This module will build on the statistical machine learning module, with the main aim to provide hands on experience with real data across a variety of domains. The module will provide an understanding of the basic blocks of data analysis using statistical and machine learning tools. This will cover various data management approaches, such as quality control, tools to deal with missing data, and to visualize data. It will also introduce common techniques to evaluate model performance and to carry out model selection using Frequentist and Bayesian approaches.
Learning Outcomes
By the end of the module students should be able to:
Demonstrate an understanding of complexity of health related data and their management
Demonstrate an in-depth understanding and ability to perform data analysis using appropriate ML tools
Demonstrate good understanding of data analysis process and specific stages to modelling data
Design appropriate measures to integrate various data types accounting for the complexity of information available
Demonstrate self direction and originality in tackling and solving problems
Assessment
Assessment Methods & Exceptions
Assessment:
100% Written Assignment submitted online, based on an analysis. The submission will consist of code and evaluation of the code.
