University of Chicago - MS in Financial Mathematics

I graduated from UChicago's MSFM program in December 2021 and am an incoming quant researcher for a multistrat hedge fund. I am extremely satisfied with this program and would heavily recommend it to anyone looking to further their education or get into the quant finance industry.

My Background
I studied math and economics in undergrad and have a MA in economics from a top program. I wanted to pursue a PhD in economics, but after the MA program, was no longer interested, but still looking for a quantitative career that was fast paced and empirically driven. Quant finance seemed like a good fit, which led me to UChicago.

Why FinMath
The primary reason I chose UChicago's FinMath program was because of the placements. By having consistent placements in hubs like NY and Chicago, employment by some top firms, respectable reported salaries, and high employment rates at graduation, I knew my goals and the programs outcomes were well-aligned. Beyond placement, the University of Chicago is a renown university for multiple disciplines and a great learning environment. Furthermore, a dedicated career office (CDO) to the FinMath program allowed for one-on-one advising and guidance throughout the program, offering tips on preparing for interviews, searching for jobs/internships and networking.


Technical Skills Acquired
Python is the primary language used in most of the courses offered. The program starts with foundations, and effectively builds the skills acquired to be sufficient in industry. Coming from an R background, I found the learning curve steep, yet manageable. The courses give great exposure to Jupyter notebooks and IDEs like PyCharm. C++ is a secondary language for students interested in more of a quant developer's track, and few classes (primarily electives taught through the stats department or business school) are taught in R.


Workload
The initial Fall Quarter is by far the most challenging quarter and I believe all subsequent quarters are as difficult as you would like--there is a lot of flexibility.


Project Lab
The program offers a short-term applied research project sponsored by companies facing actual quant problems. I found this to be extremely interesting, and offered a realistic application of the theory and skills that I had developed over my time in the program. It also was a great opportunity to hone softer skills like teamwork and communication.

Course recommendations
I am going to piggyback off of one of my classmates below. In general, all of the required course (portfolio theory, option theory, stochastic calculus, and python, and quant trading) are all exceptional classes. For added color:
- Seb Donadio's Python and C++ courses. Python is required, and will be challenging even to experienced python programmers. The C++ class is helpful and takes a similar approach as Python. Seb really cares about the students, and is very approachable.
- Roger Lee is an outstanding teacher. The theoretical option pricing course is great, and the Numerical Methods to option pricing is even better.
- Brian Boonstra’s Quantitative Trading Course is taught by an actual quant who has plenty of years in industry and teaching. Lectures are lively and problem sets are challenging.
- Multivariate Data Analysis is a deep dive into unsupervised learning, covering everything from PCA to state of the art techniques.


Summary
This program deserves a wholehearted recommendation. Besides everything outlined above, I am compelled to comment on my cohort. I was throughly impressed by some of my classmates in terms of their intellect, experiences, and goals. They are outstanding scholars and these great friendships provided the camaraderie required to get the most out of the program.

I am a current student at the University of Chicago's MSFM program. I will be graduating very soon in December 2021 and will be joining an asset management firm as a quantitative strat. I had noticed during my own application season back in 2019-2020 that many of the reviews about this program are quite old, dating back to the early 2010s. So, I thought to pen down my own experience, which I believe could be much more useful to the prospective students.

Short review:

Amazing program with relevant and rigorous courses. Instructors are helpful and industry experts within their respective domains of quant finance. Outstanding career services. An additional bonus of Chicago being an international hub for finance, especially for proprietary trading shops.

University of Chicago is renowned for its mathematics, economics, statistics and the Booth School. Hence, it has a solid brand value and great connections with reputed firms all over the land.

Detailed review:

Background- I’m an international student from Asia having an undergrad major in economics and statistics from a top-5 university in my country. Prior to coming to the US, I had worked for like 2 years, much more related to economics than quant finance. I wanted to work in the quant finance industry rather than just economics + statistics. I had applied to Financial Engineering programs in the US and received admits from UChicago and a few other good schools within the top-8 of the QuantNet rankings. In the hindsight, it has been a wonderful experience. No regrets at all.

Program- Before the program starts, there is a 3-week refresher session called ‘August Review’. Topics related to regression, probability, statistics, etc. are taught. It serves as a revision refresher for those who have studied these topics earlier and serves as a great learning toolkit for those students who don’t have much experience with these topics. Since many of the firms start their internship recruitment in late August, hence I believe ‘August Review” does a great job of brushing up important relevant topics for the internship interviews.

After the ‘August Review’ comes the ‘September Launch’, which is a 4-week long session in which modules related to python programming and financial markets are taught rigorously in quite a depth. Just to remind you, this is still a pre-program phase. I believe ‘September Launch’ is an amazing way to prepare the students for the main program, particularly those students who don’t have much prior experience with Python programming and financial markets. Technical interview labs with different professors are also held during ‘September Launch’ which explicitly train students for the crucial fall internship recruitment season.

So, ‘August Review’ and ‘September Launch’ together prepares students for the main program by equipping them with required skills in Python, financial markets, regression, statistics, etc., and also for the fall internship interviews. Without these two sessions, it would be much more overwhelming for the incoming students to handle any rigorous Financial Engineering program and also the internship interviews which start as early as September.

Courses- First quarter (fall) has three main important compulsory courses: Portfolio Theory and Risk Management taught by Mark Hendricks, Option Pricing by Roger Lee, and Python Programming by Sebastien. Portfolio Theory taught by Mark Hendricks is an amazing and quite important course. Mark has balanced it perfectly between theory and applied practice. There are many real-life quant finance case studies in homework and for discussions, which really help to understand the real-life applications of quantitative portfolio theory. Mark cares about his students and is quite dedicated to his course. There are instructor hours, instructor reviews, TA hours, extra hours, and other resources designed by Mark to ensure students understand the quantitative portfolio theory and risk management inside and out.

Option Pricing courses by Roger Lee are just outstanding. I simply don’t have enough words to do justice with how good his courses actually are.

Python and C++ courses taught by Sebastien are rigorous (as they should be) and the learning curve is very steep. There is simply so much to learn in his two courses, it’s amazing. I came into the program as a beginner in Python and C++ and at this point, I feel pretty comfortable in both of them.

The quantitative trading strategies course taught by Brian Boonstra is again a rigorous course that involves lots of assignments, handouts, and videos. You could be a trading novice prior to entering his class but if you do the class sincerely you will step out equipped with sufficient tools required to start constructing complex trading strategies.

Financial Time Series Analysis class taught by renowned statistician Rue Tsay from the Booth School is an amazing class and is quite important in the quant finance industry.

The program also offers classes in Forex and Fixed Income derivatives, machine learning, and deep learning, market microstructure, multivariate data analysis, etc. There are also the classes in probability and stochastic calculus taught by respected mathematician Gregory Lawler.

Career Services: The Career Development Office (CDO) at the UChicago MSFM is one of the strongest pillars and a big plus point of the program. I remember when I was applying to the Financial Engineering programs back in 2019-2020, I came across some reviews on the QuantNet which were skeptical of the career services at UChicago MSFM. Speaking as of 2021, trust me, the CDO at this program is WORLD-CLASS, amazing, and more than what a financial engineering student would ask for. Emily, Danny, and Alma work hard with students to ensure that the students land the jobs they want. My career advisor was Emily and she left no stone unturned to help me during the entire program. From helping me significantly improve my job search process, working on resumes and SOPs to multiple interview practices and feedback, Emily and CDO were always there to assist.

The Industry Perspective In-Residence (IPR) is a great initiative by the MSFM program, whereby a seasoned practitioner from the quant finance industry is available for technical interview practice and general consultation within the university.

In a nutshell: Great program, outstanding career services, rigorous and relevant courses, helpful and expert instructors. No regrets, just awesome memories!

I graduated from UChicago's MSFM program in December 2021 and I'm an incoming Quantitative Trader of a Chicago-based prop trading firm. Overall, I loved this program! There is no way I'd be where I am without it. It thoroughly prepared me professionally (internship and job search) and technically (courses and projects). If you want to be a non-PhD quant, this is an awesome program!

My Background
I studied math, statistics, and computer science in undergrad from a Big Ten school. I was primarily interested in machine learning or a statistics PhD, but found quant finance very interesting and learned that a PhD is not necessary to be an applied quant. Having little finance background, I thought a quant program like this would be an awesome start. I applied to many schools, but my final choices were UChicago's FinMath program and Columbia's Mathematical Finance program. Both are awesome programs, but I haven't regretted my choice of UChicago for a second!

Why FinMath
UChicago has an amazing reputation in econ, math, and statistics. The FinMath program also thoroughly used Python, Jupyter notebooks, and C++, which wasn't the case at every other quant master's program. They also are very up-to-date on machine learning and statistics which I liked, since this was my background. But a main differentiator was the career development office (CDO). CDO THOROUGHLY prepares EVERY student for interviews. Whether it's networking, preparing for technical interviews, or just organizing your search process, they are excited to help you individually. UChicago's Project Lab is also a differentiator; this was an awesome opportunity to get some quant finance on your resume and is built into the program. This was useful for me since I was new to finance.

Workload
This program is rigorous, but I don't think it would have the reputation it does without rigor. However, I would not say it was overwhelming or excessive. Fall quarter is definitely the most intense, but this is mostly due to the interview process. The remaining quarters can be as intense as you want; I took lots of courses in the winter and spring quarters so my final quarter would be quite light and I liked this. This program is not easy, but I think it's very fair and teaches useful and interesting things.

Career Development Office (CDO)
One of the best parts of the program. Every aspect of the interview process (general landscape, technical prep, networking, etc.) is formally and informally covered by CDO. I couldn't have done well in the interview process without them! Surprised to hear some other quant program's career offices do so little. FinMath CDO starts working with you before the program even starts. They start by perfecting resumes and cover letters. They also suggest thoroughly reviewing the Green Book ("A Practical Guide to Quantitative Finance Interviews") before you arrive; this is crucial to do! During the program, they offer tons of workshops on technical interview prep, behavioral interview prep, and networking. They also are open for 1-on-1 meetings whenever you want. They also have a rich alumni network so you can know what to expect on your interviews.

Project Lab
Perfect for someone new to the industry. This is a company-sponsored project that you work in a team on. Each project has a supervisor and you report weekly or biweekly to your company. There is also a final presentation. This is an EXCELLENT thing to talk about in subsequent interviews as well.

Course recommendations
- Any course with Prof. Roger Lee. He is perhaps the best teacher I've had, and I've had a lot of strong teachers! He taught Option Pricing and Numerical Methods (computational sequel to his Option Pricing course). Medium workload for both.
- Prof. Greg Lawler's Stochastic Calculus Sequence. Stochastic calculus is not strictly necessary for modern quants, but I found this course extremely interesting and useful. Prof. Lawler is a highly respected mathematician and a great instructor too! Medium workload.
- Seb Donadio's C++ Course. C++ is extremely useful, and Seb will push you to become quite comfortable with it! He really cares about his students. Medium to heavy workload.
- Brian Boonstra’s Quantitative Trading Course: rigorous intro to quant trading and lots of practice coding up backtesters in python. This was perhaps the most intense course (for me) in the program, but I got a ton out of it. Boonstra is a highly respected quant.
- Ruey Tsay’s Time Series: very solid course taught by a very respected statistician. Medium workload and excellent time series content.

Summary
This is an amazing program and I'm so glad I enrolled! And make sure you review the Green Book before you arrive!

I graduated from UChicago's MSFM program in December 2020 and I'm an incoming Quantitative Trader of a Chicago-based prop trading firm. Overall, to rate this program, I'll definitely say this program gives me so much help in studying and job hunting and it's totally worthwhile to choose this program to strengthen yourself in all kinds of perspective if you wanna dig more into this area.

My Background
I learned math and economics during undergrad in the states. Honestly I don’t see myself as a typical candidate for MSFM (lack of related intern experiences and was not sure if I really wanted to be a quant in the future etc.). I mainly focused on applying for stats/biostats/applied math graduate programs during application season and I managed to get into big names such as Yale, Columbia, Cornell, Duke etc.

Why MSFM
I’ll definitely say UChicago has always been my dream school. I’m a math and economics student and without any doubt UChicago is like heaven to me. When doing all kinds of researches, I looked into the curriculum of this program and found out that all of the courses are pretty useful for my future study/career. To be a quant, you need to be good at math, coding, machine learning and also all kinds of financial products. MSFM offers a bunch of good courses in all those aspects (Python, C++, Machine Learning, Options, Fixed Income, Matrix Decomposition etc.). At that time, I was intrigued by those courses and also those well-known professors (Lawler, Roger Lee etc.). Besides what I mentioned above, the Project Lab also attracted me. I didn’t really have that confidence while applying since I’m not sure if I’m gonna secure an offer after graduation so being able to attend the project lab is a secure move and I thought it would add more colors into my resume.

Workload
I’ll admit that the workload of this program is pretty huge, which is a good thing for me. At the first quarter, we got four compulsory courses (with a log of dues, exams and projects) and all kinds of workshops to attend in order to prepare ourselves for job hunting. In the meantime, it was the hiring season and with tons of interviews coming, it was crazily busy at that time. But all the courses did teach us useful stuff which could be encountered during quant interviews. After the first quarter, we got much more time to free solo by our own and this is the time for us to choose free elective courses that we’re interested in.

Project Lab
Definitely shed some light of how this industry works. If you’re someone who doesn’t have much experiences in quant trading, project lab is a perfect starting point. Every project lab has a supervisor and several teammates (your peers) and you guys will report weekly or biweekly to the company and there will be a final presentation at the end of each quarter. This is a good chance to face the real challenge in this business (messy data, unclear information, bad communication etc.) but once you conquered all those trouble, you’ll definitely learn a lot.

Course recommendations
Roger’s Options and Numerical Methods (those two are my favorite, such clearly-illustrated and useful courses), Seb’s Python (super useful but huge workload), Brian’s Regression Analysis (good chance to practice coding, project-based, huge workload), Tsay’s Time Series (good professor, super useful course), Lim’s Matrix Decomposition (useful and important linear algebra course, love it)

Career Office
Emily is definitely the sweetest and the most supportive and helpful person of this program!!!!! I love her so much and she’s always so nice, patient and definitely offers the best career service ever. I’ve heard that some schools’ career offices offer no help at all but at MSFM, they do everything they can to help you. Before officially enrolled in the program, you’re required to send resume and cover letter to them for several versions of revision. And then, during September review, they’ll organize a tons of workshops for preparing interviews and networking. Also, before every interview, if you talk with them, Emily will give you some questions that other alumni encountered before to help you prepare and they would love to mock interview with you. Emily follows up with my job hunting during the whole 15-month program and I’ll say, without her support, help and encouragement, I would never be able to truly believe that I could slay those interviews and got my offers.

Summary
Definitely worth your time. Appreciate everything I gained from this program.

Overall:
I graduated from the program in December 2020 and absolutely loved it for multiple reasons. The program is strong academically and rigorous yet is ultimately very rewarding.

Background:
I joined the program straight from undergrad where I studied applied math and statistics so I only had internship experiences prior to starting. In general, students join with very different backgrounds (economics, engineering, physics, math, stats, finance, etc.) and some at different points in their careers so I would not say anyone is really at a disadvantage or wouldn’t be able to succeed in the program given their background (assuming program requirements are met). In fact, the finmath (UChicago financial math program!) faculty do a great job helping all students catch up and review any important material during September Review (prior to Fall quarter).

Application/Decision Process:
The application is fairly straight forward. You need letters of rec, an essay (why the financial math field? why uchicago?), and a video interview recording of you answering some questions (this is also nothing to stress over and although I don’t remember the exact questions asked, they were fairly general and nothing out of the ordinary, more or less something along the lines of “why do you want to join this program?”).

I was ultimately deciding between a few different programs and UChicago. While I obviously chose UChicago, I never once regretted it. I think a few important things to think about if you are applying or deciding are 1) prestige (I hate to say it but it’s true in this field) 2) location 3) experience visiting or meeting students or faculty from the program. For me, UChicago covered all these three categories fairly well and I can confidently say this after graduating as well as after experiencing the application and interview process for internships and full-time positions. The University of Chicago itself has a great reputation and the Financial Mathematics program even more so, so applying pretty much anywhere gives you a leg up. Furthermore, the program being in Chicago is great since there are numerous trading firms (who hire many students) there and also a great chance to actually see some of these firms in person as the finmath program organizes “field trips” for students to meet quants and traders at these firms. One thing to note, given many firms/banks in this field are in NY, the fact that the program is not in NY is a question that has come up quite a bit when I have talked to prospective students. I have never seen this as an issue or as a disadvantage when applying to jobs or going through the interview process with companies in different states. I guess the only downside is you might have to hop on an extra flight for an interview but that’s not always so bad. Finally, the actual experience when visiting or talking to students/faculty is crucial! I had some questions prior to accepting and Meredith who is the director of student and faculty services at finmath along with some current students at the time did an amazing job guiding me through these which gave me a great sense of what the culture of the program would be like. This is also important because after all, these are the people you will be going to for help with career advice, course work, research, etc. so I would say that these interactions should be a top factor in your decision making.

Courses/Program Structure:
The courses offered at the program are very thoroughly planned out and designed to help you succeed. For instance, the first quarter of the program you take core courses in options pricing, stochastic processes, portfolio theory, and python. These are important because this is exactly what most interviews cover so you jump into the program really just being fed information that you will then be asked about in an interview. Fall quarter starts in early October or late September which is a bit unfortunate since that is the peak of the recruiting season for internships. However, finmath does a great job dealing with this since the career development office starts interacting with students during the summer with study tips, resume and cover letter review appointments, etc. pretty much getting you ready for September Review. September Review is an additional part of the program that while is not mandatory is STRONGLY recommended and for a good reason. Almost every day in September there is a review session or workshop (technical and non-technical) that is helpful in preparing you for classes and interviews. During this month, many activities are also organized that allow you to meet your fellow classmates, alum, or even quants or other professionals from different firms. These are all great opportunities to network and possibly learn more about where you want to apply. Fall quarter I would say is the most hectic as you are trying to juggle courses along with applying to jobs, studying for interviews, and finally actually interviewing which can all be a lot to handle together. If you are deciding between programs, I can confidently say that this is probably true for all students regardless of the school, so choosing a program where you think you can get the most help and support in this process is vital. I think what’s really helpful at finamath is that the career development office and faculty know and understand this which makes everything slightly easier to maneuver (Meredith is also a great cheerleader for everyone in this process throughout the entire year!!!) . Furthermore throughout the program, it really feels like the faculty is there to help you and really want the best for you which is a great environment to be in. I won’t go into too much detail about other courses, but some of my favorites or those that I found to be very helpful aside from the first quarter courses I mentioned are: FINM 33150 - Regression Analysis and Quant. Trading Strategies, FINM 32700 - Advanced Computing for Finance, and FINM 32000 - Numerical Methods.
Finally, project lab is definitely one of finmath’s selling points. Project lab is an opportunity for students to join a project each quarter (most students only do 1-2) with a partnering firm. This is kind of like a group project (4-5 students) in internship format that occurs during the quarter. I know students who have loved their projects and those who have not so I think it’s definitely a hit or miss depending on what firm you choose, how engaged the professionals from the firms are while working with finmath students, and of course, the topic of the project lab. However, it's a great experience to add to your resume or even get a paid summer internship /full-time job from. Lastly, many of the finmath faculty are working professionals. This means they bring a lot of what is being practiced in industry to the classroom. They are also all a great resource for questions regarding careers and research projects or really anything you may be curious about.

Career Development Office:
AMAZING. I really couldn’t even imagine how helpful a program’s career development office could be prior to attending UChicago’s finmath program. Students are paired with one of three finamth counselors at the start of the year and this is the person you can go to for everything career related. It is really easy to get appointments when necessary so if I ever had questions prior to interviewing somewhere, I could always schedule an appointment with my counselor. The CDO is more of a network that keeps growing every year, learning more and more about other companies, the interview process, and gaining valuable insight that they then share with students. Furthermore, they organize countless workshops throughout the year that help with interview prep. A common example of such workshop is a faculty member (often times Roger Lee or Mark Hendricks) going over brain teasers or interview questions that UChicago students have seen during interviews at top firms.

Weaknesses:
While there aren’t many weaknesses I that I can think of, I think that a few of the electives offered are not taught in the most practical manner or could be updated. Furthermore, the programming courses (other than those taught by Sebastien D. - Computing for Finance in Python, Advanced Computing for Finance) could be improved and some more advanced techniques (ex: advanced machine learning techniques) that are currently used in the field should be introduced. However this may be the key difference between financial engineering and financial math programs so I guess a matter of preference.

Random things I wish I knew about the program before joining:
- Financial Math at UChicago literally has its own building which is very cool! Inside is a lecture hall (where you take most of your courses throughout the program), study rooms, a computer lab, and offices of most of the faculty.
- Most courses are during the evenings! Usually 6-9 pm so it’s definitely something to get used to but also gives you time to interview, work, or do other things during the day.
- Not a lot of information is given about September Review but it essentially is an entire month of different events ranging from company talks, socializing events, and finally most importantly review of different topics that students should be refreshed on prior to starting.
Chicago is a very fun city to be in so definitely a great place to go to grad school.
- I think every single finmath student will tell you this but buy the green book ("A Practical Guide To Quantitative Finance Interviews”) in the summer prior to starting this program or any other financial math/engineering program and start going over questions and brainteasers from it. This will save you so much time during Fall Quarter and make your life so much easier.

1. Background
I am a December 2020 graduate from the University of Chicago’s Financial Mathematics (MSFM) program. Before joining the program, I had a BS in Actuarial Science/Statistics from UIUC. Then I worked in banking and risk management for a few years. The main reason that I applied for the MSFM is that I want to switch my career to a more challenging and rewarding job. I learned about the program from some of my UIUC classmates, who attended the program after graduating from undergraduate.

The program officially starts at the beginning of September each year, and you will go through a month called 'September review', where you will be given reviews in subjects, including Math, Statistics, python programming, and data analysis. In the meantime, Career Development Office will organize a lot of job search-related training, including networking, interview skills, communication skills. It is gonna be very INTENSE!!! So be prepared.

After the sep. review, normal classes will begin. Life is still very tough from Oct. to Dec. as you will need to take classes (hw, exams) and search for next summer internship (apply jobs, interviews prep.). If you are lucky enough to land an internship, life will be easier for you during the winter and spring quarters (Jan. - Jun.). Your last quarter starts in Oct. and the full-time job search should start around Aug.

My overall experience in the program is very positive. I have achieved my goal by attending the program. I currently work as a bond trader after graduation. I would highly recommend the MSFM program at UChicago to anyone who is planning to pursue a career in the quantitative finance industry or any positions that require quantitative skills

2. Courses
The curriculums of the program are well designed and cover broad ranges of topics with the goal to train people to become well-rounded quants.

My favorite classes are Roger Lee's Mathematical Foundations of Option Pricing and Numerical Method, Chanaka's Computing for Finance in C++, and Seb's Advanced Computing for Finance in C++

3. Career Development Office (CDO)
One of the biggest advantages of the program and UChicago is its location. The city of Chicago has a huge Job market and is a trading harbor. There are many prop trading firms, hedge firms, asset management firms. In addition, most of the firms maintain close connections with the program and they will host info. sessions on campus (some companies even invite students for an office tour).

I have to say CDO is doing a superb job at helping students with job search, resume and cover letter reviews, networking with employers and interviews, etc. CDO will offer tons of workshops to cover every aspect and step you need to take to land a job and help you practice interview questions. In addition, CDO advisors are super helpful and always available when you need help.

If you never have experience searching for jobs and doing interviews, I think you will benefit the most by attending the CDO workshop and make sure you participate and practice your communication and interview skills with CDO advisors.

4. Final advice
If you have 3-4 months free time before Sep. Review (start of the program), you should use this free time wisely to study Green Book and practice programming questions on Leetcode to get ready for technical interviews. Once you start the program, you will have very limited time studying those time-consuming materials.

Be mentally and physically (make sure you sleep well) prepared for this intense adventure. It is a marathon, so stay positive and keep working hard. You will eventually get through!

I graduated Dec 2020 from Finmath program. Best program ever.

The curriculum setting is simple and straightforward, unlike other programs offering tons of courses from other department and confuse people on deciding the curriculum structure, UChicago Finmath does a great job on building up a practical schedule for students.

The most IMPORTANT PART is that courses we learnt are really practical instead of purely theoretical. They involve LARGE side of coding and computational practice which would be useful not only in academic side but also in your career side. There are also practices related to courses for interview questions alongside each class and we find them truly useful in career season.

On the other hand, we have one-to-one career advisor services that help each student with their resume/CV/career goal etc. Workshops and labs for interview/networking/working culture are also brilliant in helping student get into the career path. CDO officers are nice and warm with encouragement to us. They followup regularly with our daily school work and job fair to make sure we have everything on the right track. Tons of career fair and company tour opportunities were offered during the first month and that was a great experience.

Overall I truly admire the system UChicago Finmath has built for students. Everything we learnt are PRACTICAL and useful. Employees and professors all care about students with all their efforts.

I'm glad that I get al l sources of help from this program and I'm proud to be in finmath cohort. :)

Overall:
Graduated in Dec 2020. Currently a trader in credit market making. Will recommend this program for students who are interested in quant research, options marking making, or just want to open a career in Chicago
.

Strength:
1. Some coursework are solid. Meaning if you take them seriously and work hard, then will be able to learn a lot. My favorite: Options related courses by Roger Lee.

2. To my own experience, the Career Service (CDO) director is very helpful. She was my advisor and gave me big helps in internship & fulltime job searches. Note: CDO service experience is unique to each individual. I only speak for my experience. CDO will provide the resource/connection for you to work on, ultimately you are responsible of your own career.

3. Target school for Chicago based options market making prop shops.

4. Project labs with companies: Give you the chance to work with hedge funds/trading firms on projects. Project labs are time consuming but they can be fruitful. Choose the right one and really spend time & effort on it.


Weakness:

1. If you just want to have a career at the BB banks then NY schools or CMU may be better fits.
2. Some coursework are not practical enough. Choose what to take wisely.

Overall
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.

Background
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.

Projects
-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.
Curriculum:
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.
Faculty:
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.

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.

Background

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.

Location
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

Background
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

Projects
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

Background

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?
Yes

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.

Projects
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.

Projects
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.

Background
Master's degree in computer science. Worked for a bank as a risk analyst and currently working as an energy trading strategist for a major energy company. I am located in Singapore and report to boss in UK.
I studied part-time in the Chicago MSMF Singapore program since Sept 2009

Did you get admitted to other programs?
No

Why did you choose this program (over others, if applicable)?
I only applied for UChicago MSFM Singapore as it seems the best choice in Singapore or even Asia in terms of reputation.

Application process
The website was and is outdated. Most of the information regarding this program was found in online discussions. They responded my emails selectively.

Courses selection
1. Course selection is not flexible. I like Statistical Risk Management, Mathematical Foundation Option Pricing and Fixed Income so far.
2. Singapore students can study in Chicago for 1 or 2 quarters our of 3 quarters. a lot of us chose to go to Chicago in Spring quarter Apr - Jun
3. We can choose one course from Chicago Booth, which is only available in Chicago campus

Quality of teaching
1. Practitioners teaching Fixed Income are good. because the course is reduced for some reason they insist offering additional seminar which is not counted in grading.

2. the class is like a video-conference bringing people from Chicago, Singapore and Stamford together. the video quality is good. we can ask questions using a microphone. all classes are recorded and can be played back later.

3. Teachers for each course normally come to teach in Singapore for 2 weeks, giving lectures and office hours.

4. Singapore program has local TAs for every course, mostly previous graduates. They are very helpful. Without TA's help it's not possible to figure out homework. In terms of homework, TAs are much more useful than lecturers.

Materials used in the program
Notes are enough

Programming component of the program
Matlab is dominant. There's C# class but i haven't taken. For me not much programming except the Matlab programming in statistical risk management and regression analysis.

Projects
so far i haven't done any project, expecting one for Regression Analysis.

Career service
1. We visited a couple of banks in Singapore
2. Our resumes are compiled and sent to banks
3. Other than campus recruitment, quite a few immediate openings have been sourced by local career service.
4. nothing specific to intern

Student body
Ethnics are quite diversified and balanced. we are also invited to Chicago Booth alumni gathering in Singapore.

What do you like about the program?
I am much more confident about my math now. previously i picked up some models on my own, which can't compare to what I learn in the program in terms of both coverage and depth.

What DON'T you like about the program?
1. the most important course Stochastic Calculus should be taught much better.
2. the management doesn't care much about publicity. i think for a professional program this is very important. the more people appreciate the program the more value that the current students and previous graduates get.

Suggestions for the program to make it better
I would hire a public relationship manager to advertise the program better.

What are your current job status? What are you looking for?
as a quant strategist in energy trading. i want to expand my skill set further and look for more challenging opportunities, e.g. trading, portfolio manager, deal originator, etc