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Students will become expert in linking and analysing large complex datasets, using techniques which are transforming medical research and creating exciting new commercial opportunities. Graduates will be equipped for roles in the pharmaceutical industry, the NHS and technology start-ups, as well as academia.
Students learn how to design and carry out complex and innovative clinical research studies that take advantage of the increasing amount of available data about the health, behaviour and genetic make-up of small and large populations. The content is drawn from epidemiology, computer science, statistics and other fields, including genetics.
Students undertake modules to the value of 180 credits.
The programme consists of five core modules (75 credits), three optional modules (45 credits) and a dissertation/report (60 credits).
A Postgraduate Diploma (120 credits) is offered.
A Postgraduate Certificate (60 credits) is offered.
Core modules
- Principles of Epidemiology Applied to Electronic Health Records Research
- Data Management for Health Research
- Statistics for Epidemiology and Public Health
- Statistical Methods in Epidemiology
- Topics in Health Data Science
Optional modules
- Advanced Statistics for Records Research
- Database Systems
- Information Retrieval and Data Mining
- Principles of Health Informatics
- Machine Learning in Healthcare and Biomedicine
- Statistics for Interpreting Genetic Data
Dissertation/report
All students undertake an independent research project which culminates in a dissertation.
Teaching and learning
The programme is delivered by clinicians, statisticians and computer scientists from UCL, including leading figures in data science. We use a combination of lectures, practical classes and seminars. A mixture of assessment methods is used including examinations and coursework.
A minimum of an upper second-class Bachelor's degree, or equivalent, in a clinical or a scientific discipline with a significant computational or mathematical element.