Details of the six core modules are as follows:

• Probability and classical inference - This module lays the theoretical foundations for the course, providing a solid grounding in probability and covering all of the essential theory of classical statistics.
• Advanced regression modelling - Regression  models seek to explain variation in one variable, the response variable, in terms of other variables, the explanatory variables. This module covers many important models of this type, including linear models for regression and the analysis of variance, and generalised  linear models for the analysis of contingency tables and data that arise as counts or proportions.
• Bayesian methods - Bayesian inference has become central to modern statistical analysis by providing a very general way of tackling statistical problems. This module covers  modern, simulation-based methods, and is illustrated by hands-on computing experience, using packages such as WinBUGS.
• Practical statistics and computing - This module is concerned with the practical analysis of real data, report writing, oral presentation and consultancy skills, all of which are essential tools for the modern Statistician.  The main computer package used is R.
• Computational statistics - This module revises and integrates material from other modules, and reveals the power of Statistics for analysing novel and challenging data sets. Theory is complemented by hands-on practical experience using computer programs written in Matlab and in R.
• Principles of data collection - This module explores the important roles that Statisticians play in planning how data are collected. The two main types of study considered are sample surveys and designed experiments. Especially important are topics such as questionnaire design and the design of clinical trials to study the performance of new drugs.

• Stochastic Processes and Time Series - Time-dependent data play an important role in everyday life, enabling us to gauge changes to the climate, to analyse stock-market prices, to determine how to model  waiting times, as in hospital, and so forth. This module covers the essentials of the analysis of stochastic processes and also the analysis of time-series data.
• Stochastic Models in Ecology and Medicine - The two topics of this module are of great current importance. For instance in ecology we might be interested in what drives populations of wild animals to change over time, and in medicine  we need methods to determine how diseases develop and progress, which factors are important, and how effective are interventions.
• Analysis of Large Datasets - Huge data sets are now routinely collected in many applied areas such as economics, finance, biology and the social sciences. In this module techniques are presented to simplify such data sets, and uncover hidden, important structure

Students choose their project topic in consultation with members of staff, from a wide range covering both practical data analysis and more theoretical work. Students on the MSc in Statistics with Finance will work on topics in the appropriate subject area. Students working on their projects obtain regular supervision from their supervisor(s).