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Statistics with Finance MSc

Key information

Qualification type

MSc - Master of Science

Subject areas

Statistics Finance / Accounting (General)

Course type


Course Summary

This course is accredited by the Royal Statistical Society (RSS) and is excellent preparation for careers in any field requiring a strong statistical background. This programme trains students for careers using statistics in the financial services industry. You study the statistical modelling underpinning much modern financial engineering combined with a deep understanding of core statistical concepts. The programme includes modelling of financial time series, risk and multivariate techniques. Statistics at Kent provides: a programme that gives you the opportunity to develop practical, mathematical and computing skills in statistics, while working on challenging and important problems relevant to a broad range of potential employers; teaching and supervision by staff who are research-active, with established reputations and who are accessible, supportive and genuinely interested in your work; advanced and accessible computing and other facilities; a congenial work atmosphere with pleasant surroundings, where you can socialise and discuss issues with a community of other students.

Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers. Recent graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.

Different course options

Study mode

Full time


1 year

Start date



This module will provide a full description of Bayesian analysis and cover popular models, such as the normal distribution. Initially, the flavour will be one of describing the Bayesian counterparts to well known classical procedures such as hypothesis testing and confidence intervals. Current methods for inference involving posterior distributions typically involve sampling strategies. That is, due to the complicated nature of some posterior distributions, analytic methods fail to provide meaningful summaries. Hence, sampling from the posterior has become popular. A full description of sampling techniques, starting from rejection sampling, will be given.
MA942 - Data Science with R (15 Credits) - Core

Tuition fees

UK fees
Course fees for UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


International fees
Course fees for non-UK / EU students

For this course (per year)


Average for all Postgrad courses (per year)


Entry requirements

Students need to have a minimum of 2.2, with a substantial amount of mathematics at university level. Prior experience of finance is not required. All applicants are considered on an individual basis and additional qualifications, and professional qualifications and experience will also be taken into account when considering applications.