Latest reviews

I think this program is probably the best MFE you can find in the UK. I highly recommend it, especially for students who are certain about pursuing career in quant finance.

In the UK, there aren’t many financial engineering programs to choose like the US. Some people compare this one with Oxford. But aside from the title, I genuinely think this program is more worthwhile. It’s slightly longer in duration, offers more electives, and the timeline is more in sync with industry recruitment.

The curriculum covers nearly everything you would expect for quant. It still focuses primarily on sell-side pricing, lack of ML or deep data science, but still includes those basic ideas. One of my favorite courses was Market Microstructure, which was excellently taught.

The workload is intense, especially in the first term. If you don’t have a strong background in math or CS, it can feel quite overwhelming. There’s a lot of material packed into a short time. But that also means you learn fast and grow quickly, and it’s very rewarding if you’re committed.

On the career service side, every week in the first term there are two career events, practitioners and company presentations. The program is well-known in the UK industry. Of course, getting a quant offer still depends on your own preparation and skills, but this program definitely helps you get closer.

You’ll also meet a lot of like-minded students, many of whom are serious about the field and willing to push themselves. The atmosphere is intense but also motivating.

There’s one thing I would suggest to improve is the timing. The program starts quite late, around the end of September. By then, some have already started applications, the long summer feels a bit wasted.

Review for QF program at SBU.
For context, i have computer science background with some fundamentals in engineering and mathematics, machine learning, linear algebra, probability theory. I wanted to pursue the field of quantitative finance and work as a developer in the field. Hence started to pursue masters in financial engineering at first and then transferred to SBU after a semester for having a chance to pursue phd in the field.

Review of Masters program.
1. Though NYU has a financial engineering program, it felt as if only few courses are aimed for understanding the field, which can mostly be learned from hull book, i liked the financial computing course in c++ apart from that we have random courses and electives like news analytics as well. Its more targeted for understanding financial products, than mathematical analysis they would only teach exisiting proven knowledge.

At SBU the course structure is vastly theoretically both core courses and electives in the field of applied mathematics and qf related courses, like even in risk management course, we were thought theoretical basics of time series analysis and data, derivations etc. i liked stochastic modelling course and operations research, probability theory course, we have measure theory and sigma algebra taught for advanced prob courses. Linear algebra for computational methods and optimization. Ofc given all this ml courses were good as well, also statistical learning. It was amazing learning experience.

2. For job search i am sure nyu has more options and better reach out and interview prep, referrals than sbu. Sbu has good courses and projects but less focus on career opportunities. But because i had cs background it was easier for me to focus on general problem solving and leetcode. I cant say the same for my cohort at sbu most are non cs engineering (mechanical, civil etc) or finance background. But for someone who knows what they are doing it should be fine for interviews and its mostly role dependent. But nyu grads are more targeted for risk management roles SBU grads not so much its more diverse.

3. The faculty at sbu is amazing, robert frey, andrew mullhaupt they are genius with real time experience. Eugene finberg he is probability theory expert. Many others, i am sure NYU also had good faculty but i like the depth of sbu faculty better for qf related courses.

4. My biggest complain at sbu is admissions dept,i am honestly not impressed with the cohort overall. There is a gap between the faculty and people who admit students. I am not sure if its changed now. But previously i see some students, who have no clue why they are in this area of study. Like some have finance exp, some are “ml” experts but clearly its quite basic. Although i have seen some extraordinary people also especially in phd dept, a better cohort always helps.

5. School culture, the dept together can benefit for more events centered around some clubs and projects or competitions. It can be good networking opportunities, i know they are doing alumini connects and do have good stuff it can do better like some poker tournaments or hackathons.

6. Overall can’t complain much, end of the day its all about how serious can you prepare and crack the interviews no matter how good the school is, but i felt there should be more people with serious roles in industry.

Thanks

Putting disclaimer upfront, I wouldn’t comment on specific courses or professors, partly because I graduated a decade ago and things changed or escaped me, but more importantly casting lights into corners isn’t that helpful when the real need is an accurate picture of the overall landscape.

Sure. One can complain about the faculty, curriculum, career service, or tuition. But you would probably heard similar things if you talk to students of most other programs. And the almost perfect programs are just too selective to be broadly relevant.

The real question comes to understanding the programs, picking the fit one, and leveraging it to the best. The same program always have successful graduates every year, and can never ensure success for everyone, actually for anyone. It’s just a car, with its pros, cons, and price. We are the drivers, responsible for maneuver and navigation. Therefore why don’t focus on picking the best courses/professors and let the dean worry about the worst?

Looking back, I’m grateful for its quantitative rigor, diverse curriculum, and unparalleled location. Over the ten years, I also witnessed its improvement in candidate caliber, curriculum quality, and career service. I am giving a 4 star rating because it obviously has room to evolve, but the runway it paved is enough for takeoff.

The admissions committee generally does a solid job selecting capable candidates, and the curriculum itself is strong. I genuinely learned a lot here. The only thing I wish was different is the overall competitiveness of the cohort—if the classroom environment had been more consistently strong, this program would have been even better.

The professors are knowledgeable and experienced, but this is not a program where you can sit back and expect everything to be handed to you. You need to ask the right questions and actively participate. Many undergrads in Stevens QF are extremely sharp, so interacting with them can help you understand where you personally need to improve.

A big warning: people who come expecting an “easy U.S. master’s” get overwhelmed very quickly. The content is math-heavy, coding-heavy, and dense. A lot of students end up relying on AI tools for assignments and projects. Don’t do that. Use AI only as an assistant (debugging, explanations, intuition), not as a solution generator. You’ll be cheating yourself in the long run.

One thing I feel strongly about: self-study is the only way to truly extract the value and ROI from this degree. If you’re a complete beginner in math, coding, or quant finance, this program will feel extremely intense unless you put in serious work outside of class.

There are also some structural limitations. A single 2.5-hour class per week makes it hard for professors to cover everything in depth. Many lectures feel compressed, and difficult topics get skimmed over because of time constraints. I really wish the program had:

* Recitation classes like in undergrad
* More problem sheets or practice sets
* Two classes per week per course instead of one
* More structured quant interview prep
* More consistency in course difficulty across sections and instructors

Another reality: *18 months is not enough* to deeply cover all the math, coding, stochastic calculus, derivatives pricing, and quant interview theory required in the industry. So don’t be afraid to ask professors if something feels unclear. Whether you lack the background or the topic moved too fast, they will help you or share resources. And if it still isn’t enough, the TAs are usually very supportive.

On the career side, it’s important to set expectations clearly. **More companies open roles for Stevens undergrads than for graduate students**, especially in quant and technical roles. Career services are, honestly, below average. They provide basic support, but you cannot rely on them for meaningful recruiting outcomes. Networking, alumni outreach, and building strong projects matter far more.

The overall student quality is mixed—some exceptional people, but many students are underprepared or lack the motivation expected at a quant-focused graduate program.

Overall, Stevens MFE is a good program if you are self-driven. If you rely only on classes, expect professors to spoon-feed content, or hope career services will secure your job, you might struggle. But if you proactively study, ask questions, build projects, and push yourself, the program can absolutely deliver strong value and prepare you for quant, QR, QD, or data-heavy finance roles.

I’m currently an MFin student at MIT Sloan, and I wanted to share my honest experience for prospective applicants.

What I like about the program:

Unmatched Academic Rigor and Flexibility: Cohort is full of smart and hardworking folks. I did not feel the same when I accepted the admit but people are competitive (in a good way), which motivated me a lot. At the start, I felt that the program is more focused on non-quant side and there are very few people who are targeting quant as MIT’s MFin is oe of the few programs that offers you a wide range of concentrations and tracks one can take. But there are many people who I found to be quant focused including the curriculum's deep dive into quantitative finance, machine learning, and analytics.

Access to Research & Industry Practitioners: Professors are world-class and approachable, often bringing in leading industry executives and alumni for seminars, guest lectures, or networking. You can ask any type of question in the class and it will always be encouraged, including the basic ones. Some professors are amazing teachers.

Collaborative, Ambitious Community: The cohort is diverse and intellectually curious, with classmates from elite undergrad and professional backgrounds worldwide. You’ll find plenty of group projects, student clubs (quant finance, fintech, entrepreneurship), and networking events. For me, group projects helped me learn a lot of non-quant things like Accounting, Corporate Finance methods etc.

Room For Improvement:

Career Support: The CDO (career development office) is proactive but you'll have to do most of the things related to internship or full time recruiting. They do offer dedicated resume workshops, mock interviews, and direct connections with top-tier firms in both buy-side and sell-side finance roles tho.

Training Before Program Start: People who come from CS/Math or related background will find it tough to cope up with a couple of courses in the summer as you find many new concepts being thrown at you, and you have to learn it pretty fast. If you have done Eco/Corporate Finance/Quant Finance or similar tracks before, it will come very easy for you to grasp things in class and ace exams with 30 min of study a day before.

Pro:

- You can shape the degree however you want, amazing flexibility starting in the fall term. If you want something closer to a finance-focused MBA, you can do that. The structure lets you build your own path.
- You are surrounded by very impressive people.
- Once the fall term starts, the teaching quality really stands out. Some professors genuinely change the way you think about finance, risk, incentives, and markets. The right professor can turn a class into one of the highlights of the program.
- Being around MBA students and PhD students with more experience gives you a lot of value. You can join clubs, explore different areas, and even take classes outside Sloan. The environment is rich if you want to take advantage of it.
- MIT brand: The name helps. It opens doors and people immediately recognize it.
- Boston is beautiful

Cons:

- Recruiting is self-driven; there is not much guidance. You need to drive your own process, be proactive, and stay on top of timelines. They could help you more. I was a little disappointed by that.
- The summer intro term is heavy and not particularly rewarding. It also overlaps with early recruiting, which makes it feel even more stressful. Thankfully, the real program feels completely different once fall begins.
- High workload

Overall, I am satisfied with the program- I received what I expected from it.

Admission was straightforward: submitting the required documents and a brief written response at some point. Nothing unusual or tricky in the process.

The courses are taught at a generally very strong level, and I agree with previous reviews noting that students really do take a lot from them. My only small criticism is the heavy emphasis on stochastic calculus- officially two core classes devoted to it, whereas I personally believe one would have been enough given how many other courses rely on it indirectly. My favorite classes were Monte Carlo Simulation with Ali Hirsa and Volatility Modeling with Amal Moussa.

Career services were supportive and encouraging during both internship and full-time recruitment. They shared opportunities that are not always visible to everyone and offered useful suggestions. Nevertheless, it ultimately comes down to students to secure positions through interviews. The Columbia brand opens doors, but not all of them.

Classmates formed a strong group- both international and domestic students were with impressive backgrounds. The alumni network can be very helpful if used well.

Overall, I got what I wanted from the program and feel it gave me a solid foundation for my career. I don’t think it is the absolute best program, but there aren’t many that are meaningfully better- perhaps with the exception of two.

King’s College London is a great university to be at, and the Strand campus, where this programme is taught, is a brilliant place to study. Overall, this is a good course in terms of the content: it enables students to develop advanced financial knowledge while further strengthening their mathematical abilities. It teaches you how to apply mathematics to finance, and how to utilise machine learning and programming languages such as Python and C++ to support this. In addition, there are many opportunities, both within the course and through wider university events, to meet and hear from industry professionals.

The course itself is very challenging, and although the structure is quite good, certain improvements could make it significantly better. There are good modules available, but it would be better if there were a wider selection. The dissertation at the end allows students to work in a very practical way, putting much of what they have learnt into practice while continuing to learn more.

Overall: Don't come here if you are expecting GT to carry you to landing a top job. You have to earn it. Classes are fine, but you'll do most of your learning by reading textbooks and working on projects. Career services provides many opportunities to network, but it's on you to end up with the job you want.

First semester:

Classes: Finance & Investments, AI for Finance, Stochastic Processes in Finance, and Design and Implementation of Systems for Computational Finance (Python and C++).

This semester has a very heavy workload and you will spend most of your time working on projects for the AI class. Hard to balance workload with internship search. Stochastic Processes is hard material and the teaching style doesn't land for a lot of people, so you have to put in a lot of outside study time to actually learn anything. Class averages for the tests were in the 20s usually. Computational finance gives a brief intro to C++ but then switches to Python half way through the semester. If you want to really learn C++ you will have to take an elective class or do some outside projects.


Second semester:

Classes: Derivatives, Fixed Income, Numerical Methods, Data Mining.

Much lighter than first semester, and you will have to shift from a project-based workload to a study-based workload. Limiting use of LLMs to help with homework is really the only way to learn a lot this semester, since most tests allow a cheat sheet or are open notes/ internet. Coding projects allow use of any language, but most people use python.


Third semester:

Classes: Practice of QCF (capstone), electives.

Practice of QCF class is setup where you only meet a few times throughout the semester. You just pick a topic and do a semester long project on that topic. It's important to pick a group of people that you can work well with and are all interested in the topic. Dr. Chava did a good job at providing opportunities to present your work, and brings in good guest lecturers, but the communication was terrible. Make sure to pick electives that you are interested in, and try to pick at least a few easy ones so you can focus on your job search.


Career Services: Extremely helpful as long as you stay on top of your applications and communicate well. They provide multiple opportunities to meet top employers, but it is in your hands to network well to land a good job.

There are two distinctive strengths of this program. First, the school is well known for its asset pricing research, and the program brings in leading faculty members to teach the core courses. I’ve also seen the faculty consistently engage with the industry to gather feedback and refine the curriculum each year. Second, the program tends to select genuinely kind and collaborative candidates during the interview process. Instead of cut-throat competition, you feel a strong sense of mutual support, which sets this program apart from many others.

Regarding career services: the positive side is that the advisors are supportive and encouraging. They offer guidance on salary negotiations, help communicate with firms when students need extensions, and the program has built strong relationships with several AM/HF firms over the past decade.

That said, there are two areas that could be improved.

1. Advisor coordination:
a. Some advisors have limited or no industry experience;
b. There could be more advisors dedicated specifically to firm outreach and proactively sharing first-hand recruiting information;
c. The advisors managing employer relations should communicate more closely with the advisors who interact with students most frequently.

2. Professional preparation:
The program could invest more in the peer quant mentoring program, allowing senior students to share recruiting insights and run mock interviews for first-years. It could also adopt a model similar to Princeton’s, where industry mentors play an active role in students’ recruiting preparation—funding these practical initiatives rather than hosting too many purely networking-oriented events.

Overall, it’s an excellent program—and I did secure a top-tier AM internship and a HF full-time offer through it.

The following review is subjective and is most useful for students aiming to seek employment in the U.S.

Courses:
The program offers great flexibility, and professors are generally very nice. What you gain will largely depend on the courses you choose and how seriously you engage with them. Keep in mind that most courses may not directly help you during job interviews, but they can be valuable for building your knowledge base.

Career Services:
Resume drops can give you more exposure to employers, but whether you ultimately get interviews or offers depends heavily on your own background and skills. Some students get interviews from resume drops, and some don’t. Do not expect too much from career services.

Job seeking:
Employment statistics don’t guarantee anything. You could end up in either the numerator or the denominator of that employment rate. If your goal is to work in the U.S., you will need to figure out your own strategy based on your background. I would give the program a neutral rating in this regard. The program provides a reputable name on your resume, some industry connections through alumni, and a valid U.S. visa with work authorization if you are an international student. However, it is a highly autonomous program, and your outcomes depend largely on your own efforts.
This is a decent program for students with strong backgrounds, self-awareness, and determination. Nothing much to praise, nothing much to complain. In other words, it gives you a chance to be seen by Wall Street, but you will likely have to learn how to stand out on your own.

Other notes:
For those want to experience nature and country-life, Ithaca has its beauty.

The Master’s in Financial Engineering program at Lehigh is a strong, well-designed program that prepares students for a career in quantitative finance. The courses are flexible and focus on cs, statistics and finance.
This program will provide students capstone projects to work on quant related, hands-on project. Students get to work directly with industry sponsors, making the experience feel real and relevant.
Networking is also a major part of the program. There are plenty of events, guest talks, career panels, and conference that make it easy to meet alumni and professionals. These activities are especially important for me. They give me a chance to practice communication, build confidence, and improve my social skills.

The Master’s in Financial Engineering program at Lehigh is an outstanding program that fully equips students to succeed in a career in quantitative finance. In addition to its rigorous coursework, the program emphasizes hands-on project experience, allowing students to collaborate with industry sponsors throughout the duration of their studies. Networking is also a central component, giving students ample opportunities to showcase their skills to industry professionals. Overall, the environment is incredibly supportive, the coursework is on par with top programs, and what truly sets Lehigh apart is its exceptional project-based learning and industry engagement.

My experience at Fordham University has been deeply rewarding, both academically and personally. The curriculum—particularly within the quantitative finance and analytics disciplines—strikes a thoughtful balance between theoretical rigor and practical application. Courses are structured to push students beyond formulaic understanding, encouraging critical thinking, technical precision, and the ability to translate complex models into real-world decision-making. This approach has been instrumental in shaping my development as a quantitative professional.

The faculty at Fordham deserve special recognition. My professors—particularly Prof. Sheng—have consistently demonstrated exceptional mastery of their subjects, coupled with a sincere and enduring commitment to student success. Their guidance extended far beyond lectures—through office hours, project mentorship, and career advice, they fostered an environment where curiosity is met with encouragement and academic challenges become opportunities for growth. Their support has played a significant role in my confidence and progression throughout the program.

Equally impactful has been the community of fellow classmates. I have been fortunate to learn alongside peers who are driven, intellectually curious, and collaborative. Whether discussing derivative pricing, debugging code, or preparing for interviews, the shared motivation and mutual support within the cohort created an atmosphere that made even the most demanding coursework engaging and enjoyable.

Overall, Fordham University provided an enriching experience that strengthened both my technical skill set and professional maturity. I am grateful for the education, the mentorship, and the friendships formed during my time here.

Overall solid program

The cohort is very selective—students are smart, hardworking, and generally come from strong STEM backgrounds.

Academics:
The coursework provides a strong and relevant foundation in quantitative finance. It’s not overly career-driven like some other programs that tailor content toward interview prep, but the professors are professional, dedicated, and genuinely invested in teaching. You can learn a lot if you’re willing to put in the work. Some courseworks can get niche/too hard that it doesn’t help with your general job searches, so be careful with your course selection.

Career services:
This is where the program falls short. The career team doesn’t source many real opportunities; instead, they mostly host mandatory info sessions where company/ppl brand themselves, and students have mandatory follow-up essays to write to prove how much they “learnt”. I heard they improved a bit after my class’ graduation in 2023, I really hope they do. Most cohorts I know found their jobs on their own. The biggest career advantage is simply being in NYC, where networking and opportunities are more accessible.

Final Thoughts:
If you’re excited about quant finance, want to build a solid technical foundation, or just want to experience NYC, this program is a good choice. But be ready to push yourself through a lot of challenges—some meaningful, some not—and expect to drive your own job search. Being in NYC definitely helps if you know how to take advantage of it.

I earned my Quantitative Finance (QF) degree from Bayes Business School, and I would highly recommend it to anyone considering a career in finance or quantitative roles.
The courses that impressed me the most include:

Derivatives
Stochastic Calculus
Advanced Stochastic Processes
Simulation Techniques
Financial Modeling
Risk Management
Fixed Income

These modules are exceptionally well-structured and highly practical, especially when preparing for the job market. The modeling skills and techniques I learned during the program are widely applicable in my current role, both in theoretical aspects and practical implementation such as coding.
This degree not only helped me secure a job but also transformed my approach to problem-solving. Lecturers provide comprehensive notes, materials, and research papers, and they are always willing to give detailed explanations if you ask questions proactively. The program opens the door to the world of quantitative finance efficiently while leaving room for further academic pursuits, such as a PhD, if desired.

I’m a current MFE student (graduating May 2026) with a focus on energy. I came into the program from a finance undergrad with strong motivation but a clear need for deeper mathematical fundamentals, real research experience, and better industry access. After 1.5 years in the program, I’m very happy with my decision to come here. Below are what I consider the strongest aspects of the program, in order, though the lower points are not weak by any means.

1) Community & Culture

Beyond academic rigor, the professors genuinely care about your success and take a personal interest in helping you reach your goals. If you are proactive about your education, they respond exceptionally well -- guiding you toward the projects, topics, and skill sets that matter most for your career.

The cohort is also large and diverse. We are one of the largest across MFE programs ranked on QuantNet. You meet people with different backgrounds, strengths, and industry experiences. These classmates become your study partners, project teammates, and, eventually, part of your professional network. People here work extremely hard, and I’m confident I’ll stay in touch with many of them for years.

The programs administration team are equally supportive. They consistently approach student requests with a “How can we help this student get where they want to go?” mindset from the top down. Whether that means adjusting schedules to accommodate a job, helping with cross-registration, or advising on long-term career goals.

2) Academics & Research Opportunities

The curriculum is rigorous and flexible. Industry-practitioner faculty are extremely strong, and more excellent professors have been joining recently.

If you want deep theory, you can take multiple PhD-level classes that count toward the MFE. If you want practical quant research experience, opportunities are abundant.

You can also cross-register for courses across schools in NYU: Courant, Data Science, Stern, and others. This adds a huge amount of breadth and depth that many other MFEs simply don’t offer.

Importantly, the program is what you make of it. If you are genuinely passionate and actively involved in the subjects you came here to study, you will get enormous value. If you lose interest but still expect top outcomes, you might not have the same experience.

Many of the practitioner professors also go beyond teaching — they help students find internships, refer them for roles, and in some cases hire them for part-time or full-time work.

Professors who have made a particularly strong impact that I'd recommend students learn from are: Bouchouev, Ivan, Yao, Conklin, Shimko, Tang, Perry, Abousallah.

3) Careers

Career support is a real strength at Tandon, especially if you’re willing to put in effort and engage early.

Our careers director, Sara, works incredibly hard on behalf of the students. She supports networking, interview preparation, and overall positioning for competitive roles. The internal job portal she curates is excellent. Many of the strongest roles each year (hedge funds, banks, trading firms, quant/data science positions, and more) come through it.

I will be joining an oil major in their trading arm, which was a perfect match for my energy focus and research interests. I believe the program prepared me extremely well for this path.



The NYU Tandon MFE program rewards motivated, curious, proactive students. If you embrace the coursework, leverage the research ecosystem, and engage with the community, the return on investment is high.

I’m grateful I chose this program and would make the same decision again.

The course load is quite heavy, with about six or seven courses per semester, and the courses are generally more sell side or academic and It requires balancing academic studies with preparations for finding internships in the industry.
The program has an excellent reputation in Europe and a rich alumni network.Every week, some companies will give presentations, etc. (but they don't offer internal job openings).
Overall, the program is helpful for entering the industry, but in recent years it seems that the job resources it provides have been gradually decreasing. Most graduates still need to rely on their own preparation and applications, and the program doesn't offer much assistance upon graduation.

You get what you pay for. 95% of this course can be completed from home. Some of the Autumn modules results are based on an exam in summer that coiunts for 100% of the grade. Only 1 od the modules actually gives you "practical" assignments (only 2) that have anything to do with what a career in finance might entail. Its very theoretical. Some maths modules very difficult if you are not from a Maths/Physics background, the coding modules are basically "intor to Python/C++ 101". Two of the more interesting modules have had their lectureres replaced with TAs who cant even read the slides. This course is a massive disappointment and feels more like a few modules from different courses were thrown togetehr to create a degree. The Mathematical Finance MSc is much better and you might gain some knowledge/skills from that.

## Curriculum 4/5

The materials covered in the program are well-structured and comprehensive. It goes into details and deducts everything from the ground up. For example, for econometrics, I have utilized all linear regression techniques and time series models before I went to the program, but the class started from the derivation of OLS estimators and mathematical proofs. This approach helped me to understand the concepts more deeply and apply them more effectively in real-world scenarios.

However, I heard lots of students complained its too academic and theoretical, even with course projects, they still feel hard to connect the theories with real-world applications. Personally, I never find it hard to connect theories with applications, but I do agree that the program could benefit from more practical examples and case studies to bridge the gap between theory and practice.

## Delivery 2/5

Although I enjoyed the materials covered in the program, almost all class notes and slides are well-prepared and cover everything in details. But the delivery of some courses is not satisfactory.

I attended every class in person (mostly, LOL) and take notes carefully, I got full-gpa in the program, but I still feel like I self-studied half of the materials in some classes.

## Students 3/5

To be honest, student quality is not the best thing about Rutgers MQF. I maybe biased to say this, my batch is realitively strong compared to previous year and next year, we got several students from top undergrad programs, and I could see some smart and hardworking students in my batch.

But the standard deviation is huge. There are also students who basically do not care about the program, they barely study and cheat in exams!

The program is intense and demanding! The coursework is actually really heavy and tough, if you truely want to achieve a good gpa, you have to put in a lot of effort. But some students just just gpt through the program with minimum effort, they could still pass the classes, and give a impression that the program is extremely easy to pass (which is true for them). In short, its easy to pass, but hard to excel.

## Career Support 4/5

The career support provided by Rutgers MQF is decent. Some career events are not meeting my interests, but overall, the career services are helpful. Let's face the reality, in recent years, the competition for quant roles has become increasingly fierce, most of time you are relying on yourself, not the school, to land a good job.

I believe that the program has tried its best to provide career support, but ultimately, it's up to the students to leverage these resources and put in the effort to secure their desired positions.

## Summary

I truly believe I learned a lot from Rutgers MQF, the program has equipped me with a solid foundation in quantitative finance, and I feel more confident in my abilities to tackle complex financial problems. I would recommend this program to anyone looking to deepen their understanding of quantitative finance.

But at the same time, I put my effort to self-study and practice outside of the program, which I believe is essential for success in this field. And some students in the program may not share the same level of dedication, which can impact the overall learning environment.

If you are considering Rutgers MQF, I am sure you will benefit from it, but be prepared to put in the effort to make the most of the program.