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:
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.
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.
You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.
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Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff.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.
Teaching & assessment
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. Your 'Year in Industry' typically starts at the end of June or beginning of July and lasts for a maximum of one year. The individual project is taken over 12 weeks during the Summer after the placement.
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. The placement is assessed as part of your degree (10% of the individual project).
Although the responsibility for finding a placement is ultimately with the student, our Careers Team will help you identify suitable opportunities, make applications and prepare for interviews. Please note that progression to the placement is conditional on good academic performance. Students who fail to qualify for or find a placement are automatically transferred to the one-year programme. Find out more on our year in industry page.
Your future career
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. Our graduates have an excellent track record of finding 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.