Latest reviews

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++.

The program is a wonderful journey far beyond expectation. Through this program one can learn essential C/C++ programming techniques with fairly steep learning curve and gain experience via application in modeling exercises. Furthermore, TA would provide insightful support on any issues and improves one’s understanding.

The courses is extremely helpful in learning the basic concepts of C++ and programming, with strong emphasis on object-oriented programming.

I took the C++ course to build a strong foundation in programming and understand the core concepts of object-oriented design, data structures, and memory management. Since C++ is widely used in software development, game engines, and system programming, I felt it was essential to learn it for improving my logic-building and problem-solving skills.

The course was very engaging and practical, with assignments and projects that helped me apply what I learned. I especially enjoyed working on coding exercises involving classes, inheritance, and STL, which deepened my understanding of real-world programming in C++.

The course experience was good. I found it relatively simple to follow and complete. I took the course to learn C++ and obtain the certificate.

I chose NYU’s MSFM program over Columbia’s MAFN, now I feel some regret

I come from a math and econ background, I had prepared myself thoroughly before entering the program, stochastic calculus, machine learning, regression analysis, statistics, programming, CTA trading internship(okay cap), qr trading internship(not big names),

For me, the coursework itself is not inherently difficult, but it is extremely time-consuming. Weekly assignments and quizzes dominate the schedule, and in one course, it allocates two-thirds of the grade to weekly quizzes. That structure forces students to devote upwards of around 12 hours a week to just one class. And all the lectures are scheduled after 5pm and the latest one is ending at 10:25pm. Easily forty hours locked into classwork each week if you really want to comprehend the courses. This leaves little space for the most essential component of a professional program, preparing for recruiting, I believe to land a job in quant finance is a common goal for most of the cohorts, same as one of the main purpose of the program, but it’s not.

Career services: never used them, excepts the talk given by some companies, shout out to Ariane.

Updates on application: despite the program’s small class size, only a handful of students even received interview invitations from sell-side banks, and almost all were rejected by buy-side firms. The majority of students failed to make it past résumé screening. This raises a serious question: are résumés being carefully reviewed with input from actual industry practitioners? Or, NYU Courant MSFM has become a history after Peter Carr? From what I witnessed, I have serious doubts.

As for the Director, I never personally saw him in person throughout my time in the program.

My impression of the courses themselves is mixed. Stochastic Calculus was excellent—coherent, rigorous, and enriched by the lecturer’s valuable industry insight. Machine Learning was also very strong. Computing in Finance, views vary. Demanding for those coming from little programming backgrounds, was ultimately fair; the professor was strict but responsible, with quizzes that, while heavy, were clearly designed to uphold the program’s reputation, the lecturer knows exactly what the industry needs. Risk and Portfolio Management: my least favorite course, most of my friends find it fine tho. It attempted to cover a range of topics, from risk measures to fixed income to equity models and etc. To truly master the material, a student would have to dedicate an enormous number of extra hours. Financial Securities and Markets was fine.

What makes this workload particularly frustrating is the comparison with peer programs. I have friends at Columbia, CMU, and UChicago, and they all report lighter workloads except CMU’s. Their programs give students more time to focus on recruiting, some of them provides more resources which is precisely what translates into stronger placement outcomes. By contrast, the MSFM curriculum consumes so much energy that a big part of the students often cannot balance their studies with the interview process, which results in they can do neither well.

Me personally need a lot of time to refine my cv, doing leetcode, greenbook, more stats skills and each of them are time consuming.

I do not see myself as a victim, and as Charlie Munger once said, I do not complain but simply do the work. Still, it is important to acknowledge what is missing here. The program is somehow academically solid, but it is not aligned with the actual needs of students who are seeking quant finance roles. It consumes their energy without adequately supporting their career ambitions.

Don’t forget Courant is famous in strong mathematics, I’ve you have strong mathematics and are considering a phd, this place might suit you well, or why not just do your phd after undergrad? For those who wishes to land onto quant finance role while not exposed well enough to the recruiting process in the undergrad, well you have to work really hard, regardless of which program you choose.

The rise and fall of a program’s reputation cannot be explained by a single factor. However, I believe the role of the director is vital in every aspect. I did see the directors from other prestigious programs are making a scene in building stronger connection with the industry.

I admire those who works hard as hell, I respect that. I just presume there could have been a more efficient way for this program to achieve the students’ goals in career-wise, meanwhile improving this program’s reputation. Application office can be more strict in selecting the candidates . And also, all I care about is a job, that’s the place to learn actually insights. I’m not the smartest person ofc, but I’m persistent, it’s still frustrating to see your got rejected just because of the name of your programs.

I mean there's no program can guarantee your success in landing a job, you have to work hard and hard and hard whereveryou are. But it would be good if there's someone giving a assistance and guidence to save your time, that's the main purpose of my ideal program.

I left two stars rather than one due to the great efforts from the lecturers, Ariane, and Nancy.

I really enjoyed c++ 11 -20. My favorite part was concurrency and parallelism. I took the course to further my coding skills and see some of c++ in a finance setting.

Graduating soon but I’m going to try to give an in-depth and honest review of the program for the incoming applicants. I am generally happy with my own outcome at UCLA and I emphasize this to point out my review is not an expression of personal frustration. I am appreciative of the program, so my hope is my feedback can be used by both future applicants and admin.

Student Quality:

Probably the worst part of this program. Around a quarter of the class admits to having zero prior programming knowledge and I overheard one student say they’ve never taken any calculus before. Also almost a quarter of the program has only a passing interest in quantitative finance and are immediately turned away when they realize the rigor and breadth of knowledge required, and instead pivot to targeting traditional finance roles. Few come into this program prepared with the baseline knowledge required for the coursework or about the industry.

But what’s the worst is the work ethic and culture. For the mandatory career prep coursework before the MFE started, only ~15% completed it by the deadline. The homework load is designed to be handled by a group of 4-5 randomly assigned, but every group I knew of was filled with freeloaders who did not contribute. Cheating on exams is rampant (hint to admin, nobody needs to go to the bathroom that often) and almost everyone’s assignment submissions are thoughtless ChatGPT copy/paste. Lectures are more than half empty and many TA sessions have zero attendance, yet they will complain about how hard the material is, how bad their grades are, and that the MFE didn’t help them find jobs. The department has an unofficial "no Fs" grading policy, which works in favor of many who would otherwise be flunked out.

Despite this, there are many standout individuals who are extremely intelligent and hardworking. The range of quality in this program is shocking, you have professionals with strong international experience somehow in the same cohort as fresh grads who are scared of a matrix and need AI to debug a print statement.

Sadly, behind the scenes the admin has acknowledged that most students here do not have what it takes to work in the quant industry. Admissions should tighten up standards to improve program outcomes, but it’s a chicken and egg problem. Why would good applicants want to join a program with a poor ranking, but the program will continue to have a poor ranking until good applicants join.

Courses/Instructors:

Decent range of electives with generally good quality and depth (the MBA electives are a waste of time and it’s a shame they’re offered, if you want MBA level coursework you shouldn’t be in an MFE) but I’ll focus on the mandatory courses:

Quarter 1:

Investments: The class is a little too basic, spending weeks going over how simple cash flow discounting works, but it picks up faster near the end and gets the job done. Chernov is a knowledgeable and pleasant instructor, but does not come across as very involved. Usually fair assignments and exams.

Financial Accounting: Easily the worst class in the entire curriculum. Accounting being a core class in an MFE is a questionable decision, and while Dill is a pleasant person that responds to feedback, the material is taught poorly and he is also arguably not best suited to teach this course. Individuals from traditional finance backgrounds were getting confused by in this accounting class. This class should be compressed into a 2-week crash course before program starts and replaced with another; dedicating a quarter of the most important semester in the internship search to accounting is a terrible decision. Fair assignments and exams, despite having to pay to do weekly assignments on an online textbook.

Stochastic Calculus: On the opposite side of Accounting, this class is amazing. Panageas is a passionate instructor and this material is challenging but the class is designed for you to pass. His explanations and derivations in class are phenomenal at presenting complicated concepts in simple terms. Challenging assignments but very fair exams.

Econometrics: Another good stand out in the first quarter. Despite Yavorsky coming from a marketing background, he is exceptionally knowledgeable and skilled at presenting econometrics concepts at a technical level. He will not rest until you truly have learned the material, and you will learn a lot of material. Only downside to this class is it is taught in R (including a 1-week R bootcamp before class), let’s let that language die already. Relatively easier assignments but harder yet still fair exams.

Quarter 2:

Derivative Markets: The highlight (or lowlight depending on what kind of student you are) of the entire MFE program. This class is no longer the cake walk described in earlier reviews, it is extremely rigorous and technical and builds on Stochastic Calculus. Reiner is a passionate instructor who knows almost everything there is to know about derivatives, although sometimes he has trouble communicating it to us who are seeing most of this for the first time. It also feels like we are trying to learn too much in too little time or not spending our time on some material wisely. The TA sessions for this class are also a standout in terms of expertise and difficulty, one of the very few TA sessions attended by most students. The assignments are usually very hard and lengthy, as well as the exams. Students perform so poorly, a 0 on his midterm got you a B+ after the curve this year.

Empirical Methods: An extension of Econometrics which is taught by Lochstoer who again is very knowledgeable and pleasant individual. Useful material, but the class doesn’t stand out either positively or negatively. Maybe a little bit too much time spent on old topics like Fama-Macbeth but otherwise a good class. The assignments and exams are of medium difficulty.

Fixed Income Markets: Picks up where Investments finishes off. While it starts again quite slow, it goes into great technical depth of fixed income pricing. Longstaff is an expert, and his strength comes in explaining these hard concepts in layman’s terms during the lecture, then having us go on to implement these ideas in more technical ways during assignments. His assignments vary from easy to hard (but always fair) and his exams were on the harder side.

Trading, Market Frictions, and FinTech: This is an MBA level course that doesn’t go beyond the surface level of a variety of topics and was considered a running joke amongst the entire cohort. Zhang is again friendly and approachable, but her demeanor doesn’t compensate for bad material. I understand the intention behind a class like this, but it needs to be revamped and increased in depth to bring it to the level of quantitative finance. Extremely easy assignments and exams, just forget this class exists.

Quarter 3:

Risk Management: A useful course taught by Haddad. Haddad has an important quality of being able to explain why this material matters and will schedule extra time out of his day to help the class succeed. The main criticism is that it feels like the whole class is just spent talking about different variations of VaR which gets quite repetitive. Medium difficulty assignments and exams.

Data Analytics & ML: A continuation of Empirical Methods taught again by Lochstoer. More interesting but still relatively foundational topics discussed such as non-linear models, otherwise runs exactly like Empirical Methods. This was recently made a mandatory course, which I believe is a positive step for the program. Same difficulty as Empirical Methods.

Quarter 4:

Applied Finance Project (AFP): AFP can be hit or miss depending on the firm you are anonymously selected by and the work group you have formed. This isn’t a class in the traditional sense.

Career Services:
Antoine runs the career team with Jeremy assisting on the administrative front. Both are extremely approachable for help or advice, and Antoine’s strengths lie in creating and rebuilding the alumni networks (alongside Leanna in the admin). There is an annual NY career trip that has been moved to October, to be more in line with recruiting timelines. They also have a resume book and occasionally invite industry speakers, but the career impact of these efforts is not very strong.

Unfortunately, that’s where the positives of the career team end. Nobody has any first-hand experience with quantitative finance, and at times their efforts may even be detrimental. This cohort was forced to attend 9 hours of mandatory MBA coaching on “how to build a story in your interview”, something that would probably get you laughed out of a quant interview if you couldn’t pass the technicals. They do organize technical sessions (QIPS) but these are infrequent, loosely structured, and don’t fulfill the technical preparation needs. Targeted technical preparation is the key to passing quant interviews, and there isn’t anywhere near enough emphasis put on it.

The people in the career team are amazing individuals, but they need a hands-on expert who knows what they’re doing in this industry. Don’t rely on the them to be anything other than a supportive shoulder to cry on. Career outcomes are weak and have been getting worse over time, most graduates don’t go on to work in quantitative finance at all.

Overall:

UCLA’s MFE ranking is deflated by the poor admit quality and career outcomes, but the program instruction is still relatively competitive (top 10 QN peer score, actually above MIT and NYU Tandon). Admin is composed of great people, acknowledges most of its weaknesses, and are attempting to make some changes, but it’s not enough. It also seems like they’re hampered a lot by the department, maybe this is the business school (dis)advantage they always advertise. At the end of the day, this is just another cash cow program targeting internationals just like almost every other MFE out there. Potential applicants, keep in mind what’s hidden in these rankings is there is a selection bias; having met individuals in all MFE programs, it’s not as if Baruch or Berkeley will transform you into expert quants after a year of coursework, they simply tend to admit only those that have the highest probability to succeed in the industry in the first place.

If you are an international applicant, are passionate about breaking into the US quant finance industry, are willing to put in a lot of work inside and outside the classroom, and are able to pay one of the highest tuition fees (and worst scholarships) amongst MFE programs, UCLA’s MFE will get your foot through the door. If you do not tick all these boxes, do not go here.

This last piece of advice applies not just to UCLA MFE applicants but all MFE applicants, perhaps consider a technical non-MFE graduate degree instead (CS, Stats, OR, etc.); something you won’t be told a lot is that all MFE programs are blacklisted at many top quant firms.

I found the course to be incredibly educational and rewarding. The problems were quite challenging, and the homework assignments required a significant amount of time and effort. I often spent hours researching C++ concepts on platforms like Stack Overflow and even GitHub. While demanding, this process greatly deepened my understanding of the material.
Personally, I am stronger in mathematics than in computer programming. The math in this course was very manageable — in my opinion, anyone with a solid grasp of arithmetic and algebra would be well-prepared. The greater challenge lies in the programming. I recommend having a good foundation in C++ before starting, as the professor moves through the material quickly. He is extremely knowledgeable, though the online lecture format can make it harder to ask clarifying questions in real time. The discussion forum is helpful, but I also suggest reading the recommended books if you don’t have prior experience with C or C++.
Students who already have a strong base in C++ — or who are comfortable seeking out additional resources independently — will thrive in this course and likely find it easier to build upon their skills.
The applications to quantitative finance were particularly fascinating and easily my favorite part of the class. I initially enrolled in this course because I plan to pursue graduate studies in quantitative finance. Several universities, including Carnegie Mellon, specifically recommended this course for students without prior C++ or object-oriented programming experience. I wanted to strengthen my programming background, knowing that C++ is fundamental to quantitative finance due to its speed and flexibility.
Overall, this course pushed me to grow, challenged me to improve, and humbled me in the best way possible. It not only improved my technical skills but also reinforced the importance of perseverance. The TA was incredibly supportive and made the learning experience much more approachable.

I have been enrolled for one month, and my study experience is very good. I believe this program is underrated. The lecturers are excellent, and the courses include a lot of math, which is a great complement to the courses I took during my undergraduate studies. I've heard that the program is particularly strong in the commodities, but I do hope the career services can give more resources and industry connections. Overall, I'm looking forward to growing with the cohort and making the most of what the program has to offer.

I took this course to prepare for a quant finance master's that I just started. Because of it, I'm having no problems with any of the programming assignments in my courses. This course also covers a lot of stuff that people don't really cover in usual programming classes, like generators. Knowing about the intricacies of the language allows me to make much better programs, especially for scientific computing. The case study was also a great addition to the course, as it helped me assemble everything and make something cool.

I took this course primarily to refresh my understanding of options. The study materials, quizzes, and homework are exceptionally well designed. They explain complex concepts clearly and demystify many aspects of options, making them more intuitive and approachable with effort. The course strikes a strong balance between mathematical rigor and practical relevance, making it valuable not only for pre-MFE students but also for professionals in the industry. TA is also very friendly and helpful, the community is amazing and supportive, you will find many good experiences and tips shared by previous students. Overall, really enjoyed this program.

I found the course very helpful to get a more broad and in depth understanding of options and its stratergies. It is a good prep material for interview and also serve as a well balance introductory course for harder option courses in MFE's programme.

I previously learned C++ in my university courses, but I had no idea how to apply it in the financial field. Baruch’s "C++ for Financial Engineering" course taught me how to integrate C++ with finance.

To give a summary of the program's main features and bugs:

Good:
- Highly flexible. You can truly do whatever you want with this degree -- whether you want it to be completely quant focused, or basically an MBA.

- Extremely sharp cohort. Many of your classmates will be among the best in their country academically. This includes countries like India and China with very intense competition. As a result, you will constantly be surrounded by excellence. Pro tip: even if someone seems unassuming, they probably much more talented than you realize.

-Very strong professors. Admittedly, you have to find out who the good ones are, but you have some of the best people in the world teaching you certain subjects. We've had former heads of institutions like the SEC, IMF, etc come and teach. Also heads of divisions at major hedge funds. Many parts of modern finance theory were invented at MIT, and the professors who developed them still teach here.

-Value from other programs. If you are early career/pre experience, most of the other people in Sloan will have much more work experience than you. This means you get a disproportionate amount of value talking to these people, especially those in your target industry. It's business school access for pre-experience folks.

-MIT Sloan brand name.

-Boston as a city is awesome. Beautiful and walkable. Just very expensive.

Bad:
-you have to guide your own recruiting journey. Not much handholding here (as there is sometimes in some other programs) -- figure out your timelines, do your coffee chats, and send in your applications. You should really aim to connect with other students in the field here if you can.

-not as many resources around quant prep. No one else at Sloan recruits for this, so there's not many resources around it. Your best bet is talking to other MFins, potentially in the class above you.

-initial summer classes suck. Way too much work and not enough reward. Especially since it's during recruiting season. It sucks but is not representative of the rest of your time at MIT.

Pro tips:
- Judge a course by 1. the professor and 2. the content, in that order. A good professor can completely change your experience (and understanding) of a course. Make sure to gather info on the best professors before you start the program!! This is probably the most important things you can do to make sure that you get the best value out of this program academically.

-Less is more. look around if you want, but at some point, choose no more than 2 student clubs and 4-5 classes to focus on. Do them well. If you get greedy and try to do more, you will not learn any of them properly.

-Take advantage of clubs for recruiting. Especially traditional fields like IB/PE/Consulting. People in these clubs (MBAs) probably work at your target firms. Talk to them.

-prioritize recruiting above classes the first summer. Eat the B, focus on your applications. Trust me, they matter a lot more -- the effort to reward ratio is not high in the initial term.

-Don't stay in the Sloan bubble. Get involved with main MIT campus (whether through class, clubs, etc). Take a Harvard course. There's cool stuff there and is worth checking out while you have the opportunity to do so.

-get winter clothes. Black Friday is a good time to do so.

The end goal here should be to get set up for a good career and to enjoy your time here. Make sure to not ignore that second point by over-focusing on the first point.

Do what needs to be done, then do what you genuinely would like to do.

As a Financial Technology and Analytics student, I have found the Stevens FTA (Financial Engineering Stream) program to be both rigorous and rewarding. The curriculum has a strong balance of finance, programming, and applied analytics, which has allowed me to build a solid technical foundation while also gaining exposure to real-world applications.

Professors Hatzakis and Florescu have been excellent mentors throughout the program, both highly knowledgeable and approachable. Their teaching style goes beyond theory, making sure students understand the intuition behind complex models and how they connect to practical finance and risk management problems.

One of the biggest strengths of the program is its emphasis on hands-on learning. Access to the Hanlon Financial Systems Lab and exposure to real market data has been invaluable in developing the technical and analytical skills needed for today’s industry.

Career development support is also strong, resume reviews, interview prep, and networking events have been helpful in aligning academic experience with career goals. Being so close to New York City adds another advantage, with plenty of internship and networking opportunities on Wall Street and in fintech.

While the workload can be intense, the experience overall is highly rewarding, and the program is constantly evolving to stay aligned with industry needs. I would highly recommend this program to anyone looking to combine finance, technology, and analytics for a career in quantitative finance or fintech.

I wished to take my first step into the world of quant, searched online for a good beginner's course and ended up with this one. It's been a fruitful journey and it really helps you get an idea about what exactly is going on when we talk about financial engineering.

I stumbled upon the course through Baruch's MFE program prerequisites, but chose to follow through with it after seeing great positive sentiment online. I found the structure of the course to be great, with the idea of learning through previous students' threads to be a lucrative aspect of it. It engaged discussion and allowed me to see peoples' though processes unfold.

I found the course through my father who recommended it to me. I decided to take it to brush up on my C++ skill and apply it to finance. I found it interesting and it helped push my C++ and quantitative finance skills further.