COURSE CURRICULUM

The curriculum consists of core and optional modules, plus a dissertation. Core modules include: Fundamentals of Health Data Science (introducing data handling and programming in R/Python); Statistical Learning (covering regression, classification, and machine learning); Health Data Analysis (focusing on epidemiological data and survival analysis); and Computing for Health Data Science (addressing big data technologies and databases). Optional modules may include topics like Longitudinal Data Analysis, Bayesian Methods, or Clinical Trials Design. The program culminates in a 60-credit MSc Project, where students undertake independent research on a health data science topic, often in collaboration with industry partners or NHS organizations. Teaching methods involve lectures, practical computing sessions, seminars, and group projects. Assessment is through coursework, exams, and the final dissertation. The curriculum is designed to build interdisciplinary skills, integrating statistics, computer science, and health sciences, with an emphasis on applying methods to real health datasets while considering ethical and legal frameworks.