Summary of content

This module will cover advanced methods and techniques of Data Science. Students will learn how to extract useful information from large datasets using a variety of analytical techniques. Focus will be given to approaches such as Bayesian statistical analysis, specifically prior and posterior distribution construction, decision theory and model selection. Students will also be introduced to Markov chain Monte Carlo methods and how they can be used to implement Bayesian models. Ultimately, students will be able to utilise Bayesian statistical techniques to build complex models of data. The percentage of Work Based Assessment for this module is 10%.

Module structure

Assessment methods