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
- 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.
Data analysis (20 credits)
Database systems (*) (10 credits)
Foundations of Finance (20 credits)
Investment and portfolio management (20 credits)
Programming for data analysis (10 credits)
Individual project (60 credits)
(*) This module is compulsory and available only for students who lack background in the corresponding area.
In addition to these mandatory course units there are a number of optional course units available during your degree studies. The following is a selection of optional course units that are likely to be available. Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.
Advanced Data Communications (10 credits)
Advanced Distributed Systems (20 credits)
Applied Probability (20 credits)
Business Intelligence Systems, Infrastructures and Technologies (20 credits) Computation with Data (10 credits)
Computational Optimisation (10 credits)
Computer Security (20 credits)
Concurrent and Parallel Programming (10 credits)
Corporate Finance (20 credits)
Cyber Security (20 credits)
Decision Theory and Behaviour (20 credits)
Deep Learning (20 credits)
Digital Forensics (20 credits)
Financial Econometrics (20 credits)
Fixed Income Securities and Derivatives (20 credits)
Inference (20 credits)
Intelligent Agents and Multi-Agent Systems (10 credits)
Interconnected Devices (10 credits)
Introduction to Cryptography (20 credits)
Large-scale Data Storage and Processing (20 credits)
Machine Learning (10 credits)
Methods of Bioinformatics (10 credits)
Methods of Computational Finance (10 credits)
Micro-econometrics (20 credits)
Network Security (20 credits)
On-line Machine Learning (20 credits)
Security Management (20 credits)
Security Technologies (20 credits)
Security Testing (20 credits)
Semantic Web (10 credits)
Smart Cards, RFIDs and Embedded Systems Security (20 credits)
Software Security (20 credits)
Topics in Applied Statistics (20 credits)
Visualisation and Exploratory Analysis (10 credits)
Wireless, Sensor and Actuator Networks (20 credits)
Please note that some of these modules may have pre-requisites. During Welcome Week, your personal advisor will help you choose the electives that best suit your background and career ambitions, as well as your timetable.
Teaching is organised in terms of 11 weeks each. Examinations are taken in April/May of each academic year, except for Data Analysis for which the exam is in January. The individual project is taken over 12 weeks during the Summer.
A weekly seminar series runs in parallel with the academic programme, which includes talks by professionals in a variety of application areas as well as workshops that will train you to find a placement or a job and lead a successful career.
Assessment is carried out by a variety of methods including coursework, small group projects, and examinations, the proportions of which vary according to the nature of the modules.
This degree can be taken part-time - visit our website for details
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.
International and EU entry requirements
Please select your country from the drop-down list below
Students from overseas should visit the International pages for information on the entry requirements from their country and further information on English language requirements. Royal Holloway offers a Pre-Master’s Diploma for International Students and English language pre-sessional courses, allowing students the opportunity to develop their study skills and English language before starting their postgraduate degree.
Demand for data scientists is buoyant, in the UK and worldwide, with salaries much higher than other IT professions and at least double the UK average full time wage. All our graduates found jobs at the end of (if not during) their studies.
We bring several companies to our campus throughout the year, both for fairs and for delivering advanced topics seminars, which are an excellent opportunity to learn about what they do and discuss possible placements or jobs.
Together with the University’s Careers Service, we offer you workshops and one-to-one coaching that prepare you to find a placement or a job and lead a successful career.
- 90% of Royal Holloway graduates in work or further education within six months of graduating.
- Strong industry ties help to provide placement and networking opportunities.
- On-site Careers Service provides help and support for students.
Home and EU students tuition fee per year 2017/18*: £8,300
Overseas students tuition fee per year 2017/18*: £18,500
A scholarship of £2,000 will be automatically provided (and deducted from the fees payable) for overseas students holding the equivalent of a UK first-class undergraduate degree.
Other essential costs**: There are no single associated costs greater than £50 per item on this course
Find out more about funding options, including loans, grants, scholarships and bursaries.
*The tuition fees given above apply to students enrolled on a full-time basis. Students studying part-time are charged a pro-rata tuition fee and information is available from the Royal Holloway Student Fees Office on Student-Fees@rhul.ac.uk. All fees are likely to rise annually in line with inflation but no more than 5 per cent per year.
For further information, please see Royal Holloway’s Terms & Conditions.
** These estimated costs relate to studying this particular degree programme at Royal Holloway. Costs, such as accommodation, food, books and other learning materials and printing etc., have not been included.