The UCL programme in Data Science for Research in Health and Biomedicine covers computational and statistical methods as applied to problems in data-intensive medical research. Students learn techniques that are transforming medical research and creating exciting new commercial opportunities. Our recent graduates, many of whom begin paid internships while completing the MSc, have moved on to roles in industry and academia.
Modes and duration
Tuition fees (2018/19)
- £9,850 (FT) £4,950 (PT)
- £24,860 (FT) £12,380 (PT)
Note on fees: The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Current Students website.
A minimum of an upper second-class Bachelor's degree, or equivalent, in a clinical or a scientific discipline with a significant computational or mathematical element.
English language requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
The English language level for this programme is: Standard
Further information can be found on our English language requirements page.
Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.
International applicants can find out the equivalent qualification for their country by selecting from the list below.
Select your country:
About this degree
Students learn how to link and analyse large complex datasets. They design and carry out complex and innovative clinical research studies that take advantage of the increasing amount of available data about the health, behaviour and genetic make-up of small and large populations. The content is drawn from epidemiology, computer science, statistics and other fields, including genetics.
Students undertake modules to the value of 180 credits.
The programme consists of five core modules (75 credits), three optional modules (45 credits) and a dissertation/report (60 credits).
A Postgraduate Diploma (120 credits) is offered.
A Postgraduate Certificate (60 credits) is offered.
- Principles of Epidemiology Applied to Electronic Health Records Research
- Data Management for Health Research
- Statistics for Epidemiology and Public Health
- Statistical Methods in Epidemiology
- Topics in Health Data Science
- Advanced Statistics for Records Research
- Database Systems
- Information Retrieval and Data Mining
- Principles of Health Informatics
- Machine Learning in Healthcare and Biomedicine
- Statistics for Interpreting Genetic Data
- Electronic Health Records
- Clinical Decision Support Systems
All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).
Teaching and learning
The programme is delivered by clinicians, statisticians and computer scientists from UCL, including leading figures in data science. We use a combination of lectures, practical classes and seminars. A mixture of assessment methods is used including examinations and coursework.
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Students on this programme will be passionate about research and know that, in the 21st century, some of the most exciting, stimulating and productive research is carried out using large collections of data acquired in big collaborative endeavours or major public or private initiatives. We hope that graduates will build on that passion and, together with the experience gained on the programme, will go one to develop careers as entrepreneurs, scientists and managers, working in industry, academia and healthcare.
The programme is designed to meet a need, identified by the funders of health research and by a number of industrial organisations and healthcare agencies, for training in the creation, management and analysis of large datasets. This programme is practical, cross-disciplinary and closely linked to cutting-edge research and practice at UCL and UCL’s partner organisations. Data science is arguably the most rapidly growing field of employment at the moment and employers recruiting in health data science include government agencies, technology companies, consulting and research firms as well as scientific organisations. A number of employers are supporting the programme in different ways, including providing paid internships to selected students.
Why study this degree at UCL?
Data science is an exciting area with a dynamic job market, including in healthcare. Our graduates have gone on to work for a range of companies, including large research organisations and small start-ups, while others are working in health care or pursuing their interests in universities.
The lecturers on this programme are international experts in health data science and students will learn about cutting-edge research projects. The collaboration is part of the Farr Institute, a network of centres of excellence created to enhance the UK’s strength in data-intensive research. This MSc will draw on that collaboration, giving students access to the most advanced research in the field.
We work closely with a range of employing organisations to ensure that our graduates have the best possible preparation for a career in data science. This includes offering industry-sponsored dissertations for selected students.
Department: Institute of Health Informatics
Student / staff numbers
› 11 staff
including 10 postdocs
› 98 taught students
› 14 research students
Staff/student numbers information correct as of 1 August 2017.
Research Excellence Framework (REF)
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
What our students and staff say
"UCL has a strong identity. The tradition of radical thinking is valued, and people who work here don't just pay lip service to those ideals: they expect and want the freedom to develop and express their ideas and give others the space to do the same. My interests are in the computer systems that clinicians use to make decisions. My current research focuses on the computer analysis of medical images, working with a team using chest X-rays to screen vulnerable groups for tuberculosis."
Paul TaylorHealth Informatics MSc and Data Science for Research in Health and Biomedicine MSc
UCL Centre for Health Informatics & Multi-professional Education
Application and next steps
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
Application fee: There is an application processing fee for this programme of £75 for online applications and £100 for paper applications. More details about the application fee can be found at www.ucl.ac.uk/prospective-students/graduate/taught/application.
Who can apply?
The programme provides a good grounding in statistics, and no prior knowledge is assumed. However applicants should ensure that evidence of their readiness for this kind of training is clearly presented either through their educational history or in their personal statement. Applicants whose first degree has little or no numerical content are unlikely to be accepted.
- All applicants
- 27 July 2018
For more information see our Applications page.Apply now
What are we looking for?
When we assess your application we would like to learn:
- why you want to study Data Science for Research in Health and Biomedicine at graduate level
- why you want to study Data Science for Research in Health and Biomedicine at UCL
- what particularly attracts you to this programme
- how your personal, academic and professional background meets the demands of a challenging programme
- where you would like to go professionally with your degree
Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.