Online Genomic Medicine with Data Science MSc

Build a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

Gain an understanding of the use of high-throughput biomolecular data generation methods. The emphasis will be on understanding methods and the data that they typically give. Techniques covered will include whole genome/exome sequencing, gene expression, RNA-seq and epigenetics, proteomics, chemical proteomics, high-throughput RNA biology, single-cell methods and metabolomics.

Undertake a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.

Understand methods of analysis that allow you to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

Evaluate and use DNA and protein sequences and structure. The emphasis will be on the use of computational tools to gain information about genes and variants, their function and associations with disease, as well as predicting protein structure. Computational biology databases and tools that aid in the interpretation and understanding of biomedical research results and their placement within the wider context of the field will be explored.

Take an introduction to genetic epidemiology covering the main topics of current interest in the field. An introduction to human genetics will be included, but the main emphasis is on understanding statistical and epidemiological aspects of the study of the genetic basis of human diseases.

Understand the principles of clinical trial design, conduct, analysis and reporting. The emphasis will be on understanding the practical issues that arise through real examples backed up with the relevant theory. It will provide a grounding in the basic specialist knowledge and skills required by a non-statistician working on phase II/III clinical trials.

Gain insight into the way big data is impacting our understanding of human disease and the development of therapies. It will focus on a variety of disorders ranging from rare Mendelian disease to common disorders of complex aetiology. 

Statistical learning is at the core of the modern world. Online advertising, automated vehicles, stock market trading, transport planning all use statistical models to learn from past data and make decisions about the future. Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. It is how we translate data into knowledge.

Focus on the challenges and latest developments in anti-cancer drug development from a pharmacological point of view. The aim is to provide a good understanding of the processes and difficulties in successfully translating anti-cancer research into clinical practice to improve patient outcomes.

This module will give you an opportunity to analyse large datasets in an independent manner in order to answer a research question of their design. This will cover analysis of a range of data types and analytical techniques, for example germline DNA or RNA sequencing.