University of Oxford - MSc Mathematical and Computational Finance

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 .