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University of Chicago - Master of Science in Financial Mathematics

University of Chicago - Master of Science in Financial Mathematics

Full-time, part-time
Chicago, IL
Currently the Program offers 15 months of accelerated, integrated coursework that explores the deep-rooted relationship that exists between theoretical and applied mathematics and the ever-evolving world of finance. To assist our students with applying theory to practice, we offer Project Labs with area employers to help build out our student's skills and knowledge about the quant industry. Our Program, with its unmatched student body and highly accomplished faculty, works hard to uphold the University’s core values by creating an atmosphere that encourages intellectual collaboration and growth.

Lastly, students of the MSFM Program at the University of Chicago enjoy a world-class education. Courses are taught by a combination of established academics and industry professionals. Beyond academics, the MSFM Program offers students the opportunity to grow both privately and professionally. Networking events are held regularly to encourage communication and professional growth.

Our mission is to equip our students with a solid foundation in mathematics, and in doing so provide them with practical knowledge that they can successfully apply to complicated financial models. Our students become leaders in their field; program alumni have gone forth to find success at companies like JP Morgan, UBS, and Goldman Sachs

Interested in learning more? Request information by going to: The University of Chicago | Financial Mathematics and someone from our admissions team will follow up with you shortly.
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Latest reviews

I am December 2019 graduate from University of Chicago’s Financial Mathematics (MSFM) program. This program has transformed me and helped me achieve my goal of breaking in the [quantitative] finance industry and has given me the tools I need to succeed in the workplace. I want to thank all the faculty, students and other professionals I have worked or networked with along the way. For that, I cannot give the program anything but 5 stars for a rating. Although there are areas that need improvement, if you utilize your resources wisely, you will succeed in the program and ultimately start (or continue) a successful career path. This is an extensive review meant for prospective students to consider when choosing a program and also for current students on what they should look out for when attending. Feel free to reach out if you have questions on my review or on the program.

US Citizen; white male; BS Mathematics from a state school in NY (SUNY); 1 year of full time experience as a pricing analyst for an engineering company. Even though I had a limited finance background, the pricing analyst job was important in the sense that I got comfortable analyzing data via Excel but also being able to communicate esoteric analytics to stakeholders and clients. A big reason why I got admitted, despite the non-financial background, is because of the interest and willingness to undertake quant-related projects and learning how to code before starting the program. Moreover, I took several actuarial science courses, albeit unrelated, that showed my interest and ability in applied math.

Did you get admitted to other programs?
I got into a few programs but my next best option would have been Columbia: Mathematics in Finance (MAFN). It was a difficult decision for me but ultimately I chose Chicago and it ended up being the right decision. At the time, Columbia was much less transparent and had difficulty communicating with me and answering my questions via E-mail or phone (even after I had been accepted). On the other hand, Meredith Muir (Assistant Director for Student and Faculty Services for the program) was very extremely helpful with any concerns or questions I had. To me the program seemed more intimate in the sense that it felt more like a community. Columbia was also more expensive. There were a few other programs I got accepted to but turned them down.

Why did you choose this program (over others, if applicable)?
See above answer. To add on, I would say that UChicago has a great reputation and brand and this holds true even for masters or PhD programs. Granted, the undergraduate ranking has the true “elite” reputation but the prominence is still applicable to higher education. Even though the feeling eventually fades, stepping on UChicago’s campus and being surrounded by its architectural beauty, is a humbling experience. Industry heavyweights like Eugene Fama, Myron Scholes, Milton Freedman and other Nobel prize winners have been here to study, conduct research or teach. Lastly, growing up and having attended school in NY all my life, I was keen on moving out and expending all my energy on a new start in a new city (Chicago is definitely a fun city to spend ~1.5 years in).

Application process
Standard GRE/GPA evaluation along with an essay and video interview. The programs selection process is holistic in nature and they will give you a chance if you show them that you have interest and potential. You can find out more information through Meredith. I’m sure if you reached out to her she will answer any of your application related questions.

Course selection
The course selection I would say is relatively standard in the sense that the main toolkits you need in quant finance will be gained: option pricing, numerical methods, Python, C++, stochastic calculus, and portfolio management. The downside however is the lack of diverse electives. I was quite disappointed that the electives were either too specific (e.g. Multivariate Data Analysis via Matrix Decomposition) or too concentrated in one area (e.g. multiple courses on Market Microstructure or Algorithmic Trading). I understand that Chicago is a trading hub but not everyone who graduates from this program wants to be a trader. Moreover, the timing of these courses is somewhat inconvenient -- there were some electives I wanted to take in the Spring but I was too preoccupied by the required courses. Looking back, I would say the bulk of what I have taken from the program is from the required coursework.

Quality of teaching
Before I list out the faculty and their strengths and weaknesses, I want to emphasize that the core course professors are also involved in the management of the program and wear multiple hats (e.g. Roger Lee as the options pricing professor and the Director of the program, Mark Hendricks as the portfolio theory professor and Associate Director of the program, etc.). This means that you will spend more valuable time with these people outside the classroom and gain exposure in many different ways than just coursework (e.g. technical interview labs, clubs, career/employer events, etc.). I am going to give an in-depth review of the required courses professors and give a brief review of the other notable ones.

Roger Lee: FINM 33000 - Mathematical Foundations of Option Pricing. This course is the highlight of the program and the reason why you should attend this program. There is not a more qualified person to teach such an introductory, yet vital course such as this one. Put it this way - for my first project on the job post-graduation I had to read multiple research papers for the model I was working on at the bank and his name came up multiple times in the references section of these papers and even directly in the model my firm built. His teaching style is relatively slow paced but it is the reason why Roger is unlike anyone else. He takes his time to teach and makes sure everyone understands and explains extremely thoroughly. I don’t think there was ever a time he was unsure of what he was talking about. He’s a leading industry expert with many research publications. I still to this day, for my job, refer back to his notes and lecture videos. Roger also teaches FINM 32000 - Numerical Methods, which is a great course that has important topics. For some reason, I wasn’t able to get that much out of it at the time but now that I work more closely with some of the things he mentioned, I was able to go back to his notes and found it helpful. The homework’s are a combination of Python exercises and written examples. For both courses, Roger’s homework assignments are extremely well written, thought out, and useful.

Mark Hendricks: FINM 36700 - Portfolio Theory and Risk Management I. To start, Mark is a great guy - very personable and understanding. He is engaging in the classroom and is always willing to help. This class is of utter importance to anyone who wants to manage a portfolio or trade (even if this is not your job in the short-term, anyone in quant finance will eventually need to understand risk-return dynamics to either make money for a firm or for themselves). Aside from it being great course content-wise, I would like to add that Mark also served as our Coach for the McGill International Portfolio Challenge (another good way to get exposed to portfolio management in UChicago). He was there to guide us with our approach on the case study even though this was outside the bounds of the program - this is what I meant by saying that the professors wear multiple hats earlier.

Sebastian (“Seb”) Donadio: FINM 32500 - Computing for Finance in Python. I don’t think you can find a better programmer than this guy - he also teaches at Columbia for the Financial Engineering master’s program. Seb is an extremely interesting and funny guy who has many hobbies outside of finance which makes his course more fun to sit in on. Although Seb’s course gives you much opportunity to learn Python (“you will eat Python!”) with what seems like an unlimited amount of homework and exercises, to me it was a bit of a quantity over quality kind of class. You do get better at programming, but for a beginner like me at the time, it was tough to really grasp anything. For example, he would have multiple slides on commands found in the Pandas package (this is easily Google-able and people will always utilize StackOverflow for these while working when unsure), whereas the main important points of what DataFrames are used for, examples found in practice, and why it’s important to manipulate the data inside would be more valuable information. His written exams were a pain (although now his course has changed to HackerRank so you won’t have to deal with this). My Python skills progressed more in due time with other courses that came after this course and most quickly while working during my internship or full time. So, although it gives you a lot of time to go through exercises, for me the course kind of just passed me by and I haven’t used any of his course resources after the class (unlike Roger’s course materials which I use very frequently even after graduating).

Steven Lalley: Stochastic Calculus. Very smart guy but the course was extremely mathematical (Lalley is from the pure math department). For me, as a math undergrad, it was relatively interesting but a lot of people found it intimidating or just not useful. It was required at the time (now an elective) and most people had to pass fail this course. This course is a combination of theory and applications but felt more heavily geared toward theory which can be daunting and unrelated if you are not going for your PhD.

Yuri Balasanov, Lida Doloc, Jeff Greco: FINM 33601 - Fixed Income Derivatives. I, and others will probably tell you, that this course was pretty much a waste of time and money (to put it honestly and bluntly). The only semi-valuable thing gained from this course is the lecture notes which can probably even be found better written in other papers or online. The course was too disorganized and nobody seemed to know what’s going on in class or was really attentive to the material. The labs utilizing Bloomberg data and doing some calculations in Excel were just pointless. The homework assignments was re-used from previous years and had little practical application - a lot of formula derivations. There was one however useful discussion and homework exercise in Python on PCA - probably the only thing that really stood out to me in the class. This course has so much potential and to me it seemed like the program blew it. My recommendation for the program is to revamp this course by spending less time going over every little thing and spend more time on one vital and focused application for each lecture. For example, you can spend a whole lecture discussing how one specific exotic derivative is priced or replicated and then do a homework on building a pricer by running a Monte Carlo or PDE, test for errors and convergence rate, etc. -- things actually done in practice instead of just giving us all the formulas for floors, caplets, swaptions etc. (these things are easily found online). Then this idea can be carried over to other derivative products.

Niels Nygaard: FINM 33160 - Machine Learning. Fun Fact: Niels is the founder of the program. He is a very bright person and I found him to be helpful for questions related to HW or outside projects that are coding or ML related. The downside was that I found his class to be kind of monotonous as he would go through code line by line. Not sure how else to teach ML though but for what it was I can probably re-use the lecture notes or the actual code in the future.

Materials used in the program
I liked mbeven’s answer: “a computer and a brain” so I will go with that.

-McGill International Portfolio Challenge. I got a chance to travel with my fellow students to Canada to compete and numerous hours in the lab working on this outside of whatever I had going on in my courses. See MIPC website for more info.
-Project lab: I’m sure you have heard from others by now what this is so I will not describe it but I had to chance to work with a bank on a credit curve modeling project. I even traveled to NY to present to the team but most of the work was done remotely via biweekly phone conferences. It was an interesting and relevant project but was tough to grasp for me in the autumn quarter as I have not dealt with this material before but overall it was a good experience to get exposed and have on my resume. I would recommend if you are more experienced to take project lab after Autumn quarter so you do not get swamped with courses. If you have limited experience it will be better to take an autumn project lab to have something relevant to discuss for an internship superday (as was the case for me).

Career service
If I can give an MVP award to any person I met along this program it would be Emily Backe - Director of Career Development! (I would also give Meredith Muir one for being the life of the program). Emily and others (Alma Ceballos and Danny Michael) are there to guide you for any career advice you seek. This goes beyond just resume writing or interview tips. Emily was literally there for me when I had to make life-changing decisions - such as choosing or switching jobs). It’s really hard to find someone as committed, passionate and enthusiastic in their job as Emily. I highly recommend you utilize what Career Services has to offer - there is inherent value in this program just from the career services, especially if you had limited experience like me. There is a downside, however, associated with the career services -- see below section on
“Suggestions for the program to make it better”.

Student body
Everyone was very friendly and willing to help - I initially thought this was going to be a cutthroat program when I walked in but that was definitely not the case. We had multiple parties throughout the year completely outside the program to get to know each other or just enjoy our time in Chicago. Student life for me was important because I served as the Student Board President where my team and I organized many events our program sponsored to come together as what felt kind of like a family - Chinese New Year dumpling making event, Super Bowl party, ice-skating events, a few pub nights, and best of all: an all-inclusive booze cruise on Lake Michigan. The student body is not too diverse as the majority of students are Chinese natives but this is the case for most quant programs. There were a few other international students in my graduating class from places like Mexico, Spain, Canada, and Singapore.

What do you like about the program?
All the positive aspects mentioned in the above answers. The main point to get across is that the program helped me get a good job after graduating. Other pros include: Bloomberg terminals in the lab; taking the Time Series course by Ruey Tsay (he wrote the book on time series analysis) from the Booth School of Business; numerous behavioral/technical interview labs; getting a solid general finance background which motivated me to pursue some standardized finance certifications; the network you gain from meeting people in the industry or faculty; huge bonus is that all the lectures are recorded so if you miss or cannot attend you can watch online - I mentioned earlier that I re-watch Roger’s lectures when I need understanding in some options area for work.

Suggestions for the program to make it better
-See section on “Course selection” where I discuss electives.
-See feedback on “Quality of teaching” section where I discuss some cons of the coursework.
-Although Career Services is helpful by preparing your resume or prepping you on the behavioral component, the job listings they send you and the Career website resources available (FinMath Connect) post lackluster or outdated jobs. In that aspect, you’re expected to just apply to as many positions as possible. It would be much more helpful if they were more active on personalizing the experience for each student and helping students connect with more alumni or industry professionals to tap into a warmer network. And one more point on the career services - although I had lots of phone interviews, it was hard for me to close out some “superdays” and get an offer. I understand this is my responsibility but some more guidance or training on final rounds would have helped me.

What is your current job status?
Quant at a bank.
I graduated from the MSFM in 2019. Overall, I am very satisfied with the program. Despite it has some things to improve, all the staff is working hard on it based on the comments of the students.
The coursework is very solid. Starting from the September review where you get a refresh of the basics in theory and Python programming, and later in the first quarter getting deep into the quant courses with Options pricing and Portfolio Theory. Once the program starts there is a mix of required and elective courses, all of them very useful for later in the industry. Now you can shape the program depending on the career path you want to take. My favorite courses were Options Pricing, Python and Corporate and Credit Securities.
The program has a lot of great teachers, I particularly enjoyed Roger Lee, Mark Hendricks, Sebastien Donadio and Steven Lalley. All the teachers are always willing to help you, not only with the class, but also preparing for interviews and their main goal is your learning.
Career and Student Services:
The career services Team (Emily, Alma and Danny) is great, they work very hard to grant your success getting an internship and later in the job search. You can schedule as many appointments as you need in order to practice behavioral interviews; they give you feedback, help you with your resume and cover letters. They also do technical interview labs with professionals from different sectors in order to practice and receive feedback from experienced people. In addition to that, a lot of firms come to the campus specifically to recruit people from the program and they also organize visits to the trading firms offices in Downtown. If you are really doing your part, you will surely get a summer internship and a job after graduation.
On the Student Services side, Meredith Muir is amazing. She is always there for the students, helping with any course related issue including the registration process or accommodation for exams if any of your interviews overlaps. She is very involved with the class and is always trying to relief the stress of the students.

Project Lab:
The project Lab is a research project for a firm. It allows you to be coached by experienced people in the industry in projects that are important beyond the academy. It adds a lot of value to your resume whether you have experience or not, but if you are part of the last group, being enrolled in PL will help you with the internship search.
After graduating from this program, I would definitely recommend it to everyone considering a further study in quantitative finance. Curriculum is well designed and all the faculties are really helpful. Thanks to this program, most of the students including myself are well prepared for the job market.

- Background

Bachelor’s degree in finance from Mainland China. In capacity of a sell-side equity trader for nearly three years. Now working as a quant in a BB. Most of the students in my class have a degree in math, engineering or finance.

Why did you choose this program (over others, if applicable)?

Because of the brand, curriculum, the location of Chicago and the career service.

- Curriculum

Before the start of the first quarter, there is a September review. All the important knowledge in math, statistics, finance and computer science is reviewed and emphasized during the one-month time. Some greatly honored professors in The University of Chicago, such as Fefferman and Lawler, will give classes to you. That is awesome!

The course covers topics in both sell-side and buy-side quant. It gives you deep insights into mathematics that works behind finance. Every class is recorded and videos are uploaded in the website so that students can review the lessons easily in case they have an interview and cannot attend the class in person. Even though the program is admittedly difficult, professors as well as classmates are always there to help. Option pricing and Numerical Methods are fabulous courses to handle sell-side interviews. I was asked lots of questions that were covered in this two classes. Roger (the director of the FinMath Program and the instructor of the two courses) is really good at turning complicated math concepts into simple dishes for us to digest. I enjoy each one of his classes very much. He also gives class on brain teasers during interview seasons, which will benefit a lot. Besides, Portfolio Theory taught by Mark covers nearly all of the models and risk management methods widely used by mutual fund and hedge fund. The homework for this class is real case study which allows us to apply theory to practice. When I interviewed with a Mutual Fund, 80% of the questions had been discussed thoroughly in this class. Moreover, students can select courses from other departments and schools, such as Booth and Statistics. These courses are super good.

For most of the courses, coding in Python is greatly emphasized. This technique will be taught in the class called Computing for Finance in Python by Seb. I picked up the Python skills solely from this class and practiced it throughout program. Some of the classes also require you to have knowledge in C++, Matlab and R. All these coding skills will be covered specifically in the class.

Project lab is a great feature that distinguishes this program from its counterparts. Each quarter, students have opportunities to work with outside companies (including assest management, trading firms, banks, etc.) and participate in the project that solves real world problems. Some students even got their internships and full time roles via Project Lab. I did not have any experience in quant before. After completing a Project Lab project, I accumulated some knowledge in machine learning which was crucial for me to prepare a quant career. This is a great experience for us to learn and to put on the resume when hunting for internships and full time jobs.

- Career Service

Chicago is a trading harbor. There are countless prop trading and market making firms here. The FinMath at Uchicago is a target program for all the recruiters. They like to interview students coming here.

Our Career Development Office (CDO) is doing a great job. Two months before the start of the program, CDO begins collecting resumes from prospective students and help them revise it. Then they make resume books which include the information of each student and send the resume books to recruiters. This early action takes the first step to make the students well prepared for the job market.

During the quarter, they organize lots of networking events with employers and alumni. I also enjoy the technical question workshops where I can get instructions on how to solve tech problems. Whenever you need a mock interview, CDO is always there to help. They will provide constructive suggestions on your answer, the way you present, the gesture you make, your accent and tones. Emily, the director of the CDO, is caring and helpful. I had countless mock interviews with her before my superday and she did give me very useful suggestions. During my summer internship, she talked with me a lot in the phone about how to prepare for a success in my career. Whenever I had problems and became struggling, she always gave me encouragement and helped me out. She cheers me up. Alma keeps a close relationship with companies and help contact employer when we have a problem. Danny will send out hiring information every day and let us become well informed of the opening positions. CDO as a whole is trying to offering as many resources as they can to make us succeed.

However, one cannot fully rely on CDO service to guarantee a job position. They will definitely help you, but they cannot attend the interview for you. One should never assume a job will automatically fall on your head once entering into this program. Students themselves are supposed to work hard and stay active during the recruiting process. Job hunting is somehow time consuming and torturing. After you insisting and conquering all the difficulties, everything will be ok. Most of the students in my class end up in super great positions.

- What DON'T you like about the program?

A few classes are theoretical, need to be more practical. Some classes should change the way of teaching, trying to simplify the complicated knowledge and make it more understandable. Do not squeeze in too much knowledge one time.

- What is your current job status?

I am now working as a quant in a BB.

As a whole, I will recommend this program and I will never regret coming here.
  • Anonymous
  • 5.00 star(s)
To echo some of the other sentiment on this forum -- there are certainly minor issues that need to be addressed, but ultimately the program as a whole is fundamentally solid. The curriculum is put together cohesively, and for the most part, the quality of teaching is superb. Faculty and staff are all incredibly friendly and caring -- in particular, the student services administrator, Meredith, is great. The program is admittedly pretty difficult. I came from a Math background and sometimes still had trouble on problem sets. Coding in Python is emphasized throughout most courses.

I'll skip all the small details and focus on the most important things: relevance of coursework, quality of teaching, and career advancement services.

Relevance of Coursework:
I think Roger (the director of the program) works really hard to construct a curriculum that follows a rational progression, and overall I agree with most of his choices. The best courses are Portfolio Theory, Options Pricing, and Numerical Methods. Project Lab, a course that allows you to work with outside companies, is an added plus because it gives you exposure with real-world problems. You can also take courses from Booth or the Statistics department -- these are really good.

Having an intermediate-level Probability and Statistics course during the first quarter would be godsend for recruiting; I know there is one in place right now, but it focuses too much on theory.

Quality of Teaching:
For the most part, the quality is pretty good, but there are definitely some courses that could be taught better (I think the program is fully aware of which ones). Roger Lee and Mark Hendricks are fantastic. Sebastien Donadio (the instructor for Python) is pretty good, although I would argue that while you learn a lot of Python in his class, you do it inefficiently. From my personal experiences, I think there are more efficient ways to teach coding than to bombard students with a heavy amount of coding assignments (some useful, some not). I'm not sure if this has changed since I graduated. But if you want to learn a lot, Seb is your guy.

Career Services:
If you want to do trading, the location is an added plus because Chicago has a high density of prop trading/market making firms.

The career services people are friendly and helpful, but one has to realize there's only so much they can do. They do a good job of putting together and coordinating workshops, and try to provide as many resources and references as they can, but it ultimately depends on the students to actually prepare for recruiting. Recruiting is more a matter of diligence and mental fortitude than anything else, so job placement ultimately depends on the individual more than anything else. New students shouldn't expect to walk into the program and assume that they have a job locked up after graduation, but if you put in the effort you should be fine.
I recently graduated from the FinMath program in Dec 2019.
Overall Experience:
I was very satisfied with the teaching, career services, and professional opportunities provided. The program is majorly taught in Python, and a few classes are in C++.

Things that can be improved: The program can improve on adding more electives, and improve some of the existing ones (namely Machine Learning). Maybe we can have some more advanced classes being offered as electives outside FINM.

Courses: Stand out courses are Option Pricing, Portfolio theory, Quant Strategies. Another advantage is some of the electives offered are cross-listed with the Business school, or Statistics departments, so you can fine tune the course to your requirement.

Project Lab: One of the best experiences to work with companies (HF,AM,Banks) on real problem that they want explore and solve.

Career Services: We had plenty of networking events, company events and alumni dinners to network. I believe the career office is doing a fairly good job.

Bachelor's degree in Engineering from India. Worked as a sell-side analyst for 2 years before going to UChicago MSFM program. Currently working as a desk quant in a bulge bracket.

Did you get admitted to other programs?
Yes, Georgia Tech QCF, Columbia MAFN

Why did you choose this program (over others, if applicable)?
Because of the location, brand- name and some scholarship.

Application process
The application process is pretty easy. Submit the transcripts, resumes, application statements with a video.

Courses selection
The courses which liked the most were Options Pricing, Numerical Methods, and Portfolio Theory. Python has become the primary language of the program. Right from September review students are taught Python programming (which I don't see in other MFE programs). I found this to be very helpful in the internship/job interviews. Other programming courses include C++ programming and advanced C++ programming applications.

During the September review, there were some technical interview preparation workshops which I really liked.

There are trading related classes such as Quantitative trading, Applied Algo trading, and Machine Learning in Finance.

Machine learning in finance is a good class to get into the application respective of machine learning in finance and trading. However, this class can be improved a lot.

There are still some required and elective classes that involve heavy math. e.g Stochastic calculus, Fixed income derivates.

Quality of teaching

Option classes are always at the top of academia. Professor Roger Lee always has a unique way of expressing complicated option concepts in simple terms. I enjoyed both his classes despite the heavy math concepts behind.

Prof Mark Hendricks Portfolio Theory is very practical, he follows a business school teaching style where his assignments are interesting case studies by asset management companies.
There are many trading professionals lecturing the classes. E.g Quantitative trading and regression classes are great to get started in trading.
The Machine Learning and Deep Learning classes still need some improvements though.

I really appreciated the video recording of the lectures. I can review any class again.

Overall, the teaching quality is good except for one or two courses. The faculty is a blend of academic professors and industry practitioners

Overall the curriculum is good but needs more elective courses.

Project Lab
This a collaboration between Uchicago Finmath students and companies (hedge funds, banks, asset managers) to work on some real-world research projects. This is the MSFM program's trump card. I know some students (2 or 3 ) who got their internships and full-time roles through project labs but still, it's difficult to convert. But this is really important for those students who come with no prior experience as they can work on lots of interesting projects. Very time consuming but you can learn a lot from it.

Career service
According to my understanding, the career service has improved greatly in the past 3 years. I attended many networking events, company information sessions, interview workshops, recruiting events and alumni panels. The Career Development Office helps you a lot in interview preparation. The program is making its best efforts to forge good relationships with employers.

Chicago has a lot of trading firms and exchanges. So it's the ideal place for students trying to get into trading shops. New York anytime is the better location as compared to Chicago. U of C has a competitive advantage for the job market in Chicago. Firms based out of Chicago like interviewing students from this school.

What DON'T you like about the program?
Some of the classes are still pretty theoretical, need to be more practical. Machine Learning course needs improvement.

Suggestions for the program to make it better :

Consider improving Machine Learning and Deep Learning class. Add more electives.

What is your current job status?
I will be working as a desk quant in a bulge bracket.
A year out from graduating here, I wanted to give a fair evaluation of the Financial Mathematics Program at the University of Chicago (U of C). The program has its problems, but I gave 5 stars ultimately because my high expectations were met

Came from Australia. Bachelor of Actuarial Studies / Bachelor of Finance. 2 years work experience at an actuarial consultancy, 2 internships at banks in market risk and credit risk. Qualified actuary

Did you get admitted to other programs?
Yes. My next choice would have been Columbia MSOR

Why did you choose this program (over others, if applicable)?
- I was dead set on becoming a trader and Chicago is the trading hub of the world. There are so many trading firms in Chicago, as well as elite hedge funds. The banks mainly operate out of New York

- U of C has a competitive advantage for the job market in Chicago. This is HUGE. There are only two nearby schools I can think of that compete; the University of Illinois and Northwestern. U of I is still annoyingly far away, and as far as I know Northwestern doesn't have a relevant masters program

- Massive brand name. The ranking system that people seem to care about most over here is the USNews one, where U of C is right up there. You'll get interviews in any city. Companies love this school in general

- The actual beauty of the school. Whether it's the amazing libraries, lecture theaters where countless nobel laureates have taught, or the actual MSFM building/house, the school itself is absolutely inspiring. I have to admit, I didn't know U of C was a top school before starting to research graduate programs, but there's no doubt in my mind that it's one of finest academic institutions in the world

- Project Lab (mentioned below)

- The business school. Even though the masters degree is separate, it doesn't hurt to have one of the top business schools on campus. You can try to take courses there (with extra approval). I worked on research in Booth, which was great

- Chicago is beautiful

- The courses are taught by heavyweights in both academics and practice

Application process
Standard process apart from an extra video that you have to provide, answering a couple questions

Course selection
Good range of courses taught on a quarter system (VERY intense). Here are some key points:

- Courses are at night to accommodate for lecturers who work outside of U of C
- One month of refresh courses before the program actually starts. This is a great time to get ready and even start putting in internship applications
- Decent range of electives, including ones on trading directly, machine learning, and time series analysis (taught in Booth). Generally speaking, the core courses are very theoretical and electives are practical
- The options courses are fantastic
- Increasing focus on computer programming. I believe the main language used has been switched to python
- Some very technical mathematics, including stats and calculus courses. These weren't too useful to me, as I wasn't pursuing a pure quant path and had already learned a lot of the material as an undergrad. They were also taught at lightning speed
- Courses on portfolio theory are good. I'm not sure if this is still the case, but both were compulsory; one was on credit risk, which I didn't find useful at all for my career path
- You can test out of some of the programming and an introductory finance course. The introductory finance course I heard was a waste of money

Quality of teaching
Some amazing lecturers and some not-so-amazing lecturers. As I mentioned, there are both academics and practitioners. Being a top school, the academics are very knowledgeable. Being a trading hub, the practitioners are also very knowledgeable (some are current or ex-head quants at top-tier firms). My favourites (in no order) were: Brian Boonstra, Mark Hendricks, Yuri Balasanov, Niels Nygaard, Roger Lee and Chanaka Liyanaarachi

Materials used in the program
A computer and a brain

Notably there is Project Lab, which is a cooperative project between a group of students and a company. This is the MSFM program's trump card. I'm not completely sure on the statistic, but something like 100% of students who apply for Project Lab will end up on a project. The companies are: trading firms (IMC, Belvedere, etc.), hedge funds/asset managers, exchanges and consultancies. I don't believe any banks are in the rotation

Career service
I never really used the MSFM career service apart from our job board, because hustle generally works. Our job board is good. Honestly the careers office seemed a bit small for the number of students and money being hauled in from tuition. This has been restructured since I left though. Outside of the program, there are big career fairs at U of C, as well as companies pencilled in weekly for presentations; these are amazing.

Student body
Very smart students with not a lot of work experience. Not demographically diverse enough in my year; 95% foreign students from Asia. Cheating is treated harshly, which I think is the right way to go. It's an EXTREMELY intense program though, so many resort to cheating. There are a handful of professionals taking the course part time after work

What do you like about the program?
To sum it up, I entered the Financial Mathematics Program at the University of Chicago to learn, AND THEN from that find a job. That's exactly what happened; I learned a boat load, both through the program and on my own... and out of it have come some fruitful years in the US

Suggestions for the program to make it better
- Shave away some of the worse courses or make them electives
- Try to become more diverse without sacrificing student quality
- Beef up the careers services
- Create alumni events in New York ;)

What is your current job status?
Quantitative options trader at a midsize market making firm

Bachelor degress in Math and Statistics in the US. Worked as a investment analyst before going to UChicago MSFM program. Currently working as a quantitative trader in Chicago.

Did you get admitted to other programs?

Why did you choose this program (over others, if applicable)?
It is the location. Chicago is the hub of trading business. I decided to come to Chicago to start my career in trading.

Application process
The application process is pretty easy. Submit the transcripts , resumes, application statements with a video.

Courses selection

Recently UChicago has been working hard on improving the courses. Right now in the first quarter, it offers a Python class which requires zero background in coding and the class will eventually cover a pretty decent depth in the topic of programming. E.g Inheriance, OOP, Concurrency, TCP servers.

There are a lot of trading related classes which I liked the most. Quantitative trading, applied algo trading and Machine Learning in Finance.

The new Machine learning in fiance is also a great class as to get into the application respective of machine learning in finance and trading. However, the class is not easy and requires a lot of work to understand the Mathematics concepts behind each algorithm.

The option pricing classes are the core classes at UChicago MSMF program. Professor Roger Lee always has a great way of getting you to understand the hard Math concepts.

There are still some required classes which involve heavy math. e.g Stocastic calculus , Fixed income derivates.
Making them as elective classes would be a better choise.

Quality of teaching

Option class are always on the top of the academia. Professor Lee always has his unique way of expressing complicated option concept into plain English. I enjoyed both his classes despite the heavy math concepts behind.

There are many trading professionals lecturing the classes. E.g Quantitative trading and regression classes are great class to get started in trading. I was asked a lot during the interview processes.

I really appreciated the video recording of the lectures. I can review any class again.

Materials used in the program
Notes are enough

Programming component of the program

They are teaching C++ and Python from zero.
Matlab is needed for a lot of classes, you can use Python or R for most of these classes as well
I picked up C++ and Python from almost zero and know using them for daily work.

Lots of interesting projects. Very time consuming but you can learn a lot from it

Career service

Location, location, location. Chicago has a lot of trading firms and exchanges.
Career service provides mock interviews and organizing recruiting events.
However, it is still a lot of work for the students to prepare.

What do you like about the program?
A lot of interesting classes, events and workshops. It prepares me for starting a career in trading industry.
The professors and lecturers are easy to work with.
You can learn programming efficiently and masters it during the program.

The project lab is a great way of getting working experience while still in school. It helps a lot on the resume building as well.

What DON'T you like about the program?
Some of the classes are still pretty theoretical, need to be more pratical
First quarter is very intensive, while we need to prepare for the interviews.

Suggestions for the program to make it better

Consider to drop some of the heavy math classes, add in more practical classes.

What are your current job status? What are you looking for?
I'm currently working as a quantitative trader. I'm planning to become a quantitative portoflio manager soon.
Not bad and sounds like things have been improved

What do you think is unique about this program?
Hard to say because I only have second hand info about other programs, but I will try:

I felt like the pace was really fast. When courses drag on too long, it's easy to lose interest and motivation.

Also, there were some really good professors: Paulsen was an excellent lecturer and was very helpful in understanding the material. Lee did real well at explaining and teaching some pretty complex and deep material (for me). Hanson did a great job making stochastic calculus accessible to someone with an engineering background. Even though Fefferman only did a few days of intro on measure theory, he was also excellent at making very theoretical and deep material accesible. There is no shortage of teaching talent, but not every professor is awesome.

What are the weakest points about this program?
The year I went (2009-2010), there were way too many students admitted (~150, which means around 250 people in lecture, including part-timers from previous years). About half seemed legit and about half seemed like undergrads who couldn't get jobs and whose parents had the money to send them. Essentially spoiled brats with no manners. They made it difficult for the instructors because they don't shutup for lecture and they were always suspect of cheating, so the instructors had to be jackasses during exams and during lecture a lot of times. Some of these kids were really bright and some, well, were just kids with no professional experience and no clue how to conduct themselves. Of course, I believe this has changed and the year I went was definitely a transitional period for the program.

This is a minor complaint, but I think there could have been a little more flexibility in the curriculum. For instance, I would've liked to take some computer science classes like machine learning or AI. and perhaps some accounting or more pragmatic finance courses in the business school. It would have rounded things out well if I could've done that.

Career services
I tend to think, "You make your own breaks," for stuff like this. There were quite a few companies recruiting and there were opportunities, just not as many as a b-school student would have. But then, b-school grads get hired into a more broad spectrum of industries and job functions. The school had a decent career website and recruiting process.

The career services I thought were done fairly well, but not outstanding. Tim Weithers did a great job, as did the others.

I and many others found pretty good jobs. A combination of the right fit for a position, a little luck and networking really does a lot.

Of course, that doesn't mean everybody had the same experience I did. I also know some people who had trouble finding a job and either had to revert back to their original job function or had to look for an extended period of time. I'm not sure that's the school's or the program's fault. A bad job market and perhaps lack of planning could've contributed.

Student body
Even though the network was smaller, I met and keep in touch with quite a few people from the program. I have many fond memories of U of C and the program and did a lot of learning there. No amount of disparaging remarks about the program, the professors or the students will change that.

I mentioned the class size above, which made it harder to meet more good people. Again, I believe this has changed and thank God for that.
Can you tell us a bit about your background?
B.S. Engineering Physics (High Honors), University of Illinois at Urbana-Champaign, 2002
B.S. Computer Science (Highest Honors), University of Illinois at Urbana-Champaign, 2002
Five years of IT and software development
I studied full-time in the program from 9/2007-6/2008

Did you get admitted to other programs?
Yes; NYC Courant's Mathematics in Finance and Carnegie Mellon's MS Computational Finance
Why did you choose this program (over others, if applicable)?
Short duration, lower cost of program and of living, reputed heavy mathematical focus.

Tell us about the application process at this program
Online application. Had to submit GRE General test scores. Went smoothly.

Does this program have refresher courses for incoming students? How useful was it?
There were free courses offered to incoming students which were nominally "refresher" courses. In practice, these courses were survey courses in material that students were unlikely to have been exposed to. Several of them were very useful; the measure theory course conducted by Robert Fefferman was outstanding, and the finance introduction by Tim Weithers was well-done.

Tell us about the courses selection in this program. Any special courses you like?
Mathematical Foundations of Option Pricing - excellent
Numerical Methods - excellent
Statistical Risk Management
Stochastic Calculus
Data Analysis And Statistics - disastrously bad
Topics in Economics - well-taught, but questionably relevant
Portfolio Theory and Risk Management - good
Foreign Exchange
Fixed Income Derivatives - wildly mixed quality depending on presenter
Advanced Option Pricing
C# Programming (optional)

Tell us about the quality of teaching
Teaching quality ranged from very competent to insultingly poor.

On the "competent" end, Roger Lee deserves particular commendation for his course preparation and delivery in Mathematical Foundations of Option Pricing and Numerical Methods. The design and delivery of both courses was absolutely meticulous and well thought-out. Other instructors who deserve positive mention include: Paul Staneski and John Zerolis (Portfolio Theory), Lida Doloc (Fixed Income Derivatives), and Jostein Paulsen (Stochastic Calculus, Statistical Risk Management).

Sadly, there were also several instructors who did not appear to feel it necessary to assemble a coherent syllabus or present their material in an understandable fashion to the students in their classes. Chief among these was Per Mykland, who "taught" the Data Analysis and Statistics course (and I believe in most years teaches stochastic calculus as well). His lectures were very rough surveys of material which was inaccessible and unknown to most of the students in the course. Generally, no supplementary reference texts were mentioned to provide comprehensible explanations of the topics covered, and his lecture notes were filled with errors that had clearly gone uncorrected for years. Much of the final section of his notes consisted of text copied directly (save an occasional misspelling) from N.H. Chan's _Time Series_. The material covered by the homeworks was often not addressed by the lectures; in fact, the head teaching assistant informed us in no uncertain terms that he expected most of the class to fail the second homework. The teaching assistants were often unable to help with the homeworks, leading me to believe that they frequently did not understand the material any better than we. Students did so poorly in the class that he announced his intention in the final lecture to simply give all students 'A's in lieu of a final exam (though he was later overruled).

Professor Mykland's disregard for his teaching obligations is legendary; there are discussions on Wilmott Forums of his lack of concern for his audience's comprehension all the way back in 2005 and it seems that little has changed. Unfortunately, data analysis and statistics are fundamental to financial mathematics, and for those of us who did not enter with graduate degrees in statistics, we were left woefully unprepared for the remainder of the courses (particularly stochastic calculus and statistical risk management) as well as job interviews. As a particularly pointed example, I was unclear on what a "standard error" was until I engaged in a program of self-study after his course...in which I got an A-.

Materials used in the program
Primary texts included:
- Hull's _Options, Futures, and Other Derivatives_
- Carmona's _Statistical Analysis of Financial Data in S-Plus_
- Ingersoll's _Theory of Financial Decision Making_
- Bjork's _Arbitrage Theory in Continuous Time_

I also found Baxter and Rennie's _Financial Calculus_ to be an excellent introductory text, though it was not used directly in the courses.

Programming component of the program
Excel, R for statistical analysis, MATLAB for numerical methods, C++ with Quantlib for some interest-rate derivatives work. The introductory programming courses were taught in C# and covered basic ASP.NET. Many of the homework assignments had a programming component.

There were few "projects" per se, though much of the homework was group work.
Career service
Unknown; I didn't use them.

Can you comment on the social interaction between students of different ethnics, nationalities in the program?
Many of the Chinese students formed a fairly tight social group, I suspect due to shared language. The remainder of the students were very outgoing and mingled and worked together freely.
What do you like about the program?
Excellent instruction in a few topics, a good reading list, U of Chicago name on the diploma, meeting a lot of great people.

What DON’T you like about the program?
The general disregard for the quality of education received by the students.
Suggestions for the program to make it better
- Dismiss Per Mykland.
- Solicit feedback from students to ensure that professors generally are doing a good job, and reward them or remove them accordingly.
- Ensure that professors are aware of how their courses fit into the overall curriculum. Many of them seemed unaware of what students entering their classes would and would not be expected to know.

What are your current job status? What are you looking for?
I am employed by an investment bank as a software developer, and would like to find more quantitative challenges in the near future.

Other comments
If you have a graduate level of statistical experience, are very comfortable with PDEs (and preferably SDEs), and are willing to put up with having to fight to learn in some classes, you could get a lot out of this program. Otherwise, I would suggest going elsewhere.