Masters applications for 2023 entry are now closed.
Applications for September 2024 will open on Monday 25 September. Applications are now open for programmes with a January 2024 start. View our programmes »
| UCAS code |
1234 |
| Duration |
1 year full time
2 or 3 years part time |
| Entry year |
2025 |
| Campus |
St Luke's Campus
|
| Discipline |
Healthcare and Medicine
|
| Contact |
|
| UCAS code |
1234 |
| Duration |
Over 2 years |
| Entry year |
2025 |
| Campus |
St Luke's Campus
|
| Discipline |
Healthcare and Medicine
|
| Contact |
|
Overview
- 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
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
Top 10 in the UK for our world-leading and internationally excellent Clinical Medicine research
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.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course. The required test scores for this course fall under Profile B2. Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Course content
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.
Awards
This MSc course can be studied on a full time basis over one year or over two or three years (part time), which may suit applicants who are already working full time. 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 or a PGDip after completing 120 credits of taught modules. The list of modules below shows which are compulsory.
Contact Days
Contact days for MSc Health Data Science 2024/25
(these timetables are draft and may be subject to change).
Course flow diagram
View diagram that shows the course flow through the MSc Health Data Science programme.
Compulsory modules
The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual review of this programme.
Students will have the opportunity to pursue a Research Project on one of the two broad themes – health services research or stratified medicine.
Students will have the opportunity to pursue certain elements of the programme at a more technical level, subject to demonstration of appropriate skills and knowledge. A simple exercise will be undertaken prior to the programme’s start to determine whether students should be directed to the foundational modules (HPDM171 and HPDM172) or to the more technical modules (HPDM139 and ECMM445).
Full-time MSc: You will take all 180 credits of the modules listed above in one academic year.
Two-year part time MSc: you will take track-specific modules (marked a/b) and one of the 30-credit modules in your first year, and the remaining 120 credits in your second year.
Three-year part-time MSc: you will take track-specific modules (marked a/b) in your first year. You should not take more than one 30-credit module in any one year.
PGCert (1 year): you will take two of HPDM171, HPDM172, or HPDM182 in the first term and one of the 30-credit modules to make up 60 credits required for this award
PGDip (2-year part time): you will take track-specific modules (marked a/b) in your first year plus one 30-credit module, and the remaining 15 credit modules in your second year with another 30-credit module, to make the 120 credits required for this award.
a For all awards, students may select to study modules marked b instead of these modules, subject to demonstration of appropriate skills and knowledge. Modules marked a cannot be taken with modules marked b
| Code | Module |
Credits |
|---|
| HPDM092 |
Fundamentals of Research Design | 15 |
| HPDM182 |
Statistics for Health and Life Sciences | 15 |
| HPDM097 |
Making a Difference with Health Data | 30 |
| HPDM098 |
Stratified Medicine | 30 |
| HPDM099 |
Research Project | 60 |
Optional modules
| Code | Module |
Credits |
|---|
| HPDM171 |
Coding in Python for Health and Life Sciences a | 15 |
| HPDM172 |
Computational Skills for Health and Life Sciences a | 15 |
| HPDM139 |
Coding for Machine Learning and Data Science b | 15 |
| ECMM445 |
Learning from Data b | 15 |
Fees
2025/26 entry
UK fees:
Fees are subject to an annual increment each academic year.
- MSc fees (1 year): £13,200 full-time
- MSc fees (2 years): £6,600
- MSc fees (3 years): £4,400
- PGCert (1 year): £4,400
- PGDip (2 years): £4,400
Standalone module fees: UK £1,250 per 15 credit module
International fees:
- MSc fees (1 year): £30,900 full-time
- MSc fees (2 years): £15,500
- MSc fees (3 years): £10,300
- PGCert (1 year): £10,300
- PGDip (2 years): £10,300
Standalone module fees: International: £2,800 per 15 credit module
Find out more about tuition fees and funding
Scholarships
The University of Exeter has many different scholarships available to support your education, including £5 million in scholarships for international students applying to study with us in the 2025/26 academic year, such as our Exeter Excellence Scholarships*.
For more information on scholarships and other financial support, please visit our scholarships and bursaries page.
*Terms and conditions apply. See online for details.
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.
Read more from Pavel
Pavel
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.
Facilities at St Luke’s campus include:
Programme Directors
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.
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Dr Harry Green
Prof. Michael Weedon
Dr Harry Green
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.
Profile page
Prof. Michael Weedon
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.
Profile page
Careers
Who is this course for?
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.
Studying whilst working
This programme can be taken either over one, two or three years. For applicants who are working full time (or close to full-time), we recommend applying to complete the Masters degree over two or three years rather than one year.
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.
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"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"
Erin
PhD student at University of Exeter
