Students need to have an understanding of regression modelling (particularly logistic models), maximum likelihood estimation, hypothesis testing and confidence intervals; basic familiarity with R, including the ability to read in data, perform basic data manipulations (e.g. reshaping, recoding missing values) and fit regression models; and familiarity with key epidemiological concepts (including the main study designs and issues around exposure and outcome measurement). 

These prerequisites would be fulfilled, for example, by the following modules: Statistics for Health Data Science, Programming (R Sessions), and Concepts and Methods in Epidemiology. Other modules or courses would also be considered if they cover the required prerequisite content listed above.