Digital Health MSc

The modules in this programme have varying methods of delivery and assessment. For more details about each module, including weekly contact hours, teaching methods and assessment, please see the latest module catalogue, which is for the 2018–2019 academic year; some elements may be subject to change for 2019 entry.

Compulsory

Students take five compulsory modules.

• 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.
• Computing in Statistics: introduces and provides experience with the software package SAS and the statistical language and environment R.
• Data Analysis: provides coverage of essential statistical concepts, data manipulation and analysis methods, and software skills in commercial analysis packages.
• Knowledge Discovery & Datamining: covers many of the methods found under the banner of "Datamining", building from a theoretical perspective but ultimately teaching practical application.
• Statistical Modelling: 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 two of the following optional modules.

• Data-Intensive Systems
• Information Visualisation: 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
• Programming Principles and Practice: introduces computational thinking and problem solving skills to students who have no or little previous programming experience.

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.

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.