Start Date between March 25 - July 26

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.