Students take four compulsory modules.
- Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.
- 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.
- 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.
- Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalized linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.
Students choose four of the following optional modules.
- Computing in Statistics: teaches computer programming skills, including principles of good programming practice, with an emphasis on statistical computing.
- Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation.
- Data-Intensive Systems: presents the programming paradigms, algorithmic techniques and design principles for large-scale distributed systems, such as those utlised by companies such as Google, Amazon and Facebook.
- Information Visualisation and Visual Analytics: explores how to utilise visual representations to make information accessible for exploration and analysis.
- Masters Programming Projects: reinforces key programming skills gained during the first programming module of the programme and offers increasing depth and scope for creativity.
- Object-Orientated Modelling, Design and Programming: introduces and revised object-oriented modelling, design and implementation to reinforce study in other modules.
- Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience.
Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students (see the University's position on curriculum development).
During the second semester, students work with staff to define and agree upon a topic for the extended project, which they will work on during the final three months of the course, and which culminates in a 15,000-word dissertation. Dissertation projects may be group-based or completed individually (students are assessed individually in either case).
The dissertation typically comprises: a review of related work; the extension of existing or the development of new ideas; software implementation and testing; analysis and evaluation. Students are required to give a presentation of their work in addition to the written dissertation.
Each project is supervised by one or two members of staff, typically through regular meetings and reviews of software and dissertation drafts. Supervisors and topics may be from either of the schools of Computer Science or Mathematics and Statistics and many are in collaboration with companies or other external bodies.
If students choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma instead, finishing the course at the end of the second semester of study.