Cancer Genomics and Data Science (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.

Cancer Genomics and Data Science (Online) from Queen Mary University of London 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.

Key facts

• You’ll leave this program with competence in state-of-the-art analytic workflows and hands-on experience of a wide range of real-life cancer and medical data, so that you will be ready to meet research and industry needs after graduation.
• There is high demand for well-trained bioinformaticians and computational biologists to manage, analyse, integrate and visualise “big data”, both in academia and industry. Bioinformatics and data science skills are highly transferable, allowing skilled individuals to move to other sectors, such as data analytics, software development and quantitative finance.
• We anticipate that graduates from this program are likely to move into roles such as:

• Bioinformatician
• Data analyst
• Computational biologist
• Researcher
• In both academia and industry, including large pharmaceutical companies, small and medium-sized enterprises, and start-ups.

Courses included:

• R and Python Programming in Biomedical Research

• Omics Data Analytics and Practical Training

• Computational Genomics, Transcriptomics and Evolution

• Mathematical Modeling and Application

• Single Cell Analytics

• Machine Learning/Al and Application to Biomedical Research

• Cancer Genomics and Data Science Research Project