Modules

Year 1 The first year consists of 100 compulsory Mathematics credits: •Analysis (20) •Calculus (20) •Linear Algebra (20) •Dynamics (10) •Probability (10) •Programming (10) •Statistics (10) Together with a further 20 credits which can be chosen from: •Discrete Mathematics (20) •Any other available Sciences, Arts and Social Sciences modules (subject to pre-requisites and timetabling compatibility) The first-year Mathematics modules expand and develop topics that may be familiar from A level (or equivalent), smoothing the transition to university study. Fundamental statistical methodologies are developed from first principles in the Statistics and Probability modules, providing a mathematical language and coherent conceptual framework with which to structure subsequent developments. Other modules equip you with the essential mathematical tools needed for further study. Year 2 In the second year, you will take four compulsory modules (60 credits): •Analysis in Many Variables (20) •Statistical Inference (20) •Data Science and Statistical Computing (10) •Statistical Modelling (10) Together with a further 60 credits which can be chosen from a wide range, including: •Complex Analysis (20) •Numerical Analysis (20) •Markov Chains (10) •Mathematical Modelling (10) •Probability (10) The four compulsory modules will furnish you with the central mathematical, inferential, modelling, and computational tools needed for modern statistics and machine learning, as well as looking at important surrounding issues such as data governance. Further modules allow you to broaden or deepen your knowledge of particular topics or techniques. Year 3 In the third year, you take a 40-credit capstone project module, tackling a theoretical area or an applied problem in particular depth. Subject to availability, this may be performed in collaboration with a company or other organisation. For the remaining 80 credits, you choose from a range of modules on topics central to modern statistics and machine learning, including: •Advanced Statistical Modelling (20) •Bayesian Computation and Modelling (20) •Decision Theory (20) •Machine Learning and Neural Networks (20) •Mathematical Finance (20) •Stochastic Processes (20).