This programme covers computational and statistical methods as applied to problems in data-intensive medical research. As part of this programme, you will gain an understanding of techniques that are transforming medical research and creating exciting new commercial opportunities.
Modes and duration
Tuition fees (2020/21)
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 Students website. Fees for flexible, modular study are charged pro-rata to the appropriate full-time Master's fee taken in an academic session.
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: Good
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
You will learn how to link and analyse large complex datasets. You will also 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 programme draws on a range of areas, including epidemiology, computer science, statistics and other fields, such as genetics.
Students undertake modules to the value of 180 credits.
The programme consists of six compulsory modules (90 credits), two optional modules (30 credits), and a dissertation (60 credits).
A Postgraduate Diploma, six compulsory modules (90 credits), two optional modules (30 credits), full-time one year, part-time two years or flexible study up to five years, is offered.
A Postgraduate Certificate, two compulsory modules (30 credits), two optional modules (30 credits), full-time one year, part-time two years or flexible study up to five years, is offered.
Upon successful completion of 180 credits, you will be awarded a MSc in Health Data Science. Upon successful completion of 120 credits, you will be awarded a PG Dip in Health Data Science. Upon successful completion of 60 credits, you will be awarded a PG Cert in Health Data Science.
Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.
- Basic Statistics for Medical Sciences
- Data Methods for Health Research
- Principles of Epidemiology Applied to Electronic Health Records Research
- Principles of Health Data Science
- Regression Modelling
- Scientific Software Development with Python for Health Research
- Advanced Computational Biology
- Advanced Statistics for Records Research
- Essentials of Informatics for Healthcare Systems
- Information Retrieval and Data Mining
- Machine Learning in Healthcare and Biomedicine
- Public Health Data Science
- Further information about these modules is available on the department website.
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.
Today, 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. As a student on this programme, we will work with you to develop your passion and interest in this area of research. You will gain skills for a career as an entrepreneur, scientist or manager, working in industry, academia or 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 a 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 - IQVIA, Roche, AstraZeneca and Public Health England - 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 - Roche, IQVIA, AstraZeneca, Evidera, Hitachi - and small start-ups, while others are working in health care or pursuing their interests in universities, working towards PhDs.
The lecturers on this programme are international experts in health data science and you will learn about cutting-edge research projects. Programme content is aligned with the newly-founded Health Data Research UK (HDR UK), a multi-funder UK institute for health and biomedical informatics research, led by UCL in London. 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 - hospitals, data companies, pharma - 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
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.
There is an application processing fee for this programme of £80 for online applications and £105 for paper applications. Further information 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 programming, 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
- 24 July 2020
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 Health Data Science at graduate level
- why you want to study Health Data Science 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.
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