Start Date between Sep 25 - July 27

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 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.

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 train participants to 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. In addition students will be introduced to multivariable analysis of data (e.g., Principal Component Analysis and K-means clustering). It will demonstrate how these techniques can be applied to gene expression data, to reduce the dimensionality of the data, reveal clinically relevant clusters, perform relevant statistical tests to identify associated variables and interpret the results.

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.