Aims

Health Data Science is a discipline that combines mathematics, statistics and computing to answer questions in the biomedical sciences by the analysis of data. The Health Data Science theme aims to provide training in biostatistics, epidemiology, machine learning and health informatics, to equip students with the quantitative knowledge and skills for a career in health data science. The course offers a strong academic grounding in current and emerging knowledge and methods, and practical experience of analysing biomedical datasets. 

Learning Outcomes 

By the end of the course, students should be able to: 

1. Understand the statistical foundations of approaches used to draw inferences and make predictions from health datasets. 

2. Understand several study designs commonly used in health data scienc. 

3. Understand a range of statistical and machine learning methods commonly used for analysing health data.

4. Manage and manipulate complex biomedical datasets 

5. Conduct appropriate analyses of health data using a range of quantitative methods. 

 Core modules

Students following the Health Data Science theme must follow the Statistics for HDS core module. 

 Theme module options 

Required 

• Advanced biostatistics for health data science 
• Introduction to machine learning 

Optional

Choose 3 modules from the following list: 

• Bayesian statistics 
• Genetic epidemiology 
• Causal inference 
• Geostatistical modelling 
• Applied machine learning 
• Infectious disease modelling 

Plus one other module, chosen from any theme, from the full list of student-selected modules.