modules

 Statistical modelling lies at the heart of modern data analysis and is a vital part of data science, particularly when decision making is involved. Simple statistical models include linear regression familiar from most foundation courses in statistics. This module places linear regression into the very broad framework of Bayesian statistical data modelling, which has become one of the most popular approaches to data analysis. Bayesian inference will be introduced as a unifying modelling framework, and the module will introduce modelling concepts such as Generalized Linear Models, Generalized Additive Models, Hierarchical Models, Multi-Level Models, Discrete Mixture Models, Models for Flawed Data and predictive model validation. These will provide you with a toolbox and the ability to analyse any real world data set, including binary data, count data, contingency tables, data with temporal and spatial structure as well as data that are missing or partially missing. We will use the statistical software R as the main platform to fit this wide range of models, and will use it in practical sessions so that, as well as a sound theoretical basis, you will develop an understanding of how to apply techniques discussed in the module in practical data analysis. Pre-requisite Modules: MTH2006 or equivalent (knowledge of linear regression) and MTH3041 or equivalent (e.g. self-learning of bitesize pre-recorded essential material from MTH3041 )  

_{Please note that all modules are subject to change, please get in touch if you have any questions about this module.}