alumni network so you can know what to expect on your interviews.
Perfect for someone new to the industry. This is a company-sponsored project that you work in a team on. Each project has a supervisor and you report weekly or biweekly to your company. There is also a final presentation. This is an EXCELLENT thing to talk about in subsequent interviews as well.
- Any course with Prof. Roger Lee. He is perhaps the best teacher I've had, and I've had a lot of strong teachers! He taught Option Pricing and Numerical Methods (computational sequel to his Option Pricing course). Medium workload for both.
- Prof. Greg Lawler's Stochastic Calculus Sequence. Stochastic calculus is not strictly necessary for modern quants, but I found this course extremely interesting and useful. Prof. Lawler is a highly respected mathematician and a great instructor too! Medium workload.
- Seb Donadio's C++ Course. C++ is extremely useful, and Seb will push you to become quite comfortable with it! He really cares about his students. Medium to heavy workload.
- Brian Boonstra’s Quantitative Trading Course: rigorous intro to quant trading and lots of practice coding up backtesters in python. This was perhaps the most intense course (for me) in the program, but I got a ton out of it. Boonstra is a highly respected quant.
- Ruey Tsay’s Time Series: very solid course taught by a very respected statistician. Medium workload and excellent time series content.
This is an amazing program and I'm so glad I enrolled! And make sure you review the Green Book before you arrive!