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The MSc in Mathematical and Computational Finance at Oxford is an exceptional program for those aspiring to build a career in quantitative finance. Most of the faculty are leading researchers in mathematical finance, and the courses are meticulously designed to provide a strong foundation for quant careers. While the majority of the teaching is excellent, there may be a couple of courses where the experience is less polished.

The Oxford name undeniably opens doors—its prestige often increases your chances of getting shortlisted for interviews, a competitive edge in this field. Additionally, the Oxford experience itself is unparalleled, with traditions like matriculation, formal dinners, and the college system making it feel like stepping into a Hogwarts-like world. Studying in some of the most stunning libraries, including those featured in the Harry Potter films, adds to the magic.

The program is, however, incredibly demanding. What many universities in the USA cover over two years, Oxford condenses into just 10 months. The packed schedule leaves little time for breaks—you’re often balancing exam preparation, coursework, and job or internship applications simultaneously, making it a true test of resilience under pressure. Weekly seminars with leading companies in the first semester provide excellent exposure to the industry.

Despite the intensity, the journey is highly rewarding. Completing this rigorous program equips you with not only the technical expertise but also the grit needed for a successful career in quantitative finance. If you're ready for the challenge, the MCF at Oxford is an experience like no other.

I first became aware of this course because of Baruch's graduate program MFE. also I was well aware of the importance of C++ in the quantitative field. Looking at the results, I think my coding level has improved significantly.

They cover every aspect of Financial Engineering including advanced C++, Time Series Econometrics, ML/LLM. The program director herself is a great quant who worked at UBS doing market making. The career center is super supportive of finding students their first internship.

As with any program there are dipshits, but I think the time our program director has invested into getting good candidates into the MSFE has yielded great results, especially in terms of scholarships. The friends I've made here are definitely for life and we have supported each other a lot.

The program director does a lot to get people jobs by getting us into various corporate sponsored projects. For others this has helped them especially in the interview stage.

The administration/directors of the program are decent at responding to issues. For example, there recently was an issue with international students having to pay extra for OPT that was resolved. I have my old review regarding this issue for posterity on my profile.

One issue, however, is lack of teachers in the business department. This has led to two big issues. One is classes being overlapped with undergraduates. Normally, this wouldn't be the biggest issue, but the derivatives course is going to be at an undergraduate level, so it is less useful for graduate students. The professor has been great and will try to adjust the course just for graduate students but he shouldn't be forced to do that. Two, the program has professors who have influence on the curriculum and keep their outdated courses in it.

The program and Linda have managed to rope in a big figure as the new Director, this will make the program substantially better than it was for the past few years.

Program is decent but mostly on your own. I interviewed 30+ firms this cycle (JS, Citadel, DE Shaw, IMC, AQR, SIG, BH etc), and received two offers. I would say the support coming from the program is 35% and the rest is on my own.

Curriculums:

4 required courses (ACCT, Investment, Corp Fin, ML). The ML course is pretty useful and cover basically all you need for QR interviews. It's taught by Dacheng Xiu, literally the best financial ML scholar today.

For electives, you can tailor your classes to fit any track you’re interested in. I'm for quant and all in PhD-level courses in statistics, finance, and OM, all of which are excellent. PhD Courses like Asset Pricing taught by LPH, Probability and stats, Stochastic Optimization, and Convex Optimization are particularly useful for interviews. They also leave some options for PhD, which is great.

Job Placement and support:

Program received $60M in funding from AQR, which provides some preferential opportunities for our students. also internal referrals from top mutual funds and Booth alumni hedge funds. However, beyond these , job support is fairly average. We dont have program career fairs but can only participate in the Uni-wise. Some info and network session though with Seniors from Citadel, GS, KKR, BX, AQR, NorthernTrust but still mostly will not have any meratialized benefits. Most students are on their own for the majority of the job search process.


Peers:

Overall great but with extremely high variance. Many classmates come from like Harvard, Stanford, Wharton, Columbia, Uchicago, and with impressive internships at BB Snt, BB QR, BB IBD, Top 3 PE etc. But there are also peers from relatively poor background who struggle to secure interviews and keep up with the coursework, which is common ofc.


Overall:

Booth has a wide alu base and great reputation in HF and mutual funds. That says, if you are good enough and have been trained rigorously during undergrad, you will get plenty of interviews for sure particularly from top HFs and a good chance of landing a position. However, if you are new to quantitative finance and looking to build your skills, I would recommend considering other MFE programs that offer more structured support.

The content of this course is high-quality and has a standard similar to that of other big-name universities in the UK, which charge much higher tuition fees. Many of the lecturers are excellent, however there are a small number whose optional courses are avoided with good reason. The application process is simple and rarely requires an interview. The quality of students is varied, with many performing exceptionally well throughout the course and going on to study PhDs or acquire excellent graduate jobs, and others who struggle in many modules. There are career services provided, and the University has an industry partner in Santander UK, with whom a small number of students may be selected to complete their dissertation project in the summer. Overall I would recommend this course especially due to its value for money for home students and the content covered, but it may lack the industry prestige of the top UK universities.

Background:
I graduated with a major in Math from a U.S. college before attending the BU MSMFT program. My GPA was just below 3.5, and I didn’t have a lot of working experience. My GRE scores were Quant 169, Verbal 160, and Writing 4.5. In my class of 2023, there were 106 students, most of whom came straight out of college. The program included a bootcamp and three semesters in total. I also attended an optional fourth semester to extend my student status and have more time to look for a full-time job. After graduating, I landed a full-time position at a bank as a financial data analyst.

Schoolwork:
The two-week mandatory bootcamp covered the basics of programming, math, and stats without extra fees. It was a good refresher that prepared us for the actual courses in the program.
Each semester, we chose four courses worth 3 credits each and one 1-credit career prep course. In the first semester, everyone took the same courses: Stochastic Calculus, Stats, Programming, and Finance. These courses helped me build a strong foundation as a quant and were even helpful when preparing for job interviews. The stochastic calculus course was rigorous—great for those considering a PhD in finance but maybe a bit too academic for students planning to enter the workforce directly. To address this concern, a new course was developed, but I didn't have the chance to take it. However, the feedback from my classmates was positive.
Starting from the second semester, we had the freedom to choose courses based on our interests, though some third-semester courses had prerequisites. I chose Fixed Income, Machine Learning in Finance, Portfolio Theory, and Financial Econometrics (a corequisite for Portfolio Theory). In the third semester, I took Corporate Risk Management, Credit Risk, Economics of Fintech, and Accounting Risk Management. The workloads were reasonable, with homework assignments, exams, and final projects. It was great that we could showcase our academic projects on our resumes. I also appreciated learning about machine learning, blockchain, and cryptocurrencies, keeping us up-to-date with cutting-edge technologies in finance.
In the optional fourth semester, I took courses outside the MSMFT program, such as Big Data Analytics, Corporate Financial Management, ESG Investing, and Time Series Analysis and Forecasting. Through these courses, I learned valuable skills like Spark, SQL, financial modeling, ESG investing, and time series analysis. I believe these will provide long-term benefits in addition to the rigorous training in mathematical finance. I'm glad the program provided the flexibility for international students like me to have more time to look for full-time jobs.

Career:
Throughout the one and a half years of the program, alongside schoolwork, we needed to focus on resume preparation, submitting job applications, and networking to land a full-time position in the U.S. It wasn't easy, considering the rigorous courses. Despite help from the faculty and career services, it ultimately came down to our own dedication and effort.
The program offered career workshops almost every week. Some focused on resume and interview preparation, which I found very helpful. Others featured industry professionals from various financial institutions sharing their experiences. These were great opportunities to learn about different applications of mathematical finance skills and to network. However, since not all speakers were hiring and students sometimes lacked background knowledge in their fields, not everyone valued these workshops as they should. It could be better if the faculty or the Math Finance student club provided some background information beforehand, so students could have the right expectations.
Additionally, the program offered internships and research projects during the summer after the second semester. Many classmates struggled to secure internships due to limited opportunities, so it was great that many found positions internally or participated in research projects supervised by professors.
We could also request one-on-one appointments with career coaches who are current industry professionals. They provided personalized guidance on resume improvement, interview preparation, and even salary negotiation after receiving job offers.
The BU MSMFT alumni network is also a great asset. Alumni provided personal advice during networking events in Boston and NYC and were very responsive when I reached out on LinkedIn.

Faculty:
The professors were professional, knowledgeable, and genuinely cared about the students. They were always willing to talk with us—whether during lectures, in office hours, or via email. Their openness made it easy to seek help and deepen our understanding of complex topics.
A special shout-out to Professor Xing, Professor Jacquier, and Professor Kelliher for their dedicated teaching and valuable advice during my time at BU MSMFT and even after graduation. Their willingness to engage with students made a significant difference in my learning experience. I'm extremely grateful for all the career advice from Jun Fan and Jason during our appointments; their guidance helped me achieve my goal of getting a full-time job in the financial industry. I also want to thank Joe and Abby for hosting networking events and creating opportunities for us.

Summary:
My experience at BU MSMFT was great overall. The faculty is professional and caring, the coursework is rigorous, and the career services are valuable. I highly recommend future students to attend the program.

As someone trying break into MFE programs from investment banking, I had limited cs classes (1) in undergrad. I learned from various connections that this course is highly regarded in the world of quant finance, and after finishing I feel so much more confident about programming. This course is very dense in content (especially for people new to programming) and requires time to really do researches on your own (especially for Level 9) but the teaching is great. Also I received great instructions from TA and the forums. I would strongly recommend for anyone interested in quant finance, regardless of prior programming experience:)

1. Focus on Cutting-Edge Quantitative Skills
The coursework focusses on quantitative finance, machine learning, and risk modeling
2. Practical Application
Real-world projects and case studies simulate finance industry challenges. Perfect combo of Math + programming + finance
3. Industry Connections
Proximity to NYC for networking, internships, and Wall Street job opportunities.
4. Research and Labs
Access to Hanlon Financial Systems Lab for hands-on experience with financial data and analytics.
5. Alumni Network
Strong network in finance and fintech supports job placements and career growth.

The course provides a very comprehensive introduction to C++, from the basics to OOP and GP. The course has helped me establish a foundation in C++ for financial engineering, and I hope to see its real-life applications in the near future. I find my TA very responsible, and the supportive community has also been very helpful.

I heard about the course from my classmates. I wanna take this course because I didn't take a C++ programming course at school, and this is necessary for a MFE program admission. The course is really well-arranged, especially for the homework part. It enables me to dive deeper in to every level step by step.

Academics:

Core topics include optimization, stochastic models, statistics/time series, monte carlo, financial engineering / options pricing. The program had real professors (research, tenure track) teaching most of the core classes which was good. From the core courses, I thought statistical analysis taught by Professor Agostino Capponi was well structured. It gave a good balance between rigor, for understanding the theory, and just knowing, for application purposes. Professor Capponi is a great teacher as well. Unfortunately there were some professors who didn't seem to care enough. A lot of courses were not well structured nor carefully treated, resulting in a poor learning experience and not a real understanding of the material. Perhaps due to the relatively diverse educational backgrounds of the students in the program, they cannot develop the subjects more rigorously. Some classes were just speeding through lecture notes to cover the topics. Because of this, don't expect to get an understanding of how to think about problems which you haven't already seen before. Many students have already learned the core subjects, so they have an advantage when it comes to the tests, as it should be since it's only fair. But the poor course structure and teaching only increases the disparity in exam grades, which I believe is a problem, particularly so with regards to difficult tests in topics like SDEs, stochastic calculus, stochastic integration.

You need 36 credits to graduate. The core is 18 credits. There is a list of pre-approved electives (not the one on the program website) and you can also apply to get a course not on list approved as an elective. They are relatively lenient with what gets approved, as long as it is somewhat relevant and quantitative / analytical. I'm not sure how unique this is to this program, but I was still really appreciative of the opportunity to have classes outside of the program or even from other departments.

Students:

Some students were caught cheating by having the solutions to a final exam beforehand. Not sure how that happened. There are a lot of smart students though, and I think most are pretty nice people.

Practitioner Seminar:

The talks based on industry research papers were too complicated and most people got nothing out of it. I didn't like the fact that there was a required 500 word reflection after every seminar, it doesn't create the right incentives.

Career Prospects:

A lot of students have struggled to get summer internships. Career services is generic and not specialized. They use AI to grade your resume. Basically don't expect them to place you into a job with connections, you need to do it yourself. You may see some decent placements come out from the program but it's not the program that elevated them to that position. They were already at that level and they came for the brand name / visa. MFE programs in general will not place you into HFT, prop trading, buy side quant research, etc. If that is your only goal, there are better ways to achieve it.

Program / Department:

One cannot ignore the fact that the IEOR department runs 5 MS programs in their department totaling ~800 people per graduating class. That's a lot of people with very similar degrees competing for jobs. That means each student and program loses its value and it would suggest that the department does not care about the MFE nor any of the other 4 programs.

If you are an international student who just needs a visa, this is probably a good pick for you. Otherwise, I would probably look at other programs, including those outside of MFE/FinMath programs .

Excellent experience! The course is really systematic and has covered a lot of details as well as ideas for program design.
The course was recommended by a friend to me who is also learning financial engineering. He found the course useful and informative so I decided to take the course as well, which turned out to be a wise choice.

Fantastic learning experience. The TA Avi and Paul were super helpful. The course is clearly outlined and well structured, and the videos are very clear in content. It is recommended to people who want to learn C++, not especially for finance people for most of the course content (but it might be a little difficult if they have no idea about those options).
The reason for taking the course is I would like to widen my options when applying for a job. With C++, I could apply for Quant developer or other modeling positions that require C++.

My experience in the course has been good, I found the lecture notes, exercises and forums very helpful. My reason for taking the course was learning cpp but not just in the general sense. I think cpp is a lot more intimidating than python to learn and so I was looking at structured learning options when I found the course on QuantNet. I decided to take the course as the reviews seemed very positive and it seems like this certificate was more meaningful than other potential courses. I feel like I definitely learned a lot and have a good grasp of the fundamentals.