Overview
- A platform to many exciting career opportunities in health data in the UK and abroad. Previous students have moved into new careers in the National Health Service, NHS digital, industry and research, including positions in data science and AI, with the Office of National Statistics, with the Institute of Cancer Research, in Clinical trials, and PhDs in the USA, University College London and Cancer research UK.
- 1 of only 6 new MSc courses in the UK funded by Health Data Research UK (HDRUK). Five students will receive full fees and stipend (EU/UK fees) – deadline 30th April each year.
- A chance to learn and apply your quantitative skills (e.g. computing, maths, statistics) in a health and medical setting, even if you have had no experience in health, medicine or biology. Current students include those from Maths, Engineering, Computer Science backgrounds as well as those from Medicine and Bioscience and some are Professionals in the NHS. But they all share and start the course with data skills – the programme is about improving those existing skills and applying them in a health care and biomedical setting
- A chance to perform research in the real world – we have exciting research project opportunities, including those with placements in the NHS, and health-related industry such as large pharmaceutical companies. Current students have placements in local NHS trusts in the South West, two health data companies, and two pharmaceutical companies.
- A genuinely interdisciplinary experience – the programme is delivered by experts from mathematics, computing, biomedical science, the NHS and industry
- An opportunity to learn about and 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.
We are Top 10 in the UK for our world-leading and internationally excellent research in clinical medicine
Major capital investment in new buildings and state-of-the-art facilities
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 2:2 in a health/life sciences degree AND demonstrate good programming ability in a modern computer language. Alternatively you will have strong skills in maths, computing or engineering, but not necessarily have a degree.
Computer programming ability will be assessed with a short simple test as part of the selection process. We will also use your personal statement and a short interview to determine which students receive full fees and stipend.
A personal statement, detailing your reasons for seeking to undertake this subject, will be required.
Entry requirements for international students
English language requirements
• IELTS: Overall score 6.5. No less than 6.0 in any section.
• TOEFL: Overall score 90 with minimum scores of 21 for writing, 21 for listening, 22 for reading and 23 for speaking.
Please visit our entry requirements section for equivalencies from your country and further information on English language requirements.
Please visit our international equivalency pages to enable you to see if your existing academic qualifications meet our entry requirements.
International students are normally subject to visa regulations which prevent part-time study. It is recommended that international students apply for the level of the final award you intend to complete i.e. PGCert, PGDip or Masters, due to the associated cost and requirements for a Tier 4 student Visa.
Who can apply for this course?
- Graduates with a Computer Science, Physics, Engineering or Maths degree, may not have any prior life sciences, medical or health experience.
- Graduates with Life, Health or Medical Science degrees (or Medicine intercalators) with proven basic computer programming skills.
- Graduates with a strongly numerate degree AND working as analysts in the NHS or other health related organisations.
- People without a first degree, but who can demonstrate evidence of strong quantitative skills and experience, for example if they have been working in the NHS and have good computer programming and maths
Accreditation of prior learning for Masters courses in Healthcare and Medicine
Accreditation of Prior Learning (APL) is a process whereby students, who have already gained relevant skills and knowledge prior to the start of their course, may be granted a partial credit exemption from their programme instead of unnecessarily repeating work. Find out more about APL
Entry requirements for international students
Please visit our entry requirements section for equivalencies from your country and further information on English language requirements.
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Entry requirements for international students
English language requirements
Please visit our entry requirements section for equivalencies from your country and further information on English language requirements.
Course content
Our MSc Health Data Science will help you develop innovative skills needed to unlock knowledge from complex health data, to address some of the biggest health challenges that we face across the globe today. This course combines the expertise of our world-renowned health scientists with experts in mathematics and computer science to develop a better understanding of diseases and to find ways to prevent, treat and cure them.
Why Exeter?
We are one of only six UK institutions chosen to deliver this training for Health Data Research UK which means that we demonstrated scientific excellence, a track record in postgraduate training, innovative approaches to further education and strong institutional commitment. Five students will receive full fees and stipend as part of the Health Data Research UK funding.
Course structure
The programme is also divided into units of study called ‘modules’ which are assigned a number of ‘credits’. 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. Therefore, for applicants who are working full time (or close to full-time), we recommend applying to complete the Masters degree over 2 or 3 years rather than 1 year.
To gain a Masters qualification, you will need to complete 180 credits at level 7.
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
View the draft timetable of contact days for 2022/23 (this timetable is draft and may be subject to change).
The MSc Health Data Science can be taken as a full time or part time programme of study, as appropriate. It is delivered at National Qualification Framework (NQF) level 7.
The programme comprises 180 credits in total: taught modules worth 120 credits in total, and a supervised dissertation worth 60 credits. We have eleven partners from the NHS or industry who will provide and supervise dissertations.
The programme is divided into units of study called modules which are assigned a number of 'credits'. The credit rating of a module is proportional to the total workload, with 1 credit being nominally equivalent to 10 hours of work.
View the Overall Course Structure to get an idea of what your programme may look like depending on which pathway you choose.
View the draft timetable of contact days for 2021/22 for an indication of when modules will run.
Please note: The above documents are for illustration purposes only and may vary in subsequent years.
The last contact day and assessment deadline for the programme will be earlier than the actual end date of your registration with the University, to allow a period of time at the end of your active studies for further support and mitigation, if needed.
Compulsory modules
Fees
2022/23 entry
UK fees:
Fees are subject to an annual increment each academic year.
- MSc fees (1 year): £10,500 full-time
- MSc fees (2 year): £5,250
Standalone Module Fees: UK: £950 per 15 credit module
International fees:
- MSc fees (1 year): £23,000 full-time
- MSc fees (2 year): £11,500
Standalone module fees: International: £2100 per 15 credit module
Find out more about tuition fees and funding
Scholarships
We invest heavily in scholarships for talented prospective Masters students and have over £2.5 million in scholarships available, including our Global Excellence Scholarships* and Green Futures Scholarships* for international fee paying students.
For information on how you can fund your postgraduate degree at the University of Exeter, please visit our dedicated funding page.
*Selected programmes only. Please see the Terms and Conditions for each scheme for further details.
Funding and scholarships
UK government postgraduate loan scheme
Postgraduate loans of up to £10,609 are now available for Masters degrees. Find out more about eligibility and how to apply.
Funding
There are various funding opportunities available including Global excellence scholarships. For more information visit our Masters funding page.
Five fully funded places
Five students will receive full funding of stipend and fees (EU/UK fees). There will be a selection process that focuses on computing and mathematical skills to assess which students are eligible.
Scholarships
The University of Exeter is offering scholarships to the value of over £4 million for students starting with us in September 2021. Details of scholarships, including our Global Excellence scholarships for international fee paying students, can be found on our dedicated funding page.
Pro Vice Chancellor's NHS Postgraduate Scholarship
The College of Medicine and Health is delighted to offer the Pro Vice Chancellor's NHS Postgraduate Scholarship of £5000 to two NHS staff who accept a place to study on one of our Masters programmes. Please check your eligibility before applying.
University of Exeter Class of 2022 Progression Scholarship
We are pleased to offer graduating University of Exeter students completing their degree in Summer 2022 and progressing direct to a standalone taught Masters degree (eg MA; MSc; MRes; MFA) or research degree (eg MPhil/PhD) with us a scholarship towards the cost of their tuition fees. These awards are worth 10% of the first year tuition fee for students enrolling on a postgraduate taught or research programme of study in 2022/23, with the exception of the PGCE programme. Find out more
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Teaching and research
Our purpose is to deliver transformative education that will help tackle health challenges of national and global importance.
This course will be delivered by research-active academics from the College of Medicine and Health and the College of Engineering, Mathematics and Physical Sciences. Our non-academic partners, including the NHS, pharmaceutical and data companies, will also contribute to the course in the form of guest lectures and seminars, and provide at least 50% of the research projects.
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. They are also able to refer you to more specialist support services, both within the College and elsewhere across the University.
This is an example of the kind of code students will need to feel comfortable with prior to the start of the course.
You can also view an interactive version of the sample test (Google account is needed) (kind thanks to Associate Professor Tom Monks).
Professor Tim Frayling
Programme lead for the MSc Health Data Science
Dr Thomas Monks
Associate Professor of Health Data Science
Dr Caroline Wright
Associate professor
Dr Eilis Hannon
Senior Research Fellow in Bioinformatics
Professor Martin Pitt
Lead for Research Projects within MSc Health Data Science
Professor Tim Frayling
Programme lead for the MSc Health Data Science
Professor Frayling is programme lead for the MSc Health Data Science and has been working as a molecular geneticist for more than twenty years, the majority of that time with common human traits and diseases, particularly type 2 diabetes, obesity and related conditions.
He obtained a personal chair as Professor of Human Genetics in 2007 and heads a team of 14 that has become internationally recognized as a world leader in the genetics of common traits and conditions. More information, including publications, is available on the team's website.
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Dr Thomas Monks
Associate Professor of Health Data Science
Tom is an Associate Professor of Health Data Science and leads the module: Making a Difference with Health Data. He holds a joint position between University of Exeter Medical School and the Institute of Data Science and AI. He is a Turing Fellow at the Alan Turing Institute and an Honorary Senior Research Fellow at the Clinical Operational Research Unit, Department of Mathematics, UCL.
Tom is a methodologist with expertise is in applying computer simulation methods, optimisation and machine learning in health service delivery. His work aims to translate Data Science and Operational Research tools improve the quality and safety of health and social care.
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Dr Caroline Wright
Associate professor
Caroline is an Associate Professor in Human Genetics and Genomics. Her main research interests are in the clinical application of genome-wide sequencing technologies for the diagnosis of rare diseases.
Specifically, she is interested in understanding the penetrance of rare disease-causing variants, improving variant filtering and interpretation, modelling the effect of pathogenic missense variants using in silico protein structural analysis, and exploring the policy and ethical issues associated with implementation of genome-wide sequencing in healthcare.
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Dr Eilis Hannon
Senior Research Fellow in Bioinformatics
Eilis is a Senior Research Fellow in Bioinformatics and co-leads the module: Health Statistics for Data Scientists.
Her research focuses on integrating epigenetic, transcriptomic and genetic data to aid the understanding of the molecular aetiology of psychiatric illnesses and neurodegenerative diseases. Specifically, her work aims to identify how genetic risk factors for complex diseases like schizophrenia alters gene regulation in the brain.
Professor Martin Pitt
Lead for Research Projects within MSc Health Data Science
Martin joined the University of Exeter in 1998 following an early academic background in psychology, cognitive science and human-computer interaction.
He leads on Research Projects within the MSc Health Data Science. His research interest is in the use of visualisation tools to improve information accessibility and its communication to key stakeholders in healthcare. He is the Director of PenCHORD (The Peninsula Collaboration for Health Operational Research and Development).
In January 2019 he was elected President of the Association of Professional Healthcare Analysts (AphA) – a dynamic UK based network to support the development of analysts and the promotion of data science more generally within the NHS.
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Careers
You will be equipped to work in health and biomedical interdisciplinary teams and to tackle the exciting opportunities and challenges in health data science across a wide range of careers. We will focus on two broad areas in which Exeter excels – health services research and modelling, and stratified medicine, including genomics.
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.
"I was motivated to take the MSc Health Data Science after completing my Biosciences degree, where my dissertation was on AI for early cancer detection using blood levels. Data Science was a completely new area for me but I knew that this was what I wanted to do in the future and I decided to apply after a chat with Tim Frayling."
The course helped upskill my data science prowess much further. The course was application-focused, coursework was structured to be scenario based and it challenged our methodology and problem solving alongside our coding skills. This has been particularly useful at Agilisys, where we help not just the healthcare sector, but also policing and regional government to make data driven decisions.
I’ve found that the skills I learned were directly applicable to a lot of the everyday problems the public sector has. An example of this was crime forecasting for the police force, where the solution was very similar to the emergency attendance forecasting we did in the operational research module.
The highlight of the course for me was the final research project. Each project is unique and allows you to make your own specialisation (e.g. research or private). I think it was amazing that everyone got their first choice. My research project helped showcase what I’ve learned in my degree on real data problems as well as network with people, which was pivotal in securing my job at Agilisys.
Read more from Yudhis Lumadyo
Yudhis Lumadyo
Junior Data Consultant at Agilisys
