Cancer Genomics and Data Science

Entry requirements

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.

Months of entry

September

Course content

Biomedical science is increasingly data driven and a wide range of state-of-the-art techniques in cancer genomics and data science is required to analyse multi-layer large scale cancer datasets and derive meaningful interpretable results. However, there is a serious shortage of well-trained people who have the relevant skillset and hands-on experience in real world biomedical and cancer data.

• Join a programme designed and delivered by world-class experts in genomics and data science, who actively develop and apply computational tools to answer research questions
• Gain hands-on experience using real world patient and experimental data
• Learn up-to-date analytic techniques and bioinformatics/computational tools in biomedical and cancer research
• Complete a substantial individual research project to expand your analytic skills and research experience
• Choose the study option that suits you best: full-time, part-time, on campus or online

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.

You’ll be taught by academics who are actively engaged in developing bioinformatics and computational tools, and applying them in cancer and medical research areas such as genomics, proteomics, evolution, modelling and biomarker discovery. We have an extensive network of academic and industrial collaborators around the UK, who contribute to teaching, co-supervise research projects and provide employment opportunities.

Find out more about the online programme