Latest reviews

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

The MSc was an exceptional experience and extremely valuable for finding a job afterward. The Career Center provided great guidance in improving my CV and preparing for interviews. The quality of teaching was outstanding — a perfect balance between theory, practical applications, and real-world examples. I also built a strong professional network and remain in contact with many of my peers. I’m extremely grateful to the professors, the Career Center, and everyone who made this experience such an important milestone in my career.

The perfect start point to enter the quant industry. The course materials are very useful and professors have a lot of insights.

I actually come from a DS background rather than a traditional quant path, and the NYU MFE program turned out to be an incredibly valuable experience for me, which might be helpful to students with similar profiles who want to explore data science roles within the finance industry.

The best part of my FRE experience was definitely the career placement and alumni network. I found my summer internship through an internal posting on the FRE Net (thanks to Sara’s help!) — it was a financial DS role that gave me hands-on exposure to modeling in a real business context. Later, my full-time opportunity came from a referral by a senior classmate, who also guided me through the preparation process. Without this program and its community, I truly wouldn’t be where I am today.

That said, it’s important for future applicants to keep realistic expectations. Securing a full-time role in the U.S. is competitive and not something any program can guarantee. But compared with peer-level MFE programs, FRE does a solid job providing access to opportunities and mentorship for those who are proactive.

Overall, I’m deeply grateful for the program — it opened the door for my transition from data science into the finance domain, and gave me the foundation for the career I have now.

My experience with Baruch MFE so far is amazing. This is a very friendly and supportive community where everyone helps each other. The students are incredibly smart, and I’ve learned so much not just from the courses, but also from my classmates. The career service is also outstanding — almost everyone lands a job, even during recent market turbulence. The program manager has excellent industry connections, which makes the job placement process much smoother and more successful.

Academically, the program is rigorous. Most courses are math-heavy and the assignments really push you to grow, improve your quantitative skills, and learn teamwork. You get to work on practical projects that are directly relevant to what the industry values.

The professors are knowledgeable and approachable. They genuinely care about your progress, answer your questions patiently, and support your interests. If you show initiative, you can make great friends, learn from top students, and gain both academic and professional insight into financial engineering — even though the program is only one and a half years long.

Overall, I highly recommend the Baruch MFE program for anyone interested in finance and quantitative methods. It offers strong career outcomes, a great learning community, and a solid academic foundation that prepares you for real-world challenges. I’m very grateful for this experience and excited for what’s ahead!

When entering a financial engineering or mathematics program, many students aspire to become quantitative researchers or traders. While this is a reasonable goal, I've learned it's important to understand what each program specifically prepares you for. NCSU's MFM program is particularly well-suited for those interested in risk management and related career paths, rather than primarily QR or QT roles.

One of the program's standout features is its career services. The support begins even before classes start, with the team proactively reaching out to understand each student's background and career goals. They work individually with students to structure resumes that best highlight existing skills for target roles, providing personalized guidance rather than a one-size-fits-all approach. The interview preparation is comprehensive, including multiple mock interview sessions that build confidence and refine our presentation. A particularly valuable component is the semester-long practicum project, where students apply their learning under the mentorship of senior students. This experience, I believe, bridges the gap between academic concepts and real-world application. The program also regularly brings in industry guests to speak with students, offering authentic insights into day-to-day work in various quantitative finance roles. These sessions were invaluable in helping us understand the realities of different career paths and what to expect as we transition from academia to industry.

The program begins with Probability Theory and Options Pricing courses that provide a solid foundation. For students newer to these topics, they offer valuable grounding; those with prior exposure will find them a good refresher that ensures everyone starts on equal footing.
I opted to take the Fixed Income elective rather than the ML course, choosing instead to supplement my machine learning education through resources like StatQuest and various textbooks. This self-directed approach worked well for me in connecting theory to financial applications. The Fixed Income course provided strong coverage of various instruments, though I found myself wanting deeper exploration of the mathematical frameworks and tools used in actual pricing, something I supplemented through independent study.

The second semester ramps up significantly with Monte Carlo Simulations, Stochastic Calculus, and Statistical Inference. I also took the Statistical Learning elective, which I found to be very helpful as it offered a thorough treatment of ML through the ISL textbook. Having gained comfort with foundational concepts during my first semester, I was able to supplement the coursework with the more advanced ESL (Elements of Statistical Learning) textbook, which deepened my understanding considerably.

Monte Carlo Simulations stood out for its comprehensive coverage of pricing techniques and tools. Stochastic Calculus covered the essential concepts, though I would have benefited from a more gradual transition from deterministic to stochastic frameworks at the beginning. The course concluded with Feynman-Kac, and I found myself pursuing additional independent study to learn techniques for pricing American options, interest rate products, and other modeling approaches. Given the breadth of material - from fixed income to equity derivatives - I can see how a multi-semester treatment of stochastic calculus might allow for deeper immersion and more extensive practice with different securities and model calibration.

Like many graduate students, I supplemented my coursework with significant self-study, particularly for interview preparation. This included practicing programming problems, working through interview specific probability questions, and diving deeper into topics like yield curve calibration, and understanding the pricing and modeling of products in Fixed Income like MBS and ABS. While the program's curriculum doesn't extensively cover all these areas, this reflects its focus - preparing students thoroughly for risk management roles where practical application is emphasized over theoretical depth in pricing.

The program delivers on its value proposition: preparing students well for careers in risk management and adjacent fields, supported by truly outstanding career services that give students a significant advantage in the job market. For those specifically seeking deep expertise in pricing complex derivatives across all asset classes, supplementary self-study or a different program focus might be necessary. However, for students aligned with the program's strengths in risk management and who value strong career support, it provides solid preparation and a practical skill set that translates well to industry roles.

I’ll divide this review into two parts — the recruitment side (career services) and the academic environment.

Recruitment Side:
The career services team does a solid job. They bring a lot of companies to campus throughout the year, and even outside the MFin program, plenty of top trading and quant firms show up during university-wide career fairs. It’s a great chance to connect directly with people from those firms. They also post new openings pretty frequently and help with interview prep. Princeton MFin alumni are literally everywhere — at all the top firms — and that network can really help if you reach out.

Academic Environment:
This was the main reason I chose Princeton. The professors here are incredible — some of them are the ones who wrote the textbooks or research papers we study from. The program is very rigorous and theory-heavy, which I personally like. You also get a ton of flexibility: you can take classes from applied math, economics, computer science, and pretty much any other area you’re interested in.

If you want to get a flavor for research, you can do that in your second year too — one of the advantages of it being a two-year program.

That said, a few things could be improved — for example, I wish Corporate Finance was optional since most students don’t get much out of it. You can also make a case that the program can be wrapped up within 1.5 years as well and 2 years is long. And to be completely transparent, the atmosphere can sometimes get a bit toxic, especially during recruiting season. People are competing for top-paying roles, and conversations often end up revolving around offers and comp. It’s not constant, but it’s noticeable. Still, most people are genuinely nice and helpful — at least in my experience.

Overall, I’ve really enjoyed my time here so far.

The Quantnet C++ for Financial Engineering course is exceptional for mastering C++. It significantly deepened my understanding of C++ concepts, particularly in low-level design. The support from Professor Dr. Daniel Duffy and teaching assistants Avi Palley and Paul Lopez was outstanding.

Completing this course has boosted my confidence in my C++ skills and equipped me to apply them effectively in real-world scenarios.

Exceptional faculty with strong industry and academic backgrounds. You get exposure to a prestigious crowd. The professors also teach at other schools like Princeton Columbia and NYU so the level of teaching is on par with the best.

NYU FRE has amazing career support and a solid academic foundation. From day one, we had access to workshops, resume reviews, and mock interviews that really helped me prepare for the job market.

The courses are practical, and professors bring valuable real-world insights. Overall, the program has prepared me well for my future career.

It is intense, fast-paced, and rewards those who come prepared.

Internship and Recruiting Landscape:
Because the program starts in spring, the traditional fall internship cycle overlaps with coursework, which can limit opportunities. However, the summer internship path has been opening up rapidly, with firms now adjusting to Berkeley’s timeline. This shift is creating a much smoother recruiting experience for upcoming classes.

Career Services and Support:
The new career services team is genuinely committed. They care, they listen, and they are flexible with students’ goals. The career coaches are proactive and knowledgeable, they work one-on-one, provide actionable feedback, and truly try to help each student find the right fit. The office is still evolving, but it’s clear that they are catching up quickly to the expectations of such a high-performing program.

Student Responsibility:
The biggest differentiator here isn’t the program’s competence, it’s the student’s own preparation and clarity. Some students enter without a defined career plan or without the technical or industry readiness that this short program demands. Given its compressed timeline, success heavily depends on knowing what you want and moving fast toward it. The resources are there, but one has to take ownership early.

Overall Impression:
Berkeley MFE provides world-class academic training and an increasingly strong recruiting framework. The faculty and career team care deeply about student outcomes, and with proper self-direction, the program can be an exceptional springboard into quant finance. It’s not an easy ride, but for driven candidates, it’s one of the best opportunities available.

I found the course to be very important and valuable for me to learn to code in a language I had no experience in before. I had done object-oriented programming but it has been quite a while since that I had done that. Lastly, the Black-Scholes, FDE, and Monte-Carlo simulation exercises at the end were very useful applications of what I learned in the course to quantitative finance. In short, I highly recommend the course for those who want to get a solid foundation in C++.