COURSE CURRICULUM

The MSc in Health Data Science is structured to combine rigorous theoretical training with practical, hands-on experience in analysing health-related data. In the first year, students must complete 90 credits that span core and optional modules designed to develop a strong foundation in statistical, computational, and biomedical methods. Core modules include the Health Data Science Research Project, which carries 30 credits and provides students with a substantial, applied research experience in a real-world context. Other required modules include Statistical Computing for Biomedical Data, Statistics for Health Data Science, Clinical Research Design, Advanced Statistical Computing for Biomedical Data, Statistical Modelling for Health Data Science, and Predictive Modelling and Statistical Learning, ensuring students develop expertise in data analysis, modelling, and computational techniques applied to healthcare datasets. Optional modules allow students to tailor their learning and explore complementary areas such as Applied Statistics, Regression Models, Bayesian Modelling, Linear Algebra, Numerical Analysis, Machine Learning, Bioinformatics, Databases, and Data Visualisation. Economics and health evaluation are also offered through modules like Economics of Health and Health Care and Economic Evaluation in Health Care. This combination of required and optional modules ensures graduates acquire a broad and flexible skillset suitable for diverse career paths in healthcare, biopharmaceuticals, and medical technology. Students learn to apply statistical and computational methods to interpret complex datasets, develop predictive models, and conduct evidence-based research that can inform clinical decision-making and policy. The program emphasizes problem-solving, critical thinking, and the ethical and regulatory considerations of biomedical research. By the end of the year, graduates are prepared to enter the workforce as highly skilled health data scientists or to pursue further research, equipped with the quantitative, analytical, and practical skills demanded in this rapidly growing and high-demand field.