Complete your statistics training while studying the mathematical concepts, models and tools of the finance industry. Course description The course trains you to apply the probabilistic, statistical and mathematical techniques that are used in the finance industry. It's based on our Statistics MSc course, but also includes key financial topics such as the Capital Asset Pricing Model, the Black-Scholes option pricing formula and stochastic processes. You’ll also develop a detailed working knowledge of more general statistical techniques and concepts, including linear and generalised linear modelling, Bayesian statistics, time series and machine learning. You’ll learn how to analyse and draw meaningful conclusions from data, and develop your programming skills using the statistical computing software R. Around one-third of the course is devoted to your dissertation. This may focus on investigating a data set, or a more theoretical or methodological topic. The aim is to give you skills to include on your CV, such as planning and researching a project, data acquisition, problem specification, analysis and reporting your findings. Dissertation topics are often provided by external clients – for example, pharmaceutical companies or sports modelling organisations. Distance learning students often come with projects designed by their employer. Accreditation Accredited by the Royal Statistical Society Please see our University website for the most up-to-date course information.
How to apply
Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
We ask for a 2:1 honours degree, or equivalent, with substantial mathematical and statistical components. In particular, you should have studied the following topics and performed well in assessments on them (for example, a score of at least 60 per cent). Mathematical Methods for Statistics: ideas and techniques from real analysis and linear algebra, including multiple integration, differentiation, matrix algebra, the theory of quadratic forms. Probability and Probability Distributions: the laws of probability and of conditional probability, the concepts of random variables and random vectors and their distributions, the methodology for calculating with them; laws of large numbers and central limit phenomena. Basic Statistics: statistical inference, rational decision-making under uncertainty, and how they may be applied in a wide range of practical circumstances; relevant software, for example, R. Real analytics, probability and stochastic process English language requirements: Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
Fees and funding
No fee information has been provided for this course