Statistics for Health Research is a dynamic and applied module designed to equip you with essential statistical and artificial intelligence skills tailored for real-world health innovation. This 12-week module offers an in-depth introduction to the key principles of statistical inference and artificial intelligence, variability, and sampling — all critical to interpreting and communicating health data effectively. Through a combination of weekly lectures, hands-on computer practicals, and expert guest speaker seminars, you will develop confidence in using the R software package to analyse and visualise complex datasets.
Designed with research-connected teaching and active learning at its core, the module will help you to build digital fluency, critical thinking, and effective communication. You will engage with authentic assessments, including a poster presentation visualising a real-world health dataset (30% of final grade) and a written data analysis report that interprets and communicates statistical findings (70%). The syllabus covers a broad range of topics including study design, regression analysis, longitudinal and survival data, Bayesian methods, meta-analysis, causal inference, and machine learning— all highly relevant for a career in health research, public health, or data-driven healthcare policy.
Whether you are aiming to work in academic research, public health, clinical trials, or health informatics, this module provides the statistical and artificial intelligence foundation and practical skills you will need to succeed.