Health Data Science for Applied Precision Medicine MSc

On this course we welcome students with a background in biomedical or computational science as well as medical professionals, allied health professionals and clinical scientists.

You can learn how Big Data (including genomics, biomarkers and clinical imaging) and electronic patient records, can be applied in a Precision Medicine approach to improve the healthcare of individuals and populations.

Precision Medicine is an area of rapidly growing importance for healthcare organisations globally. It integrates Big Data relevant to healthcare to target safer and more effective treatments to individual patients.

Understanding why individuals differ in susceptibility to disease and response to treatment, and being able to tailor more precise treatment to the individual patient, has clear benefits both for patients and healthcare organisations, as the global Covid-19 pandemic has highlighted.

“Our experience has demonstrated that developing and delivering precision medicine solutions frequently requires working together across diverse disciplines and specialities. We have included, throughout this course, team working exercises, where your own specific skills and knowledge are enhanced through collaboration with those of others from different disciplines.”

Dr Alexander Doney, course director

In this course you will use anonymised real healthcare data linked to genome and clinical imaging data.

You will be taught by renowned biomedical research scientists and doctors applying precision principals in their clinical practice, giving you a comprehensive range of relevant applied skills, insights and knowledge.

Throughout the course you will learn by doing. This will involve:

• Examining principles and current practice of precision medicine and its clinical benefits
• Gaining data management and governance skills for research use of medical data and using R statistical software within a Trusted Research Environment
• Developing knowledge and experience of a range of bioinformatics techniques
• Exploring epidemiology and population genetics and how genes influence drug response and side effects (pharmacogenetics)
• Understanding the basic principles of how Artificial Intelligence and deep learning are applied to clinical imaging
• Producing a prototype grant application and designing, carrying out, interpreting and reporting a systematic review.
• Undertaking a final research project building on the combination of skills, techniques and insights developed throughout the course, to help you consolidate your data science skills.