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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.

Good things

1. The Program offers diverse choices of subjects that you can tailor for your desired career path. 45 credits are required for graduation, 30 core and 15 elective. The 30 core credits, corresponding to 10 classes, will cover all the basis subjects centering 3 core pillars: financial instruments (their structured and behaviors), applied math for finance (stochastic calculus, numerical analysis, econometrics, etc.), and programming. Courses in financial instruments are the most captivating of all three. Courses in math are more like acquired taste and may not be intriguing (if not highly challenging) for everyone. Programming courses (OOP & OOP II) are straight up not good (more on this later). Overall, you will get to learn what you need to know to get started. Then the 15 electives would be up to your plan for yourself. You can make it mathy (Optimization, Stochastic Processes, Financial Time Series, etc.), OR more on investment (Indexes & ETFs, Portfolio Management or Hedge Fund), OR more on trading (Quant Trading Strategies), OR more on data analytics (Data Mining, Machine Learning). Supposed that you can figure out what your tea is, you can customize the topping as you wish.

2. Some professors are amazing. So amazing that you will feel transformational simply being a student attending their classes. Andrzej Ruszczynski for Stochastic Calculus. 100% of students would agree on this. Stochastic Calculus, if taught in the wrong way, could kill off the interest. Prof. Ruszczynski would ease you in and make your 3 credits the most worthwhile of all. Other professors who are like veterans in the fields: Priyank Gandhi (Fixed Income), Ronnee Ades (Indexes & ETFs), John Longo (Hedge Fund or Applied Portfolio Management), Mariya Naumova. They have for students enlightening lectures and even opportunities to connect with people working/hiring in the industry.

3. Career Management Office. I can see that the team is very supportive and try their best to prepare you to the teeth. Of course, you have to put in effort to at least reach out and cooperate. They do a great job helping you practice and be ready for your application and interview.

Bad things

1. Students cheat. Fault on students, but I will give unpopular opinion here that fault on some professors as well (emphasis on some). Cheating is done for more reasons than just passing the subjects. For some classes, professors provide example midterm/final questions and have sessions to discuss solutions. These sessions help students to learn and to prepare. Other classes do not have so much clarity except for the policy and this gives students more reasons to look out for more information. Looking out done wrong leads to cheating. Students do study so very hard and only few are free riders. Cheating does not completely negate what a student has learned, but it gives the focus on the wrong things and it is simply just wrong. I highly recommend that professors help students with sample tests (which should be relevant instead of some toy examples with difficulty so not comparable to the actual exams) and discussing sessions so that they focus on the right things which is learning. I also recommend that students give themselves chances to make the most out of their learning experience.

2. Programming courses (OOP & OOP II) are not good. Not captivating, not good enough exercises to practice, not a good guidance for self-study, not practical. I gave these classes so many chances, but I just found myself wasting time. To get pass the classes and assignments in other classes, I had to do rigorous self-study. As a student who attends classes and pay for classes, I would love to be helped out a lot more. The thing that I would like to have more is a good set of assignments with increasing difficulty (easy to difficult), covering the key concepts, libraries that are frequently used in quant finance. Example use cases on how libraries and algos are used in pricing/trading/risk management would be much appreciated. After all, it would be very helpful to see the principles and math in paper come into real codes. It would be helpful that we have good examples and coding practices instead of figuring completely everything out on our own while struggling with our homework (when we have to strike a balance of whether we want to learn it right (but time consuming) or whether we should do just anything to meet the deadline.

Finally, I think MQF provided me what I need to land on my feet. I appreciate the classes and professors who are always dedicated to helping students in learning and in making a career. My takeaways are that many core classes are worthwhile and transformational and that one should not doubt to devote time and effort to make the most out of the years in MQF. The program does have a lot to offer. The downsides are there, but it does not take away the training and learning opportunities at all. By the time I left MQF, I knew that much effort has been put in to fix the cheating practice as well as the classes that are not perfect to students' needs. The program is good and improving. I personally recommend it to people who are seeking doors to promising career opportunities.

The MSc covered a wide range of relevant areas, giving students exposure to different fields before specialising in later terms. It also provided a strong foundation in core topics such as fixed income and risk analysis. The coursework offered valuable practical experience, helping us to consolidate theoretical knowledge, appreciate the real-world challenges of quantitative methods, and gain useful material to discuss during interviews. Some modules were quite challenging, but this was ultimately beneficial, as it better prepared us for the demanding nature of the industry.

The programme also included workshops where practitioners presented the latest advances and challenges in both academia and industry. These were especially valuable, as they provided inspiration for dissertation topics, exposed us to less common but highly relevant areas of research, and offered great talking points for interviews or for those considering a PhD.

I had heard mixed things about the career services from the previous batch, and it’s true that after Linda’s departure, the program initially struggled in that area. However, despite those challenges, the prior year's full-time employment results were still quite solid.

I couldn’t praise the professors at Haas enough. In nearly all the courses, I had a great learning experience. Even subjects that are usually considered less engaging were made interesting and enjoyable to attend. The courses on- Empirical Methods, Stochastic Calculus, and Fixed Income - were excellent. I know students really enjoyed the hands-on experience of the High-Frequency course, and you can never go wrong with Nancy teaching Asset-Backed Securities. For me, that was a delight. Learning from these professors has truly been a once-in-a-lifetime experience. That said, I believe the program and faculty could be a bit more vigilant about academic integrity during assignments. Overall, the academic experience has been outstanding.

The cohort quality exceeded my expectations. We had a diverse mix of students, including many with strong prior experience, which added real value to clarity and preparation. The batch was supportive, collaborative, and mature.
The alumni network is also very approachable and helpful, though there’s still room for alumni connections to improve.

Given the short duration of the program, I’d strongly recommend incoming students to complete their interview preparation before joining. It's important to take the full benefit of the program.

I’m quite satisfied with the new career services team. They are genuinely putting in the effort to attract firms and support students. Despite not coming from traditional career services backgrounds, Chris and Ian adapted quickly and have been very helpful throughout the process. With Vash joining the team, their effectiveness has only improved. I’m confident that the new career services team will deliver even stronger results in the future.

Lastly, while the program director’s involvement could be more hands-on, but again it’s not a dealbreaker.

Outstanding career support, strong connections with the industry and many interview opportunities in buy and sell side. This is in large part a consequence of the small class size, so that each student can be closely followed and assisted in the job search.

Course quality varies by class but most of them are very useful and some are just outstanding. I use a lot of things I learned there in my daily job. It is also good to have many instructors coming from the industry.

Very strong community and close relationships with alumni.

I would say all courses are very well designed and structured. Which helps you have solid understandings of quantitative/finance world. The courses like risk analysis, stochastics, numerical methods, etc., are extremely helpful and practical in real-world applications. Moreover, the professors are all generous to support any questions and career development. I highly recommend for joining the quant cluster at Bayes