Course overview

Receive interdisciplinary training in 'big data' analysis in relation to biomolecular studies on this Master's course.

On this stream of the MRes in Biomedical Research, you'll receive core training in multivariate statistics, chemometrics and machine learning methods.

The course will build your research experience in the development and application of these methods to real-world biomedical studies.

You'll also learn to handle large-scale data from molecular phenotyping techniques such as metabolic profiling and related genomics approaches.

Two research projects are major components of this course.

These will help you develop communication, presentation and grant-writing skills, and become familiar with evaluating research reports.

Choose your stream

You have the option of choosing our general biomedical research stream, or one of eight specialisms. All of our biomedical research streams have the same course structure and each stream has its own tailored set of projects alongside a core programme of lectures, seminars and practical classes.

You should consider which stream is right for you according to your career aims and background. If an offer of admission is made, it will correspond to a specific stream. Switching streams is not possible once you have commenced your studies.

• General Biomedical Research
• Anaesthetics, Pain Medicine and Intensive Care
• Bacterial Pathogenesis and Infection
• Data Science (this stream)
• Epidemiology, Evolution and Control of Infectious Diseases
• Microbiome in Health and Disease
• Molecular Basis of Human Disease
• Respiratory and Cardiovascular Science

Is this stream for you?

This stream is suitable for students with a background in physical sciences, engineering, mathematics, computer science or similar field who wish to apply their numeric & computational skills to solve problems with biomedical data.

You will gain experience in analysing and modelling big data from technologically advanced techniques applied to biomedical questions.

You will perform novel computational informatics research and exercise critical scientific thought in the interpretation of results, implement and apply sophisticated statistical and machine learning techniques in the interrogation of large and complex biomedical data sets.