Taught course

Computational Finance (Year in Industry)

Royal Holloway, University of London · Department of Economics

Entry requirements

UK Upper Second Class Honours degree (2:1) or equivalent in Computer Science, Economics, Mathematics, Physics, or other subjects that include a strong element of both mathematics and computing.

Relevant professional qualifications and relevant experience in an associated area will be considered.

English language requirements:

IELTS 6.5 overall and minimum of 5.5 in each subscore, for equivalencies see here.

Months of entry


Course content

This course, offered by the Department of Computer Science and the Department of Economics, allows you to specialise in modern quantitative finance and computational methods for financial modelling, which are demanded for jobs in asset structuring, product pricing as well as risk management.

Skills that you will acquire include the ability to:

• analyse, critically evaluate, and apply methods of computational finance to practical problems, including pricing of derivatives and risk assessment

• analyse and critically evaluate methods and general principles of computational finance and their applicability to specific problems

• work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, and neural networks

• analyse and critically evaluate applicability of machine learning algorithms to problems in finance

• implement methods of computational finance and machine learning using object-oriented programming languages and modern data management systems

• work with software packages such as MATLAB and R

• work with Relational Database Systems and SQL

You will be taught by world-leading academics. Research in Machine Learning at Royal Holloway started in the 1990s, at which time V Vapnik and A Chervonenkis (the inventors of Support Vector Machines) were both professors here. We have developed both fundamental theory and practical algorithms that have fed into the analytics methods and techniques that are in use today. Current researchers include Alexander Gammerman and Vladimir Vovk – the inventors of conformal predictors theory, a radically new method of estimating the accuracy of each prediction as it is made – and Chris Watkins, originator of reinforcement learning who developed ‘Q-learning’, a work that is fundamental to planning and control.

• Study in two highly-regarded departments, respectively ranked 11th and 8th in the UK for research quality (Research Excellence Framework 2014).

• Refine your skills and knowledge with a year in industry at one of the country's top institutions.

• Benefit from strong industry ties, with close proximity to England’s 'Silicon Valley’.

• Graduate with a Masters degree with excellent graduate employability prospects.

• Tailor your learning with a wide range of engaging optional modules.

By electing to spend a year in business you will also be able to integrate theory and practice and gain real business experience. In the past, our students have secured placements in blue-chip companies such as Centrica, Data Reply, Disney, IMS Health, Rolls Royce, Shell, Sociéte Générale, VMWare and UBS, among others.

Qualification, course duration and attendance options

  • MSc
    full time
    24 months
    • Campus-based learningis available for this qualification
    part time
    60 months
    • Campus-based learningis available for this qualification

Course contact details

Computer Science Postgraduate Admissions
+44 (0)1784 443432