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I have been reading many reviews from the UK universities, and even though they're very useful, I still think people need a more crude, honest review to assess whether money is being invested or wasted. Let me be the first to do it with this MSc in which I am a current student of. This will be the first part, as I think people would love to know one's opinion when it starts (admission, induction, some courses, etc.) and after it finishes (job offerings, course difficulty, exams, etc.).

First, let me tell you how I arrived here so that you can imagine how. I was an Economics student in Pompeu Fabra University in Spain. This is one of the greatest Economics programmes worldwide, but also has something very special: it has a minor in Math that allows students to be more or less prepared for more technical programmes. I did it, and I also took another advanced undergraduate programme in BSE. Moreover, I came here to Warwick as a visiting student for 6 months. Then, I got into the MSc in Statistics and Operations Research at UPC, the best polytechnic school in Spain. There, I did some research assistantships and obtained various scholarships, and I also started an MSc in Mathematical Engineering online at the same time so that I can end up with two technical MSc's. All of this was planned to have a chance to enter at the UK good MSc's in Financial Mathematics. I have relevant industry experience too at Deloitte as a Quant Credit Risk Analyst and in a small dutch crypto market-making firm. I applied and got accepted to LSE, Edinburgh and Warwick. The latter gave me a 25% scholarship and is one of the best programmes, so I accepted it. For people who do not have that much money: this is also a very good choice because the yearly living costs are like half of London, and due to Warwick's ties to London and the train connections, it is easy still to go there for whatever.

Am I telling this to you to brag? No, is for all european (or other) students to see what it took me to got admitted coming from economics. Surely I could have gotten easier if I have did mathematics or some engineering degree, but I do not regret my path as I feel I have a great mix. Other students are mathematics, statistics, physics, 1 from Comp. Sci., aeronautical engineering and just 1 other from Warwick Economics. So as you can see, admission is tough (27 people in the programme this year). Moreover, I only applied to the UK instead of France, Italy, Switzerland, etc. because of personal reasons but also because London and USA tend to be the go-to destinations, so better be at some uni recognized by them, don't you think? Still ETH and other unis are recognized, so please apply if needed for a cost-effective option.

This is all for admission. When it comes to the start of the course, Warwick allows access to a pre-msc page which allows you to obtain preparation for the course, how to adapt to UK job market, resources that the uni offers, etc. This is really good. However, the preparation videos before arriving could be better, as the level seems okay until you really start the course. And then, level skyrockets.

First week, you do 1 full week (4-6 hours a day) of measure theoretical probability and then you have an exam Monday next week. The contents are not difficult if you got your right dosis of measure theory before starting. If not, well, you can always put double the effort. After that week, courses start and you can see that the most important but the most difficult is Stochastic Calculus, as Warwick's teachers are all relevant in that area and is their main research focus, so you could expect a very tough course. It is not the toughest in existence, but the pace is what makes it difficult. Students struggle to keep up with it, because you also have Financial Statistics, Asset Pricing, ML & Simulation and C++ Programming, which are not as difficult but are time requiring. Additionally, all of the modules have problem sets each week that you are highly encouraged to solve (not to get lost) but are quite long. Therefore, the amount of work and the intensity is very high.

In my opinion this is very good: not too many programmes are these intense with that good teachers, and I think that corporate life would not be way easier than this, so you have to get used to it. However, the only word of caution, mostly for international students and experienced students: you do not have that much time to apply for jobs the first term, which is the time at which they open. How can you solve this? Multiply yourself or double-down and get everything done. There is not too much to it if you want to start 2026 with a job. Nevertheless, most people wait for next cycle and keep getting good grades and using other opportunities they have available. In fact, international students can get the Graduate Visa to stay here in the UK, so situation is not that bad as in USA.

All in all, I think this programme is super good given the quality of teachers, their research and the centres they work for, the connections to London and other universities (Oxbridge, Imperial), and the education they give, specially in Stochastic Calculus. However, not everything is nice as pace is super fast and requires a lot from you. Must of us we do not have that much of a social life. But that depends on you. In the future, when this is over, I will try to give a more honest review on the outcomes of all I did. Hope this is useful!

Currently working as an part-time intern Options Trader. My experience with the program was very positive. We have a dedicated Director who genuinely cares about his students and is always willing to help them succeed in their future careers. The overall pacing of the program was intense but very manageable. The first semester starts with foundational courses to get your feet wet, while the second semester is where your skills, passion, and resilience are truly put to the test. This is also when most students enroll in the “Practicum Project,” where we work on practical projects sponsored by well-known companies like JP Morgan and CME.

In my opinion, the practicum project is what makes the program stand out, as not many MFE-type programs offer students a similar opportunity. It’s a win-win no matter what — you gain practical coursework you can showcase on your resume and have the chance to network with industry professionals. The quality of students is varied, but I see this as an advantage: if you truly stand out, your chances of landing a job are even greater relative to your cohort.

The core courses of the program are very rich in derivatives, so if that’s something you’re interested in (as I am), you’ll find yourself constantly eager to learn more.

With that, I want to conclude by saying this: as long as you have the passion and discipline to better yourself every day, you will truly be successful in this program — and you will be rewarded. Taking courses alone will not land you a job, but applying what you’ve learned and putting in the work beyond university coursework will.

The overall experience within UIUC's MSFE program is very positive. I've had gotten the opportunity to get hands on experience with projects that are far more closely tied to real world applications than what I would've expected within a Master's program. The application process was more straightforward for me as I have done my undergraduate within UIUC as well. What most surprises me about this program is that with specific courses like IE421 and FIN556, you get access to real CME and NYSE data which you can create strategies for. The unique thing is that it is the actual packet data, letting you dig really close into what really goes on in executing fast HFT trading strategies that can be optimized within a few nanoseconds. I think this alone grants this program is a pretty good spot but additionally, getting to work with industry professionals with the practicum projects is really helpful in learning how quantitative research work is like in the professional space. If you are interested in doing a more techincal career in quant, be it in technology within quant or HFT, I think this program is a pretty good fit for you.

I took this course because I am going to apply for some MFE programs and enhance my financial engineering background. I found it by friend's recommendation and knew it would be a great course because it's provided by Baruch College on QuantNet. The learning experience is great! My C++ programming skills got enhanced and I have also gained practical knowledge in its utilization in Financial Engineering.

If you are looking for a program that is career focused, MQF is not the best choice. The coordinator will send out student resume booklet for internship and fulltime, but that's it. They are still helpful because some quant internship positions in Canada are only open tot MQF, MMF, and MFI. If you are not in these program, you don't have the opportunity to see the job posting online at all. However there are not many networking sessions. Students can only rely on themselves to network alumni.

The courses are heavily related to Q measure pricing. There are not many courses related to buy-side quant. If I have to give a ratio, it would be 4:1. In the past, many MQF students already had some phD background in Math/Stat/Physics/Engineering, so the course content is very deep. Students should be very comfortable with measure theory to pass many courses. Personnally, what I learned from MQF in the past year is much more than what I have learned in my 4-year undergrad in the US.

I kinda agreed with one of the reviews that the staffs of MQF have changed a lot, and the coordinator and director are not as supportive as before. However, there are still professors that are responsible to students. Just consider the cost of tuition and living expense in Waterloo. Speaking of total tuition in CAD, 40k for international and 20k for local is super cheap for a quant program in North America. You get what you paid for.

This degree program lags behind most other MFE/Quantitative Finance masters programs by most metrics. Though the tuition is considerably lower than many other programs, particularly at other private universities, the poor quality of academics and career prospects make it difficult to recommend even considering this advantage.

Coursework/academics:

The standard first-semester courses consist of Corporate Finance, Investment Analysis, and Financial Mathematics. The former two primarily provide a foundation in fundamental finance, while the latter feels more like a crash course on statistics, linear algebra, calculus, and basic optimization. For students without a quantitative background, the Financial Mathematics course provides intro-level knowledge on these topics, but those coming from math-oriented undergrad majors will not see much benefit.

The required second-semester courses begin to scratch the surface of skills necessary for the quantitative finance industry, but go little beyond that. Furthermore, there is a lack of electives available that thoroughly develop these skills. The highest-level math course available, Computational Methods in Finance, rehashes much of Financial Mathematics while providing only a slightly deeper dive into optimization and introduces stochastic processes. The issue is that the course only introduces you to these more advanced topics without requiring true knowledge of them - they show up only trivially, if at all, on exams, meaning many of the students passing the course have not gained these skills.

Most of the other electives available stay within the fundamental finance domain. They would be passable curriculum for a non-quantitative finance degree, but the content is still not particularly rigorous and generous grading curves mean that most students can obtain their degrees without being knowledgeable about the topics studied.

Students:

Unfortunately, Northeastern relies on their reputation gained from the impressive career placements of its undergraduate students. However, its masters programs are much less selective and rely mainly on full-tuition-paying international students as a source of revenue for other departments. MSQF is no exception.

Most students come from non-quantitative backgrounds with the vast majority being undergraduate business majors. Rather than selecting more qualified applicants and creating a more rigorous curriculum, the university instead reduces the intensity of coursework to be suitable for the students it accepts. The instructors and generally highly credentialed practitioners and/or academics, but teach in accordance with what students are able to digest. One professor indicated that they wished they could teach more in-depth concepts but couldn't due to the students' abilities; another that they taught the same material to their graduate and undergraduate students.

In project groups, it became apparent that my partners had very little knowledge of the course material as well as the basic mathematical/statistical tools used. Additionally, they failed to catch glaring errors in the data they produced - the type that should be obvious to a lay person looking at the graph, let alone a qualified quantitative analyst. Nevertheless, high grades were handed out.

Many students lack clarity and knowledge about the industry, mentioning that they hope to break into IB despite pursuing a quantitative finance degree. Many of the international students indicated that they are taking the program only to facilitate working in the US.

Unfortunately, the program operates this way as a cash cow with little regard to the quality of the students.

Careers:

Northeastern markets heavily on their career prospects, which is a reputation well earned at the undergraduate level. However, placements for this program are abysmal. The program is intended to be completed in three semesters - Fall-Spring-Fall - allowing students to pursue summer internships before graduating. However, most students struggle to obtain relevant internships. The ones who achieve the best outcomes typically work in FP&A. A handful of students end up taking jobs in administrative functions with the university. I have not heard of any students breaking into quant research/trading/development.

Career services are almost non-existent and insourced networking opportunities are negligible. Additionally, the program is only a few years old, meaning there is not an expansive alumni network.

Overall:

This program offers little benefit for students looking to advance their careers, and almost no chance of developing the necessary skills for the quant industry. The only conceivable benefit would be for international students trying to find work in the US - but this degree will not help obtain a job in the relevant industries. If your goal is to break into quantitative finance, you will be sorely disappointed and $60k in the hole.

It's disappointing because Northeastern has the resources and potential to turn this into a high-quality program, but chooses instead to use it as a source of easy cash.

Rigorous! yet great program that instantly throws you into the fire. Very heavy on statistical methods, probability theory, and if you choose the right electives you basically end up getting a data science degree with a focus on finance, economics, and financial instruments. Great cohort, great resources like the bloomberg lab and all the trading challenges they offer you to take part of. Program makes you feel like you are actually at a quant shop doing rigorous work day by day. Lot of insanely smart faculty, if you pair this program with rigorous readings into linear algebra and difference equations for time series you'll become a polymath of stats, math, and coding logic. Program is also very research heavy, if you speak to the right professors you'll end up in some pretty cool rooms. Very good program for people who love the lore of financial mathematics and computational methods. Great time, friendly faces, and fantastic weather!

What I like
1. Great alumni networking event and high industry reputation, which lead to abundant interview resources
2. mock interviews and presentations from alumni to help lock in internship and full-time jobs
3. experienced industry leaders as instructors, providing insights and opportunities in industry.
4. fancy facilities and group event to create self-belonging

What I don't like and should be improved
1. More structured website for alumni networks instead of just names to connect on LinkedIn or from alumni events
2. More super sophisticated math and programming courses to boost the hard skills
3. more collaboration with other similar programs in UofT to share some common resources

I came from a strong mathematical background and wanted to move closer to the finance world. The MSc in Quantitative Finance at Bayes Business School was an excellent bridge between the two. The programme offered a good mix of technical and financial courses — subjects like Stochastic Modelling and Quantitative Programming were complemented by more finance-oriented modules such as Derivatives, Portfolio Management and Asset Pricing.

The balance between theory and practical applications was well-structured, and the professors were extremely knowledgeable and supportive. The amount of study material provided was impressive, allowing students to go well beyond the lectures. The class size was around 20–25 people, which made it easier to know your classmates and work closely with professors — though some might prefer a larger cohort.

Overall, I would highly recommend this programme, especially for students with a mathematical background who want to specialise in quantitative finance. The only downside is that the career service is more focused on corporate finance roles, so finding quant-related opportunities often requires personal effort and networking.

Overall, I really enjoyed the JHU Financial Math program. The emphasis on coursework is great — there are plenty of related classes to choose from, ranging from fundamental math and computer science to advanced topics like market microstructure, financial derivatives, and deep learning. Students can easily fulfill graduation requirements in 3 or 4 semesters, which provides flexibility for job hunting. For example, it’s possible to take only 3 courses in the fall to leave more time for interview preparation, which I found very helpful.

There are also some excellent professors — like John Miller, Hélyette Geman, and Roza Galeeva — who frequently discuss common interview questions and real-world applications instead of just going through slides, which really adds value.

As for what could improve, the career service is okay but not exceptional. The department organizes some trips to New York City for career fairs and alumni networking events, but given the limited number of hedge funds in Baltimore, the main local advantage is in energy trading (e.g., Constellation). The school provides basic help with resumes, networking, and mock interviews, but there are fewer than 10 direct referral opportunities each year — most job prospects rely on networking and alumni connections.

The current program director is John Miller, and he’s super helpful.

The University of Waterloo Quant Finance Master's program is an excellent program for someone who is interested in learning rigorous maths. The program focuses on statistics, applied maths, and relevant coding skills required to succeed in industry.

At University Waterloo, in the 2 years that it took to do my Masters (you can normally do faster in just 4 trimesters), I've learned more hard skills than in my 4 years of undergrad in business school. Waterloo will really push you to your limit, but the result will be worth it. Classes start off with classes focusing on stochastic calculus, and advanced statistics. For many students without prior background, they will struggle at first because Waterloo doesn't hold your hand at all. My first trimester, I was just barely passing, and getting only around 6 hours of sleep a night. By the end of your time at Waterloo, you'll find that the classes get easier, because many of the same concepts will be retaught/built upon. I like this teaching philosophy, because this way, you'll be given many chances to really remember what you've learned.

In general, the professors at Waterloo are genuinely nice, and will go through the extra effort to help you. Of course, there are some professors who care mostly about their own research, and will make it known that they dislike teaching. However, the number of excellent professors greatly outnumbers the number of "mean" professors.

For job placements, Waterloo is the number 2 program in Canada for quant finance. However, the job market during the time I was there was not great. Don't expect that everyone will place at prestigious big 5 bank in Canada. In my cohort of around 18 people, only around 4 or 5 classmates interned at a big bank. The 100% placement rate for co-op is also a bit misleading. If you cannot find a co-op, you simply start writing your Master's Research Paper over the summer trimester.

I wouldn't recommend U Waterloo for someone is primarily focused on finding a job in the US. The alumni network is mostly Canada based, and students are largely responsible for finding their own co-ops.

Overall, for just the quality of the education, and at the unbeatable price (when compared to US programs), I would strongly recommend Waterloo.

The NYU Math in Finance program is highly selective and academically rigorous. The coursework is extremely theory-oriented — you’ll learn a lot of deep quantitative and mathematical finance concepts. However, it’s not a career-training program in the traditional sense.

If your goal is job placement, you’ll need to prepare most of your interview and practical skills on your own. The program provides two dedicated career coaches who help with behavioral interviews and networking, but technical preparation (for quant, data, or trading roles) is largely self-driven.

You should only choose this program if you have a strong academic foundation and genuine interest in the theory of finance, as the courses and assignments are time-consuming and demanding. On the positive side, the small cohort size (around 30 students) creates a close and supportive learning environment.

My experience with the program has been overwhelmingly positive. The coursework is comprehensive and prepares you very well for quant finance interviews. Furthermore there are plenty of events and opportunities to network with industry practitioners. Our professors are leading researchers in the field and it's a great privilege to work with and be taught by them. The program has a very strong reputation in Canada and will massively boost the resume of anyone looking to work here. The faculty is very well connected with the industry and provides you with opportunities to apply for roles that are not publicly available. Coming from a math background I don't think I would have been able to break into the industry without this program. Waterloo and UofT are considered the two best programs in Canada and Waterloo comes at a fraction of the tuition cost, so for me it was a clear winner. I recommend this program to anyone looking to work in quant finance in Canada.

I decided to take this course because I plan to pursue a Master’s in Financial Engineering in the U.S. and eventually work and live there. Since Baruch’s MFE program is one of the top programs in this field, I wanted to start preparing early by building a strong skill in C++ for quantitative finance.

Amazing program! I received so much help from Dan and all the alumni. They're a group of wonderful people that go to great lengths to help their students. I'm also impressed by how much help that alumni provide in my search for a full-time job, not just from Dan and faculty - whether it's advice for technical, soft skill, or referrals, they are always happy to talk things out!

I cannot fully recommend this program, despite its strong academic foundation. While the school has a solid reputation and the professors are genuinely supportive and knowledgeable, there’s a significant gap when it comes to career services and job placement assistance.
The career support resources are insufficient, and students are largely left to navigate the job market on their own. Many graduates struggle to secure full-time positions even after completing the program, which speaks to the limited effectiveness of the career development services.
From a purely academic perspective, the program delivers quality education and the faculty truly care about student success in the classroom. However, if you’re looking for a program that will actively help you launch your career and provide robust job search support, you may want to consider other options or be prepared to be very proactive and self-directed in your job search.

I thoroughly enjoyed my experience with the C++ course. The structured content and well designed exercises provided a solid foundation and helped me develop a deeper understanding of programming concepts. I would especially like to thank my TA, APalley, for his consistent guidance, prompt feedback, and patience throughout the course. The forum community was incredibly resourceful, it offered an enormous amount of direction, discussions, and shared knowledge that greatly improved my problem solving approach.

This course not only strengthened my grasp of C++ but also taught me how to approach problems the right way, by understanding underlying concepts and learning from others’ perspectives. Overall, it was a truly valuable and eye-opening experience.

Like many things in life, people can have different opinions about the same experience, and the NYU MFE program is no exception. I can only speak from my own experience.

For me personally, the NYU MFE program opened a door that would otherwise have been impossible to reach. Academically, the program offers a mix of professors with strong industry backgrounds who can bring real-world insights and connections, and others who are deeply focused on theory and dive into the fundamental logic of mathematical models, both perspectives are valuable depending on what you’re looking for.

On the career side, we have Sara, who provides thoughtful career guidance and organizes events that connect students with alumni and help us better understand the industry landscape. I’ve even heard from alumni who said that Sara is one of the main reasons they still stay connected to the program. She truly builds a sense of community that lasts beyond graduation.

I’m genuinely grateful for this program personally. Of course, no master’s degree alone guarantees an ideal job, you still need to find your own direction and put in great effort. But if you’re looking for a solid ladder to help you climb toward your goals, NYU MFE is definitely a great choice that can make a real difference.

Academics:
Overall, I would rate the academics as average. You’ll spend your first year on the Ithaca campus taking classes from ORIE department professors. Some of them are excellent, while others are less engaging , so you’ll need to rely heavily on self-study. That said, all professors are quite responsible and willing to help. In the final semester, you’ll move to the NYC campus, where most instructors come from industry. This provides valuable exposure to real-world financial engineering applications, though the quality of courses varies by professor. You’ll also complete a capstone project, which can be a strong addition to your résumé.

Career Services:
I’ve seen mixed reviews about Cornell MFE’s career services. In my experience, they’re relatively decent compared to other programs ranked in the top 5–10 range. You’ll have access to resume drops that make it easier to land interviews than applying through LinkedIn, but you must stand out among your classmates. Some students get five or six interviews through this process, while others get none. The program directors will review your résumé, though their feedback may not always be very helpful. However, mock interviews with alumni are a big plus. Ultimately, job placement depends a lot on your own effort and a bit of luck but Cornell does provide solid exposure and opportunities.

Social Life:
This is a relatively small program with about 60–70 students, which makes the cohort close-knit. Everyone knows each other, and it’s easy to build friendships and a strong peer network.

Cost:
It’s quite expensive, so you really need to think carefully before committing to this program.

The pre-term Math/Python camp is extremely important. I’d suggest putting in as much time and effort as possible during that period, because once the semester begins, things move fast and you’re expected to figure most things out on your own.

The first semester is quite intense, with courses in Stochastic Calculus, Statistics, Programming, and Mathematical Finance. Most of our class found Stochastic Calculus and Statistics particularly challenging, as they’re very math-heavy and require a strong foundation. Personally, I felt the program could have been slightly longer since it’s fast-paced. Some courses don’t include any projects, and adding more project-based work would definitely help students gain hands-on data experience, though the homework load is quite solid.

The weekly career coaching sessions are helpful, but the industry network could be stronger. Overall, it’s a great program that gives you solid exposure to quantitative finance. However, you’ll need to take initiative on your own, both to learn and figure things out, and especially when it comes to networking and building relationships with alumni and professionals around Boston. The curriculum is also relatively structured, so there’s limited flexibility in choosing the number of courses each term.

I’d recommend the program, but there are a few cons you’ll have to work around, mainly the fast pace, limited flexibility, and the need to be proactive in seeking out projects and networking opportunities.