Health Data Science (HDS)

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 foundations of statistical and machine learning approaches used to draw inferences from health data 

2. Understand the range of study designs used in health data science 

3. Understand the specific data analysis methods used in health data science 

4. Manage and manipulate large 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.