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University of Leeds: Precision Medicine: Genomic Data Science

Institution University of Leeds
Department Faculty of Biological Sciences
Telephone 0113 343 1418
Study type Taught


The rapid transformation of healthcare through personalised genomic medicine is matched only by the consistently growing demand for talented graduates with the right skill-set.

From early diagnosis, to drugs based on our unique genetic codes, to disease prevention, there is a huge demand for more biomedical scientists with analytical skills. Responding to this gap, this unique course has been designed to directly meet the need for those with both biological knowledge and the computational and analytical interest to drive genomic precision medicine.

Whether you’re experienced with data analysis or not, this course will develop your skills and provide extra support for those students who are less confident in their mathematical ability.

You will gain the skills to use large volumes of complex data, encompassing genomics, proteomics, metabolomics, phenotypic data, epidemiology and clinical trial investigations, to improve the understanding of disease mechanisms.

Upon completion, you will find you have an excellent chance of moving into the analytical genomics field in industry, the NHS and academia.

Course highlights

  • You’ll benefit from a research-led education at the forefront of the most innovative data analytics research at Leeds

  • The University of Leeds is now a partner of The Alan Turing Institute, the UK’s prestigious national institute for data science. The partnership is spearheaded by the Leeds Institute for Data Analytics (LIDA).

  • Research in LIDA sheds new light on health and human behaviour and the action needed to tackle a wide range of medical, social and environmental problems.

  • LIDA is supported by national programmes such as the MRC’s Medical Bioinformatics scheme.

  • Leeds has recently been the recipient of huge investments from funding councils across the UK, such as the recent £10 million investment in artificial intelligence and digital pathology


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