Please note: The modules listed on the website for this programme are regularly reviewed to ensure they are up-to-date and informed by the latest research and teaching methods.
- Foundations of Computing Practices in Health Data Science (20 credits)
This module covers the fundamentals of health data management, extraction, and manipulation using Python programming. It also introduces students to data visualisation techniques for health data analytics.
- Essentials of Mathematics and Statistics (20 credits)
This module provides an introduction to essential quantitative theory in health data science. It covers concepts through a series of core problems, which will be explored in more detail in later modules. The quantitative topics include Probability Theory, Descriptive Statistics, Hypothesis Testing, and an introduction to Statistical Modeling using the R programming language, including linear models and estimation.
- Data Analytics & Statistical Machine Learning (20 credits)
The aim of this module is to provide a comprehensive understanding of the current advancements in data integration, mining, and analysis, with a focus on applications in health data science and biomedicine. The topics covered include various aspects of data, such as data types, data modelling, data management, integration techniques, as well as supervised and unsupervised machine learning models and validation approaches.
- Health Data Fundamentals (20 credits)
The module introduces key concepts in various multi-modal health data types and modalities and provides an overview of how health data science can revolutionise healthcare data use. It covers data governance, ethical implications, patient and public involvement, and informed consent. Additionally, it introduces the fundamentals of various -omics and genetics fields and their role in revealing disease pathobiology and implications in personalised medicine.
- Epidemiology and Health Informatics (20 credits)
This module introduces key concepts in epidemiology and health information at both the population and individual levels. It covers topics such as descriptive epidemiology, measures and comparisons of disease occurrence (incidence, prevalence), and various study designs in epidemiology, including ecological studies, cross-sectional studies, case-control studies, cohort studies, and randomised controlled trials. The course also provides an introduction to fundamental epidemiological concepts used to identify and quantify disease and associated hazards.
- Integrative Multimodal Data Analytics (20 credits)
This module builds upon previous modules and covers advanced topics in health data science. It introduces image analysis, electronic health records data, longitudinal modelling, and integration with multi-omics datasets. The module also explores advanced modelling methods, including deep neural networks and omics fusion strategies.
- Interdisciplinary Health Data Research Project (60 credits)
The dissertation module offers students the opportunity to demonstrate their acquired knowledge and skills from the taught modules. Dissertations must include a computational work in the health data field, and students are encouraged to select their own topics with the guidance of a supervisor. Successful dissertations delve deeply into a health data subject, posing clear research questions, employing suitable methodology, and critically analysing the results.