Royal Holloway, University of London · Full-time
Computational Finance
This degree, 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 pri…
MSc1 yearFull-time
Browse business and management courses in UK
Computer Science · Business and management
This degree, 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 1990’s, 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). + 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. + Choose from a one-year programme structure or add an optional year in industry.
Getting in
As the university states it
2:1 Computer Science, Economics, Mathematics, Physics, or another subject that includes a strong element of both mathematics and computing. Normally we require a UK 2:1 (Honours) or equivalent in relevant subjects but we will consider high 2:2 or relevant work experience. Candidates with professional qualifications in an associated area may be considered. Where a ‘good 2:2’ is considered, we would normally define this as reflecting a profile of 57% or above.
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Course details as published by the university. No graduate-outcome statistics are published for UK postgraduate courses, so none are shown.