Modules

Compulsory

• Advanced Data Analysis: covers modern modelling methods for situations where the data fails to meet the assumptions of common statistical models and simple remedies do not suffice.

• Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalised linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.

• Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.

• Knowledge Discovery and Datamining: covers many of the methods found under the banner of "datamining", building from a theoretical perspective but ultimately teaching practical application.

• Multivariate Analysis: introductory and advanced training in the applied analysis of multivariate data.

• Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation.

• Bayesian Inference

• Classical Statistical Inference

• Computing in Mathematics

• Computing in Statistics

• Design of Experiments

• Financial Mathematics

• Markov Chains and Processes

• Advanced Bayesian Inference

• Advanced Combinatorics

• Estimating Animal Abundance and Biodiversity

• Independent Study Module

• Mathematical Oncology

• Data-Intensive Systems

• Database Management Systems

• Information Visualisation

Assessment method

The programme consists of two semesters with taught components which include a mixture of short, intensive courses with a large proportion of continuous assessment and more traditional lecture courses with end-of-semester exams.