Gain cutting edge skills in the field of health-related data; a rapidly growing area in which the UK excels with access to the largest biobanks, genomic and health services resources.
You will be taught by world-leading academics in genomics, epidemiology and health services research from mathematics, computer science and biomedical backgrounds
Flexible, interdisciplinary course designed for both applicants with quantitative skills interested in health, and students from a health background interested in data science
Opportunities for real-world research project opportunities with the NHS, pharmaceutical and health data companies
This course offers on employable, transferrable skills and focuses on Python – a coding language used across the technology space
Top 10 in the UK for our world-leading and internationally excellent Clinical Medicine research
Based on 4* + 3* research in REF 2021
Major capital investment in new buildings and state-of-the-art facilities
Become a member of two data science organisations – HDRUK and Exeter’s Institute of Data and Artificial Intelligence
Entry requirements
You will have, or be predicted, at least a 2:2 degree in a strongly numerate subject (e.g. computer science, mathematics, physics), or a health/life sciences degree – prior coding skills are not required. Alternatively you will have demonstrably strong skills in maths, computing or engineering, but not necessarily a degree.
We will require a personal statement detailing your reasons for seeking to study Health Data Science. If you come from a health / life sciences background, this should demonstrate an ability, interest, or understanding of what this highly technical discipline involves. The first term of the course has two separate tracks to adapt to your prior knowledge of coding and data science.
Modern medical science is becoming increasingly driven by interdisciplinary teams making discoveries from analysing large datasets. The University of Exeter is leading the way with world-class research, data science-driven environments in genomics, diabetes, neuroscience and health services.
This course reflects that interdisciplinary environment, and the first term is flexible to your prior experience - with either an introduction to scientific computing or machine learning – alongside health statistics and research design. The second term focuses on two areas of world-leading research at Exeter: health services research and personalised medicine.
Our research projects are unique - you’ll have the opportunity to carry out a research project, working real-world health data with external partners including the NHS and companies involved in health data.
Please note that the module information displayed here is subject to change.
Awards
The programme is divided into units of study called ‘modules’ which are assigned a number of ‘credits’. To gain a Masters qualification, you will need to complete 180 credits at level seven. The credit rating of a module is proportional to the total workload, with one credit being nominally equivalent to 10 hours of work, a 15 credit module being equivalent to 150 hours of work and a full Masters degree being equivalent to approximately 1,800 hours of work.
It is also possible to exit with a PGCert after completing 60 credits of taught modules. The list of modules below shows which are compulsory.
Please note that the module information displayed here is subject to change.
165 credits of compulsory modules, 15 credits of optional modules.
Compulsory modules
Code
Module
Credits
Compulsory 1
Research Project - Data
60
Fundamentals of Research Design
15
Making a Difference with Health Data
30
Stratified Medicine
30
Coding in Python for Health and Life Sciences
15
Computational Skills for Health and Life Sciences
15
Statistics for Health and Life Sciences
15
HPDM042: Research Project - Data
In this module you will apply and extend your existing knowledge and skills by undertaking an independent research project aligned with your degree programme (MSc Neuroscience, Health Data Science or Genomic Medicine). Projects are selected from a diverse portfolio designed to reflect a wide range of scientific interests and programme specialities. Depending on your programme, projects may involve laboratory-based research, a systematic review, or in silico approaches such as data analysis, computer modelling or bioinformatics. Projects are undertaken within Exeter’s leading research groups and may include collaboration with partners including the National Health Service, pharmaceutical companies and health data organisations.
In this compulsory introductory module, you will critically examine and apply the core concepts and perspectives at the heart of applied health and care research, and the main methods and elements of study design that underpin this multidisciplinary and highly applied area of social science. It seeks balanced coverage of: quantitative, qualitative and mixed methods; a wide range of disciplinary perspectives; appreciation of the full range of health service and health policy goals (e.g. effectiveness, safety, cost-effectiveness, feasibility, acceptability, accessibility, equity, and understanding the perspectives of different stakeholders); and is based around the key study design choices which all applied social science studies face. It will also cover fundamental considerations in all high quality applied health research, such as: the role of theory; patient and public involvement; data processing and management, and research ethics.
Health services are complex organisations that must coordinate their workforce and patient pathways to deliver high‑quality care both effectively and efficiently. In this module, you will be introduced to Operational Research (OR) in health, the discipline of using quantitative models to support decision‑making in complex health service environments. You will learn how OR methods related to time series forecasting, machine learning, optimisation, and discrete‑event simulation can be used to support the planning, evaluation, and reconfiguration of health services.
The module is code‑intensive. You will work extensively in Python 3, using libraries such as numpy, pandas, keras, tensorflow and specialist libraries for computer simulation. Through practical exercises and case studies drawn from real health service settings, you will develop hands‑on skills in building, analysing, and interpreting models for service delivery.
You will explore how to improve the quality and efficiency of health service logistics using forecasting, simulation, and optimisation methods. Typical questions addressed include:
Genetic and phenotypic health data are becoming available in millions of people from around the world, through health care systems (including the NHS) and large-scale biobanks (e.g. UK Biobank). These data are being used to predict disease risk and health outcomes, and to separate (stratify) groups of individuals based on these features.
In this module you will learn about the sources of large-scale electronic healthcare data (including diagnoses, blood test results, and medication prescribing) and genomic data and their limitations. You will learn how these data are stored and used in disease prediction and classification, and the computational and statistical methodologies used to stratify individuals into groups at higher risk of disease, disease sub-types, and variable responses to treatment. You will use Python, statistical programming languages (R), database management systems (MySQL), and Linux command line tools.
You will also be taught fundamental concepts in human genetics that underpin common analyses of genetic data and learn how to interpret findings from these analyses. You will gain insight into how these findings can be used in drug development.
Theoretical sessions will be followed by practical workshops and assessments.
HPDM171: Coding in Python for Health and Life Sciences
Modern health research is becoming increasingly focused on the analysis of large, complex datasets. To extract meaningful information from such datasets, health data scientists often use computer programming languages to create bespoke analysis pipelines. Python is the most popular programming language for this task, making it a widely transferrable and employable skill.
This module assumes no prior knowledge of Python or any other computer coding language. We will be teaching Python from the ground up, starting with basic structures and objects available within Python, then developing more complex routines. When the fundamentals are established, you will learn how to manage and visualise data in Python. At the end of the course, you will learn how to perform machine learning tasks in Python, and come out of the module with general transferable computing and code-writing skills that will help you learn new languages quicker.
The overall aim of this module is to introduce students from a non-computing background to computer programming in Python, a common language for health data science. You will learn practical coding skills focused on developing the necessary skills to analyse data.
HPDM172: Computational Skills for Health and Life Sciences
Health data science is a complex field requiring a wide range of computing skills. For example, increasingly, many health datasets are hosted on cloud computing resources and requiring specialist software and multidisciplinary teams to access them. This module complements the Introduction to Python for Health Data Scientists module, with the aim of broadening the scope of the tools available to you as a data scientist. By the end of the module, you will have learned the following skills:
Cloud computing using the Openstack system
Computational thinking, including how to design an algorithm and planning programming using pseudocode
Navigating the Linux command line
Querying relational databases using SQL
Ethical and effective use of generative AI
The benefits of and how to use Git and GitHub for collaborative coding and version control
This module requires no previous knowledge of any of the required skills, although general computer skills will be beneficial.
The aim of this module is to provide a solid foundation in basic computational thinking and provide essential skills in widely used operating systems and computer software.
This module provides a broad introduction to statistical analysis for health and life science applications. The module starts by considering the different stages of a statistical investigation and emphasising the importance of problem formulation. The module highlights the benefits of exploratory data analysis based on descriptive statistics and graphs. Key concepts in probability theory and the role of statistical distributions in modelling health data will be covered. The core part of the module provides a foundation in regression modelling to include simple linear regression, logistic regression, survival analysis and models that account for complex temporal and hierarchical data structures. Embedded through the module is a strong emphasis on the critical evaluation of statistical methodology and interpretation of analysis results in the context of the specific health application. Throughout this module, you will gain practical experience of statistical computing using the R software environment and exposure to case studies based on real-world health data.
The University of Exeter offers a wide range of scholarships to support your education, with £7 million available for international students applying to study with us in the 2026/27 academic year, including our prestigious Exeter Excellence Scholarships. We also provide awards for sport, music and other achievements, as well as regional and partner scholarships with organisations such as Chevening, The Beacon Trust and the British Council. For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
University of Exeter Alumni Scholarship
We are pleased to offer the University of Exeter Alumni Scholarship, a scholarship for University of Exeter alumni beginning a standalone postgraduate programme in 2026/27 with us a scholarship worth 20% of the cost of your first year tuition fees.
MSc Health Data Science is a comprehensive course, covering everything from the fundamentals of object-oriented programming to the very edge-cutting translational applications of data science in medical research. Data is the language of modern medicine, and through this MSc, I'm becoming fluent in this language, ready to contribute to a future where data-driven decisions lead to healthier lives.
The course covers a lot of breadth and depth – I feel that the programme has prepared me for the future of healthcare; and equipped me to contribute to shaping it.
The MSc in Health Data Science stands out as an exceptional program, bridging the foundational pillars of programming and statistics with cutting-edge applications in LLMs and AI. It has equipped me to contribute to the rapidly developing data-driven research that underlies modern medicine, and I couldn’t recommend it more.
MSc Health Data Science (Intercalating Medicine student)
UK student
Teaching and research
Our purpose is to deliver transformative education that will help tackle health challenges of national and global importance. This programme is a genuinely interdisciplinary experience – the programme is delivered by experts from mathematics, computing, biomedical science, the NHS and industry.
Research
This course will be delivered by research-active academics from biomedical science, computer science and mathematics backgrounds. Our external partners, including the NHS, pharmaceutical and data companies, will also contribute to the course in the form of guest lectures and seminars, and provide aa substantial proportion of the research projects.
Students can participate in impactful research via this programme. Some students have published papers – an example here. Others have produced software that is being used in NHS services.
Teaching
Using a mix of learning formats, our modules run over a ten- to twelve-week period and are delivered primarily face-to-face with guided independent study. All teaching is delivered by research-active academics in world-leading research groups.
You will be allocated an academic tutor who will remain with you throughout the programme. Academic tutors are able to provide guidance and feedback on assessment performance, guidance in generic academic skills and pastoral support.
Learning
For our computing modules, for each hour of lecture-style delivery, there will be two hours of computer workshop time, where you will gain practical experience coding, with one of our expert health data scientists to support you. This focus means you will be spending most of your time developing your skills, rather than passively absorbing content. The course is flexible and adaptive to your prior ability, so you will be learning content at the right level for you.
Facilities
This programme is based at the St Luke’s campus in Exeter, just a 15 minute walk from the city centre and just over a mile away from the Streatham Campus. The campus is close to the Royal Devon and Exeter Hospital and RILD building, which is home to the NHS funded Exeter Health Library. Students have studied at St Luke’s campus for over 150 years and the campus enjoys a vibrant atmosphere set around the lawns of the quadrangle.
Health data science is an interdisciplinary field that brings together scientists from different backgrounds to answer the most difficult questions in modern healthcare. Our programme has two directors, Dr. Harry Green, a mathematician, and Prof. Mike Weedon, a human geneticist, reflecting this interdisciplinary environment.
Harry is a lecturer in health data science. Harry comes from a background in pure mathematics, and moved towards medicine with a PhD in mathematical modelling of cardiac biophysics. He joined the medical school in 2017 and since then has been working as a data scientist focused on using genetics to further our understanding of chronic diseases: what causes them, and how to predict them. Harry has been teaching at universities since 2012, and has experience guiding students from a range of backgrounds, having taught on Engineering, Mathematics and Medical programmes.
Mike Weedon is a professor of bioinformatics and human genetics. He has been at the University since 2001. Mike has published over 300 papers on gene discovery and casual inference across a range of disease phenotypes.
Robin is a Senior Research Fellow in the Genetics of Complex Traits Group. His research focusses on understanding the genetic architecture of human traits using large population studies such as the UK Biobank and All of Us cohorts. His current work looks at developing statistical methods and analysis frameworks and pipelines for understanding the effects of rare genetic variants using large-scale whole genome sequence data.
This course is suitable for anyone who is interested in pursuing a career or further study in health data science. We welcome students from computer science, maths, physics or engineering but who do not necessarily have any experience in biology or health – and students from health and life sciences that are keen and interested to expand their skillset into health-related data. The course is flexible and the first term will adapt to your prior experience, so you will have all the necessary support to transition into this highly technical discipline from entry-level knowledge.
Employer-valued skills this course develops
The majority of the programme uses the Python programming language, one of the most desired computer programming languages by employers. The computing skills you develop will equip for a wide range of careers in healthcare and beyond. In a world increasingly driven by AI and big data analysis, experience with coding and machine learning will only become more and more valued by employers across the world.
Work-based learning
The majority of students on do their project with an external provider – providing a chance to work in the real world with real health data. Project providers include those in the NHS, pharmaceutical industry and health data companies. Students have a wide choice of projects because we have more projects than students, a result of the outstanding reputation of the programme and the students. Students often continue working with their project providers after graduation.
Career paths (graduate destinations)
Exeter’s Masters in Health Data Science provides students with excellent careers opportunities. Students from the first two cohorts have obtained positions with employers in the NHS, including NHS Digital, the Office of National Statistics, Data science and AI companies.
Careers support
We will support your career progression by introducing you to the full range of careers open to you, with seminars and visits to different environments in industry and in NHS Trusts. By providing funds for attendance at HDRUK workshops, and, through our Institute of Data Science and Artificial Intelligence, Alan Turing Institute meetings and conferences. The role of the personal tutor will include discussion of future career paths.
All University of Exeter students have access to Career Zone, which gives access to a wealth of business contacts, support and training as well as the opportunity to meet potential employers at our regular Careers Fairs.
"After completing a bachelor’s degree in Mathematics and Sport Science, I knew I wanted to use statistics and machine learning in a health setting. This masters degree allowed me to grow and develop the skills required in this growing field. For myself, I am now completing a PhD in type 1 diabetes prediction modelling utilising genetics"
The majority of students are based at our Streatham Campus in Exeter. The campus is one of the most beautiful in the country and offers a unique environment in which to study, with lakes, parkland, woodland and gardens as well as modern and historical buildings.
Located on the eastern edge of the city centre, St Luke's is home to Sport and Health Sciences, the Medical School, the Academy of Nursing, the Department of Allied Health Professions, and PGCE students.
Our Penryn Campus is located near Falmouth in Cornwall. It is consistently ranked highly for satisfaction: students report having a highly personal experience that is intellectually stretching but great fun, providing plenty of opportunities to quickly get to know everyone.
