I’m currently trying to decide between UC Berkeley and Carnegie Mellon University and I would really appreciate your advice.
Here’s some background:
Price: The costs for both schools are nearly identical.Admission Details: I was admitted to Math/Mathematical Sciences at both schools, with plans to transfer into CMU’s Computational Finance B.S. program, as I’m interested in pursuing a career in quantitative finance (which will also likely require some masters education if that changes anything ).
Start Date: I’m admitted to start in the Fall of 2025 for both schools.
Location: I’m a domestic student from Connecticut.
Preferences: I’m looking for a school with a solid math program and a good campus culture. I prefer smaller class sizes, though I’m aware that Berkeley’s class sizes taper off in upper division courses. I found Berkeley’s campus culture more appealing and more active compared to CMU.
Pros and Cons:
Carnegie Mellon:
Pros: Smaller school with a dedicated track for Computational Finance.
Cons: Located in Pittsburgh (not my top choice), uncertain about employability outcomes, and campus culture seems less lively compared to Berkeley. Also, I didn’t find many reputable faculty in the fields I’m interested in. (Though this is not the most important thing at all)
UC Berkeley:
Pros: Strong reputation in math and quantitative subjects, with many renowned faculty. The student life is active, and I feel it’s a great place to grow academically and socially.
Cons: Berkeley doesn’t have a dedicated Computational Finance track, which means I would have to explore other avenues for pursuing this career. The school is also large, which can make things like course planning and access to professors/career services. Additionally, there are some crowded facilities.
So why am I stuck?
While CMU has a dedicated program for my desired career path, I feel its campus culture and location aren’t quite what I’m looking for. On the other hand, Berkeley has a stellar reputation, especially in math, but its large size and lack of a comp finance track, as well as some logistical challenges, concern me.
I am leaning toward CMU because of the dedicated track for Computational Finance, but I’m still weighing the differences in campus culture and location. Any advice on choosing between these two schools would be greatly appreciated! Specifically, if you’ve experienced either campus or have thoughts on the quant finance industry, I’d love to hear them.
It’s all subjective but Pittsburgh is far more appealing a city than Berkeley.
Here are CMU’s outcomes in the major - what they are reporting. One of your cons is employability and need a Masters but that doesn’t appear to be the case although they don’t show large #s (quantity wise).
You might contact and speak to a professor regarding career prospects. You might also ask to speak to a current student or be put in contact with a recent alum (or find one on linkedin and reach out and ask to speak).
I’d vote CMU - for size, location, and more but it could be there’s no bad choice - other than you did so well that you have a choice Best of luck.
Go where you feel you will be happier, environment matters. As to a career in quantitative finance, what exactly do you mean? Do you mean as a trader or some behind the scenes research or operational role at a hedge fund or financial institution, or working in the treasury or finance department of a large corporation with complex financial products, investments or other financial risks? For most jobs, a master would not be required at an entry level. @hebegebe could give you more insight once you define your goals.
I was looking more into quant research as a career path if that clarifies. I’m a big fan of mathematical modeling and quant research is one of the most lucrative jobs in that field.
I’d go where you feel you will be happy for the next four years. Your post-graduation opportunities will be determined by what YOU accomplish during your college years rather than by which one of these wonderful colleges you attend.
Thank you! I will say though I’m a bit worried about how much I can achieve at a significantly larger institution, but I also understand not all opportunities will come from the school I attend like external REUs and internships in the area.
I’m generally very skeptical of the idea you need a really specific undergraduate major like Computational Finance. If that is available and you enjoy it, cool. But often you can do whatever curriculum you would actually need with some other more general major. Sometimes there are tracks or concentrations, sometimes minors, sometimes you can just take the courses. And I think the vast majority of next step gatekeepers do not care about the name of your major, they just (might) want to know what you have studied.
And then of course people change their minds in college, many people in fact. So you don’t want to make a decision depending on some really specific major you don’t even end up doing.
So personally, I would not advise using something like that as more than a tiebreaker. Like, if overall you liked these colleges pretty much equally and you used this possible major as the last decisive pro, again, fine. But in this case, to me it sounds like you would generally prefer Cal if not for this major. And I would personally suggest you then just go with Cal, not treat this major as so important it would overrule that decision basically by itself.
I agree with @NiceUnparticularMan, students overvalue specialized majors (as well IMO double majors). For the types of job for which you are aspiring (and adjacent jobs in finance), the top employers are hiring the best available athlete, not the person with a specified certification. The question will be if you have demonstrated high aptitude in certain branches of advanced mathematics, data analysis and CS. They fully expect to train you in their methods, they just want to make sure you have the basics and intellectual firepower.
Note that most quant research jobs require a graduate degree, usually a PhD, although some companies will accept a master’s degree. Undergrads are mostly restricted to quant trading and software engineering.
This is exactly right. Students enter quant from a few different fields. CS obviously for the software engineering roles, but for quant trading and research it could be pure math, applied math like statistics, or math heavy fields like physics or electrical engineering.
Best of luck.