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  MSc Computational Finance (Year in Industry)


Royal Holloway, University of London    Department of Economics

Full time September MSc 2 years full time Award winner
Business & Management (5) Computer Science (8) Finance (14)

FindAMasters summary

The MSc Computational Finance (Year in Industry) programme offered by Royal Holloway is a specialized course that combines the expertise of the Department of Computer Science and the Department of Economics. This programme equips students with the necessary skills in quantitative finance and computational methods for financial modelling. With a strong emphasis on practical applications, students will learn how to analyze and evaluate computational finance methods, work with machine learning algorithms, and implement these techniques using programming languages such as MATLAB and R. The programme also offers the unique opportunity to gain real business experience through a year-long industry placement. Entry requirements include a 2:1 degree in a relevant subject, but high 2:2 or relevant work experience will also be considered. Graduates from this programme have excellent employability prospects in the thriving field of data science.

About the course

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:

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Entry Requirements

2:1 degree in 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.




 Course Content

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