MSc Cancer Genomics and Data Science

Biomedical science is increasingly data driven, as new bioanalytical techniques deliver ever more data about DNA, RNA, proteins, metabolites and the interactions between them in the whole tissue and single-cell levels. A wide range of state-of-the-art techniques in the field of cancer genomics and data science for example modelling, data integration, machine learning and AI is required to analyse multi-layer large scale cancer datasets and derive meaningful interpretable results.

However there is a serious shortage of well-trained bioinformaticians, computational biologists and data analysts who have the relevant skillset and experience in real world biomedical and cancer data. This programme is designed to fill the gap between research and employment demands and student training, offering up-to-date modules focusing on big-data analyses and enabling these through use of high-performance computing, together with cutting edge research projects and practical training using real world cohort data.

A 2:1 or above at undergraduate level in any subject, provided the degree contains satisfactory study of Mathematics and Statistics. Subjects likely to contain sufficient quantitative elements include Genetics, Genomics, Bioinformatics, Mathematics, Statistics, Engineering, and Computer Science. Applications from those with less quantitatively oriented Natural Sciences degrees, such as Biology and Medicine, are welcome if they have focused on the more quantitative elements of those degrees. Applicants with a 2:2 degrees with relevant content and at least one year of relevant experience, for instance work in industry, analytics, diagnostic labs, scientific research etc, may be considered on an individual basis. IELTS Academic: 6.5 overall including 6.0 in Writing, and 5.5 in Reading, Listening and Speaking. TOEFL: 92 overall including 21 in Writing, 18 in Reading, 17 in Listening and 20 in Speaking.