About the programme

This module offers a practical introduction to statistical modelling for health and biomedical research, focusing on how statistical models can be used to answer real clinical and scientific questions, covering core ideas in statistical inference, causal inference, and regression modelling. You’ll also work past challenges with real datasets, including different outcome types, interaction effects, missing data, and clustered or multilevel structures. By the end, you’ll be able to choose appropriate methods for different questions and apply them to turn real-world data into clear, defensible findings.

Through hands-on teaching using R and Stata, you will develop the skills to build, interpret, and communicate statistical models. Teaching combines lectures, workshops, and coding labs, with applied analytical assessments. You will learn to think critically about assumptions and study design, and to present results clearly to both technical and non-technical audiences. The skills and concepts developed are directly relevant to careers where robust, transparent analysis underpins healthcare and research decisions.

This module introduces the principles and practice of health informatics and predictive analytics, exploring how electronic health data can be transformed into actionable insight to support clinical research, policy, and practice; ultimately improving the delivery of care. You will examine how diverse data sources — including electronic health records, clinical trials, registries, genomic data, and sensory data — are collected, linked, and analysed responsibly. Alongside core concepts in data acquisition, quality, integration, governance and presentation, the module provides an applied introduction to predictive modelling, showing how models can be used to estimate risk, predict outcomes, and inform decision-making in healthcare.

Teaching combines lectures with hands-on coding workshops and applied exercises using R and Python. You will develop skills in health data acquisition, cleaning, visualisation, and modelling, learning how to build, evaluate, and interpret predictive models using robust validation and performance assessment. Mathematical ideas underpinning modern AI and machine learning methodsare introduced in an accessible, applied way. The skills developed are directly relevant to careers in health data science and broader informatics, providing a strong foundation for advanced study in statistical modelling and AI for health.

Health data often track people over time and across settings, creating dependence between observations that standard models don’t handle well. This module introduces multilevel, longitudinal, and survival modelling as essential tools for analysing data with repeated measurements, hierarchical structures, and time-to-event outcomes. You will explore how these models handle non-independence due to shared characteristics or environments, repeated follow-up periods, and multimodal data, providing more reliable answers to important health research questions. By working with real examples, you will see how advanced modelling frameworks reveal patterns and relationships that simpler approaches cannot capture.

You will learn to specify, fit, and interpret multilevel and mixed models, survival models such as Cox regression, and joint models linking longitudinal and time-to-event data. Teaching combines lectures with hands-on Stata practicals, allowing you to apply methods directly to real datasets and interpret model output with confidence. The skills developed are highly relevant for careers in health research, epidemiology, clinical trials, and data-driven roles where analysing complex longitudinal data is central to evidence-based decision-making.

This module introduces latent variable and structural equation modelling as powerful extensions of regression, enabling you to analyse complex systems where key concepts cannot be observed directly. You will explore how latent constructs — such as depression, ability, or attitudes — can be measured and modelled, and how multiple variables, pathways, and indirect effects can be analysed simultaneously. The module covers approaches such as factor analysis, latent class models, mediation models, and emerging machine-learning methods, and shows how theory-driven questions can be rigorously tested using rich, real-world data while accounting for measurement error and missingness.

Teaching combines lectures with hands-on computer labs using Stata with machine learning practicals using Jupyter Notebooks. You will learn to specify, fit, and evaluate complex models, interpret results, and assess model fit and suitability for different research questions. These advanced analytical skills are highly valued across health and social research, psychometrics, econometrics, education, and industry roles where understanding underlying processes is essential for robust decision-making.

This module offers a hands-on introduction to the design, conduct, and analysis of clinical trials, with a particular focus on mental health research. You will explore how trials are designed to minimise bias, compare interventions, and generate reliable evidence, while engaging with real-world challenges such as blinding, complex interventions, outcome selection, and adverse event reporting. The module also introduces contemporary developments in clinical trial methodology, highlighting why this is a dynamic and influential area of health research.

Teaching combines lectures with practical sessions and applied exercises, using real trial scenarios and datasets. You will develop skills in trial design, protocol development, data management, and statistical analysis using Stata or R, alongside critical appraisal and scientific reporting. By the end of the module, you will be able to interpret and analyse clinical trial data with confidence and understand the day-to-day role of a clinical trial statistician — skills that are highly relevant for careers in healthcare research, industry, policy, and further academic study.

This module introduces the core ideas and methods of machine learning, with a clear focus on applications in health and biomedical research. You will explore what machine learning is — and what it is not — while gaining insight into how algorithms learn from data to predict outcomes and reveal patterns. The module covers a range of supervised and unsupervised approaches, from tree-based models and kernel methods to neural networks and ensemble techniques, all motivated by real biomedical problems. Along the way, you will learn how to think critically about model choice, interpretability, robustness, and appropriate evaluation in challenging health data settings.

Teaching combines lectures with hands-on Python practicals, where you will build complete machine learning pipelines using real datasets. You will develop skills in data preprocessing, feature selection, model optimisation, and performance assessment using scikit-learn. These practical, transferable skills are highly relevant for careers in health data scie

This module explores how Natural Language Processing (NLP) can be used to unlock the rich but largely unstructured information held in biomedical and clinical text. You will examine why language data are complex, when NLP methods are appropriate, and how text analytics can transform the secondary use of electronic health records for research, decision support, and healthcare innovation. The module introduces a range of NLP approaches, from rule-based methods to machine learning and modern large language model techniques.

Teaching combines lectures, practical coding sessions, and applied project work. You will gain hands-on experience using NLP software to build, evaluate, and interpret text-based models, while learning how to assess model performance and reflect critically on methodological choices. The module develops skills in independent study, research appraisal, and communication, and is directly relevant to careers in health data science, informatics, AI, and clinical analytics.

This module focuses on one of the central goals of health and social research: drawing credible causal conclusions about the effects of interventions, modifiable risk factors, and policies. You will explore how and when causal questions can be answered using real-world data, and why naïve analyses can lead to misleading conclusions. The module introduces modern, practical approaches to causal inference, showing how carefully designed analyses can strengthen evidence from both experimental and observational studies. Throughout, methods are motivated using real examples from health research and policy evaluation, highlighting their relevance for decision-making in clinical, public health, and policy settings.

Teaching combines pre-recorded lectures with interactive live sessions and hands-on Stata practicals. You will learn to define causal questions, select appropriate study designs, and apply methods such as propensity score matching, instrumental variables, mediation analysis, and quasi-experimental designs. Assessment is based on applied coursework using real data. The skills developed are directly relevant to careers in health research, policy analysis, epidemiology, and data science, where robust causal evidence underpins effective decisions.

This module introduces the core concepts and techniques that underpin artificial intelligence and explores how they are transforming healthcare. Using motivating real-world examples, you will examine how AI can be applied to problems ranging from analysing patient health records to supporting clinical decision-making and developing intelligent digital health tools. The module provides a broad, conceptually grounded overview of AI, covering intelligent agents, problem-solving and planning, modelling uncertainty, and modern learning approaches, while emphasising how these ideas translate into practical healthcare applications.

Teaching combines lectures with hands-on practical sessions, where you will implement and experiment with AI methods using Python and real healthcare data. You will learn to design, assess, and validate AI approaches, and to critically evaluate their strengths, limitations, and appropriateness for medical settings. Through independent study, applied exercises, and engagement with current research, you will develop both technical and analytical skills. The module equips you with a strong foundation for careers in health data science, AI, and digital health, as well as for further study in advanced machine learning and artificial intelligence.

This module introduces the analysis and interpretation of genomic data, addressing one of the central challenges in modern genomic medicine: turning large-scale sequencing data into clinically and scientifically meaningful insight. Focusing on next-generation sequencing (NGS), you will explore how genomic data are generated, assessed for quality, and analysed to identify genetic variation. Using real examples from genomic research and clinical initiatives such as the 100,000 Genomes Project, the module highlights how bioinformatics underpins diagnosis, discovery, and personalised medicine.

Teaching combines lectures with practical, hands-on sessions, where you will develop skills in sequence alignment, variant calling and annotation, quality control, and filtering strategies to identify pathogenic mutations. You will learn to interrogate key genomic and biological databases, integrate genomic findings with clinical information, and apply appropriate statistical and computational methods. Emphasis is placed on professional best practice, interpretation, and reporting. The skills gained are directly relevant to careers in genomic medicine, biomedical research, bioinformatics, and data-driven healthcare.

This module examines the ways in which wearable and mobile sensing technologies are reshaping contemporary healthcare. It focuses on how these technologies enable continuous, real-time monitoring of health and behaviour beyond traditional clinical environments. Students will explore how data generated by wearable devices and smartphones can support early disease detection, personalised interventions, and remote disease management. The module further considers how sensing technologies underpin smart, adaptive healthcare systems and critically evaluates the associated technical, ethical, and human-centred challenges involved in their deployment within real-world healthcare settings.

Teaching combines lectures, hands-on practicals, and engagement with current research. You will develop skills in remote data collection, signal processing, and machine learning for analysing physiological and behavioural data, alongside experience with end-to-end data platforms such as Open-Source RADAR-base. You will develop a comprehensive understanding of system design and deployment, including associated ethical considerations, data privacy requirements, and current standards. These competencies will equip you with skills directly relevant to careers in digital health, health data science, and remote patient monitoring.

The dissertation project gives you the opportunity to design and carry out an independent piece of research aligned with your interests and career ambitions. Working closely with academic supervisors and, where possible, linked to active research groups, you will explore real-world problems using modern statistical, machine learning, and health informatics approaches with applications to prediction modelling, NLP, clinical trials, omics, e-health, epidemiology, neuroscience and multimodal data.

Delivered with structured supervision, the project develops your ability to formulate research questions, work with primary or secondary data, and apply advanced methods critically and creatively. You will build skills in research design, analysis, interpretation, and scientific communication, culminating in the writing of a research report. The module provides strong preparation for careers in data-driven healthcare, research, industry, and further doctoral study.

The Skills & Futures Series supports your development throughout the MSc, helping you build confidence, clarity, and a strong professional identity. Through a combination of structured skills workshops and forward-looking sessions on innovation and careers, the module creates space to reflect on your learning, explore emerging directions in health data science, and connect your studies to real-world practice and future aspirations.

Teaching is delivered through interactive workshops, seminars, and guest sessions; the futures series workshops will explore innovation in health informatics, data science and statistical modelling. You will develop transferable skills in academic practice, research capability, personal effectiveness, and career readiness, culminating in a professional ePortfolio that showcases your growth and achievements. The module is designed to support a wide range of career pathways, equipping you with the skills, evidence, and confidence needed for employment, further study, or research roles in health informatics and related fields.