Description

This unique programme provides numerate graduates with the requisite expertise for the development of a professional career in the profitable and intellectually exciting triangle formed by mathematics, technology and finance.

Financial institutions rely on a functional blend of Mathematics, Technology and Finance to develop, enhance and sustain their competitive edge. The financial industry is undergoing a second wave of technological transformation related in particular to¿: the establishment of electronic trading platforms; improved risk management and pricing accuracy; the high performance computing implications of expanding regulatory requirements.

As a result there is increasing demand for numerate and technologically capable personnel from a wide range of top employers including investment banks, hedge funds, financial software companies, brokerage firms and consultancy firms. Other business lines are now developing similar paradigms where numerate, technologically able personnel are part of business innovation and decision-making.

The Financial Computing MSc is run jointly by the School of Mathematical Sciences and the School of Electronic Engineering and Computer Science. It is aimed at science and engineering graduates with mathematical exposure and some experience in computer programming. The content of the programme is a combination of technology and financial mathematics. It contains modules related to up-to-the-minute industry challenges such as high performance and GPU development.

Study Details:

MSc 1 year Full time 2 years Part time

Module Details:

Semester A – Compulsory

  • ECS793P Introduction to Object-Oriented Programming
  • MTH771P Foundations of Mathematical Modelling in Finance
  • MTH739N Topics in Scientific Computing
Semester A – Elective
 
Choose any one from:
  •  ECS713P Functional Programming
  • ECS765P Big Data Processing
  • ECS708P Machine Learning 
Semester B – Compulsory
  • MTH789P Trading and Risk Systems Development
Semester B – Elective
 
Choose any three from:
  •  MTH773P Advanced Computing in Finance
  • ECS769P Advanced Object-Oriented Programming
  • ECS784P Data Analytics
  • ECS786P Parallel Computing
  • MTH774P Portfolio Theory and Risk Management
  • MTH772P Stochastic Calculus and Black Scholes Theory 
The Project

 

Programme Funding

There are a number of sources of funding available for Masters students.

These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.

Student Destinations

The staff involved in the MSc of Financial Computing have strong links and research collaborations with industrial partners including Citigroup, Nomura, Bank of England, Morgan Stanley, UBS, RBS, Lloyds, Moodys, IBM, HP, BBC, and Tech City IT start-ups. Several of these companies will be involved in the teaching activities, providing guest lectures, as well as business use cases for applying Financial Computing techniques. Additionally, several of the MSc projects offered to the students will be performed in collaboration with an industry partner, including summer placement opportunities.

Fees

Home and EU students 2018/19 Academic Year Full time £18,050 Part time £9,050 International students 2018/19 Academic Year Full time £20,900 Part time £10,450

Entry Requirements

The majority of our applicants will have an undergraduate degree with first class or upper second class honours (or international equivalent).  Offers will typically be made at 2.1 level (upper second class) or equivalent.  Students with a good lower second class degree may be considered on an individual basis.  In some cases your offer may include additional conditions, such as minimum grades in specified modules, in order to ensure that you are sufficiently qualified for our MSc programmes.

Students applying to this programme should have studied a subject with a substantial mathematical component at the undergraduate level.  We welcome those from a variety of relevant disciplines, including mathematics, statistics, physics, engineering, economics and computer science.

Please see our website for how to apply

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