University of Oxford - MSc Mathematical and Computational Finance

Lectures - 8/10:
The lectures were generally of good quality, except for some guest lecturers which did not live up to the same standard as the university ones. The content was generally challenging (and could be very challenging without a solid background), but delivered well. My main complaint is that they often felt crammed in to 16 hours due to nature of the Oxford term structure where other universities would offer 20-30 hours to cover the same content.

Seminars - 5/10:
The quality of the seminars was largely dependant on the DPhil student delivering the class (as with many universities). There were some courses which had very useful and informative seminars, but many which were poor and disengaging. This was often made worse by them being held in large lecture theatres (likely due to increased numbers of students on the course), meaning there was often little student engagement.

Careers - 7/10:
I'm quite torn with this one. I (naively) started the year thinking that having this course on my CV would walk me relatively easy into an entry level quant job - this is not the case. In the end the whole application cycle was completely demoralising with little support but a lot of pressure from the course director and other students to keep on
applying. However, I ultimately did walk away with two internships (one as a dissertation organised through the university and one off my own back), so in a sense the process worked. Hence I would say the university does everything you would expect it to to help you, with a decent careers service, and does organise dissertations in industry for some students, but does not go to the lengths some other courses do.

Exams - 8/10 (written papers), 7/10 (C++), 2/10 (Stats/DL):
The written exams are as you would expect (although it can be difficult to prepare with no revision lectures or solutions to past papers). By far the worst exams are the 48hr Deep Learning and Stats exams. While being open book eases revision, they encourage people to work through the night and are horrendous.

Student Experience - 3/10:
The intense and short nature of the course makes the student experience hard and isolating, with occasional parts that make you remember you're at Oxford (like matriculation and formals). As with any degree, it's what you make of it, but the workload on top of the internship applications makes it hard to find time to do other stuff compared to standard 12 months MSc with a bit more time.

Misc:
- Despite the above, the name of this course and the Oxford name goes a long way.
- The introduction week is horribly intense and can feel completely overwhelming, and was actually much worse than the main course for me. Try to prep some stuff in advance (particularly probability theory) if you can.
- I would not recommend this course for people wanting to do PhDs - they barely talk about them and this year of studying was so intense it put me off doing one (and it's way more expensive for most people than the 4th year of an integrated masters).

Pros:
- the whole Oxford experience, i.e. collegiate system, formal dinners, networks, meeting people from various background
- course introduces you to the main concepts in mathematical finance and related mathematical backgrounds (although given that the course is only 9 months long, it is often not that rigorous)
- the Oxford name for applications

Teaching: In general, the teaching quality is average to good. However, it varies significantly between different lectures (i.e. there are some with rather poor quality for Oxford standards). In contrast to undergrad teaching, there are no tutorials with 1-3 students but classes with -20 students taught by (mostly) DPhil students (hence of varying quality). Many professors tend to use slides (which I personally do not like for lectures focusing on mostly mathematical concepts, proofs, etc.)

Assessment: Exams/Take-Home Exams/Online Exams happen at the beginning and end of the Christmas and Easter break (i.e. December, January, March, April), so most of the time in the break has to be used for studying. Deep Learning and Statistics are 48h hour take-home exams/projects which can be quite exhausting. Moreover, in the written exams multiple lectures are assessed in one sitting (e.g. Stochastic Calculus and Financial Derivatives, or Fixed Income, Stochastic Control and Risk Management).

Dissertation: In the last term (i.e. Trinity) there are no lectures/classes, only the dissertation. Nevertheless, due to the short timeframe (-10 weeks) it can still be stressful. The Maths Institute provides a list with possible dissertation topics. Alternatively, the dissertation can be done as part of an internship. The quality of supervision is also very dependent on your supervisor (and/or industry supervisor if you are doing an internship)

Stats
Students: 42
Length: Around Sep to July
Cost: ~30K GBP for tuition . Possibly closer to 40K GBP now.

CAVEAT and Summary:
+ Personally my opinion may be somewhat biased towards a buy-side quant perspective. Buy side (prop trading, market makers) firms typically tend to hire from Oxford undergrads, or for quant research roles from DPhil level. This means that MScs are in a sort of awkward spot IMO, although many HFs do hire quant researchers from MSc level and above. If you are looking for buy side quant roles, the course is still good, but you could consider other options as well (Machine Learning / Statistics / Computer Science masters instead) . All in all, this does not mean it is not possible to find employment in a buy-side role after the MSC MCF - on Linkedin many people from multiple cohorts who have done so, it is just very competitive.
+ HOWEVER, if you are looking for a derivatives pricing / risk / bank quant type role, the course is an excellent choice. If you are an international student looking , then placement generally seems to be good. In this sense, the ROI from the course is good.
+ ULTIMATELY if you are interested in attending Oxford, and enjoy (or are open to enjoying) the topic of mathemtical finance then the course is good as well.

General Pros:
+ Access to the Oxford experience - formals, colleges - and the benefits of *being* in Oxford - being surrounded by interesting people from many background, access to events (careers, Union etc.).
+ Math Institute is a very nice building. Free coffee. MCF students have their own study room.
+ It is a good bridge if you studied a STEM degree and are looking for some knowledge of financial concepts and products. OR if you are looking to delay graduation to continue applying for internships / roles.
+ Oxford does indeed more open doors on your CV. Compared to Imperial's programme, Oxford probably more well known internationally.
+ Good preparation if you want to continue with a DPHil, e.g. the Random Systems. You get to meet academics you potentially want to be supervised by.

Course Content
+ The course content is rigorous, a lot of breadth into many topics in mathematical finance. The course will give you a very broad and comprehensive overview of stochastic calculus / derivatives pricing. If you enter a bank pricing role, you will have a good foundation.
+ For buy-side roles maybe statistical / machine learning / portfolio optimisation / trading. The Statistics, Deep Learning, and Risk modules offer some foundation towards these. However, you are not as competitive as a pure Stats / ML Masters, but you have the advantage of having some domain knowledge of financial concepts (although many firms say prior knowledge of fiannce is not needed) and being aware of how to apply ML / statstics to finance . For electives, only 2 modules towards these are available : Asset Pricing and Market Microstructure.

Teaching
+ Some professors are very good. I liked the modules taught by industry practitioners, in particular. Statistics with Dr Babbar and Asset Pricing with Dr Dan Jones and Prof Cartea. Microstructre with Prof. Obloj, Derivatives with Prof. Cont were good as well. All modules were very technical (although some may be a bit dry, most of them were still blackboard based lectures.). However bear in mind the academic responsible for each module may change every year.
+ Unlike Oxford undergrads, there are no tutorials, only workshops where DPhil students go over problem sets with half the cohort.
+ A feeling IMO is that the quality of MSc teaching is not the highest priority; given that many of the academics are world-class in this space, they are more preoccupied with their own research, or otherwise, the teaching at the DPhil / undergrad supervision level.

Assessment
+ Much of the assesment and grade is exam-based in the Lent Term only (i.e. do all exams before dissertation) which may be good or bad. There are some coding based projects , for the statistics, deep learning and C++ modules. Personally I would have preferred more project based assesment, but exams makes sense for the stochastic calculus / maths based courses.

Dissertation, Internship, Supervision:
+ The dissertation be done as part of a internship project or as a topic set by an MCF group academic in the Summer term (roughly April to July). A handbook of potential internships is given, which are mostly at banks, and practitioner lectures are given where they advertise these internships. HOWEVER, you do have to apply yourself and go through the interview process, although the process may likely be expedited (not fully sure). You can also look for internships on your own and see if the firm can allow you to do your internship. IMO, the key advantage of this is the internship component - basically try and get closer to locking in a return offer if not already.
+ If you do a industry internship, the additional benefit is that you have both a Oxford supervisor and an industry supervisor. In my experience, my industry supervisor was extremely helpful , but the dissertation project was not something the firm had done before, so could not offer a lot of insight into what technical direction to pursue next (although this is probably typical in academic research). On the other hand , IMO my Oxford supervisor was not that helpful, although this definitely varies person to person.

Employment
+ Broadly speaking , most students who wanted to stay in the UK were able to find some quant role. Majority of them are in quant derivatives pricing / risk related roles in banks. Many continued after doing the dissertation in industry. Fewer are in buy-side / HF roles. You can go on Linkedin to find more stats if interested.
+ Advice on this front : Start applying to roles immediately (earlier the better) , if your goal is full time employment, instead of relying solely on the internship handbook.


Didn't like:
+ C++ course is not that useful IMO. You are mainly writing classes with some propriety library (possibly unchanged from 2014?). If you end up in a pricing role, you will learn C++. If looking for a quant dev / software engineer role, you will not be as proficient in C++ as a CS undergrad. You need to self-learn algos / data structures to pass Leetcode screenings if you are not familiar already.
+ Course content is not evolving a lot. Innovation is a single new course on Decentralised Finance. VS Imperial's programme does not appear to be as wide range of courses available. Big focus on derivatives pricing, stochastic calculus (although it IS a Mathematical Finance degree)
+ Not a lot of budget available for cohort social events. Would be good to build more of a rapport .

Basic Stats
# of Students: 29
# of Applications: ~500 (bear in mind the admission test narrows this down considerably vs. other schools)
Length: 10 months
Cost: ~25k GBP (tuition and college fees) + 1-2k living costs per month?
This should be on the website: https://www.maths.ox.ac.uk/courses/mathematical-finance/msc-mcf

Employment Statistics
Post graduation employment statistics aren't given so I'll give this year's breakdown from what I know.

Full Time: 5 - 7 (2 had return offers from before the course)
Internships: 6 - 9
Further study: 1 - 3
(Statistics taken just before the end of the course - no doubt some will find jobs after the course etc..)

From my sources, previous cohorts also had similar employment statistics at this stage in the course.

All jobs are based in London, except for 1 in Europe, 1 in Hong Kong and 1 in the US.
Employers include the top tier IBanks, a quant hedge fund and a trading firm.

You will get (some) interviews from the reputation of Oxford mathematics but this alone will not secure you a job.

What I liked
General Oxford experience - old architecture, punting, societies, Oxford Union etc...
Top notch facilities - especially the new Mathematical Institute building. It's arguably the coolest math building in the world :D.
Excellent teaching - Apart from one or two lecturers, the quality of teaching is exceptional. You are taught by some fairly big names in financial mathematics.
Solid coursework - The coursework is very rigorous and a lot more theoretical than other programs. The overall focus of the course has been on derivatives pricing although you have an option next year to focus on data-driven topics e.g. algo trading and market microstructure.

What I didn't like
Course structure - This is by far the biggest complaint among the current students and to Oxford's credit the course will be restructured next year (2014/2015) in light of this. We learned programming far too late to be useful in interviews and were expected to find jobs at the very beginning of the course when the graduate recruitment season began.
C++ Programming Courses - You learn the basics and then learn how to use and extend the lecturer's own library. This is inadequate preparation for quant interviews and actual quant work. You will need to study some Comp. Sci (e.g. sorting algorithms) yourself.
Course is too intensive/long. The course tries to pack in too much material for the 10 months and as a result you will be pressed hard. By the start of the final semester only less than 40% of the course marks have been assessed. Make no mistake - this course is one of the most difficult in Oxford.
No careers service - you have a careers office (outside of the department) but nothing else. No one is actively searching for roles for you unlike at some top US programs. You might also get some networking sessions/presentations from banks/HF's but that's about it.
What I'm neutral about
Dissertation and Miniprojects - These were very time consuming - you need to do all of these during Trinity term. That's around 60-80 pages of stuff you are required to write in 8 weeks or so. On the other hand, these projects were a good way to reinforce material learned in the first two semesters.
Overall opinion
Overall, the course was stimulating and engaging - definitely worth the money. Although the course wasn't as great as I expected it to be, it was still excellent.

Advice for potential applicants
If you want to pursue a DPhil/PhD, this program is for the most part theoretical enough to help you in admissions interviews especially in stochastic processes/PDE's. The issue is with timing - ideally you should apply towards the end of the course when you have marks and know the faculty better but unfortunately the isn't usually the case.

If you want to apply hoping to find a quant job after graduation, my advice is to be prepared.
Students with no experience in finance beforehand only managed to secure internships at best. Those students who managed to obtain full time work had internships behind them and the Oxford brand only helped to land interviews. The job hunt begins as soon as you arrive - banks' graduate recruitment opens near the start of the course. You will not have had sufficient time to study programming/brainteasers/stochastic calculus etc. to succeed in any early interviews. Statistics/Time-series analysis are also useful skills to have going into the course.

Also Oxford's location should not factor into your decisions - the bus to London takes only 1-2 hrs.

US vs. UK
The top US quant finance programs (Columbia, CMU, Princeton etc.) have better careers services - they have active alumni recruitment programs or whatever they call it.
US programs are longer (1.5-2 years) and UK programs are shorter (1 yr). More time to get internships and to land that full time job.
In the US you get jobs via networking and over here you have to apply online mostly.
The US market seems a lot more competitive than in the UK.

It feels like that there are more investment banking quant jobs in London but fewer prop shop or hedge fund jobs.

What other courses should I consider (in the UK)?
Imperial has better careers services and employment outcomes - I'd do their industrial training over a dissertation anyday! LOL
Cambridge Part III is cheaper and just as employable/reputable as Oxford, imo. However, some say it's even more 'hardcore' than even the Oxford MSc MCF.
I don't know about LSE but my fellow students say its much worse than Oxford.

Frankly, you will have difficulty finding quant jobs if you are in lower ranked universities. Banks' tend to hire from Oxford, Cambridge, Imperial, LSE in no particular order.