Start Date between Sep 25 - July 27

The module provides 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.

The module provides 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.

This module is designed to give those with little or no programming experience 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.

This module is designed to give those with little or no programming experience 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.

The module aims to provide 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. Data analysis methods will be discussed but there will not be a strong emphasis on statistical methods that are covered in other modules.

The module aims to provide 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. Data analysis methods will be discussed but there will not be a strong emphasis on statistical methods that are covered in other modules.

This module will provide 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.

The module will provide an outline to 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.

This module will provide insight into the way big data are 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. The content will be delivered by focusing on exemplar cases and will take the students on the journey from defining the medical problem and understanding the disease mechanisms, all the way to the development of treatments and therapies. Topics reviewed will include gene replacement therapy, pharmacogenetics, phenotype-genotype correlations, patient datasets, whole genome sequencing, antisense oligonucleotide (AON)-based therapies and CRISPR-mediated gene editing. At the end of this course, students should have a thorough understanding of the exciting way big data is influencing medical research and practice today, and what opportunities it offers for the future, but they will also have an appreciation of the problems and limitations that it brings.

This module will provide insight into the way big data are 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. The content will be delivered by focusing on exemplar cases and will take the students on the journey from defining the medical problem and understanding the disease mechanisms, all the way to the development of treatments and therapies. Topics reviewed will include gene replacement therapy, pharmacogenetics, phenotype-genotype correlations, patient datasets, whole genome sequencing, antisense oligonucleotide (AON)-based therapies and CRISPR-mediated gene editing. At the end of this course, students should have a thorough understanding of the exciting way big data is influencing medical research and practice today, and what opportunities it offers for the future, but they will also have an appreciation of the problems and limitations that it brings.

This module is focussed 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.