Health Data Science MSc (2022)

London, Bloomsbury

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.

UK students International students

Study mode
UK tuition fees (2022/23)

£12,900

£6,450

Programme fees on a modular (flexible) basis.

Overseas tuition fees (2022/23)

£29,400

£14,700

Programme fees on a modular (flexible) basis.

Duration

1 academic year

2 academic years

5 academic years

Programme starts

September 2022

Applications accepted

All applicants: 18 Oct 2021 – 31 Mar 2022

Applications closed

Notification

Application closes at 17:00 GMT.

We recommend that you submit your application as soon as possible. The programme may remain open if places are still available after 31 March 2022 and will be closed as soon as it is full or by 30 June 2022.

Entry requirements

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.

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

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level. International Preparation Courses

Further information can be found on our English language requirements page.

Equivalent qualifications

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. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

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.

Who this course is for

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.

Read about what it takes to study for a Master's at UCL

What this course will give you

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, 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.

Read about what you'll get out of a graduate programme

The foundation of your career

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.

Employability

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.

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.

Modules

You will be taking five compulsory taught modules and three optional modules and completing a compulsorydissertation.

Taught modules are mostly delivered face-to-face, although some blended learning modules are available as options.

Compulsory modules

  • Principles of Health Data Science - CHME0012
  • Software Development with Python for Health Data Science - CHME0031
  • Basic Statistics for Medical Sciences - IEHC0046
  • Data Methods for Health Research - CHME0013
  • Regression Modelling - IEHC0050
  • Dissertation - CHME0021

Optional modules

  • Machine Learning in Healthcare and Biomedicine - CHME0016
  • Advanced Machine Learning for Healthcare - CHME0035
  • Computational Genetics of Healthcare - CHME0034
  • Advanced Statistics for Records Research (Blended) - CHME0015
  • Public Health Data Science (Blended) - CHME0017
  • Essentials of Informatics for Healthcare Systems (Blended) - CHME0025

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. Some of our blended modules run in Term 3 and reassessment in these modules may lead to a delay in your award.

Dissertation

All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).

Examples of past projects:

  • Generating and Evaluating Synthetic Mixed-type Structured Electronic Health Records Based on State-of-the-art Generative Adversarial Networks
  • Prediction of Alzheimer’s Disease (AD) from MRI using a Convolutional Neural Network
  • Predicting Patients with Diabetes at Risk of 30-day Emergency Readmission Using Supervised Machine Learning

You will be taking five compulsory taught modules and three optional modules and completing a compulsory dissertation over two years.

Taught modules are mostly delivered face-to-face, although some blended learning modules are available as options.

Compulsory modules

Year One

  • Principles of Health Data Science - CHME0012
  • Software Development with Python for Health Data Science - CHME0031
  • Basic Statistics for Medical Sciences - IEHC0046
  • Data Methods for Health Research - CHME0013
  • Regression Modelling - IEHC0050

Year Two

  • Dissertation - CHME0021

Optional modules

Year Two

  • Machine Learning in Healthcare and Biomedicine - CHME0016
  • Advanced Machine Learning for Healthcare - CHME0035
  • Computational Genetics of Healthcare - CHME0034
  • Advanced Statistics for Records Research (Blended) - CHME0015
  • Public Health Data Science (Blended) - CHME0017
  • Essentials of Informatics for Healthcare Systems (Blended) - CHME0025

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. Some of our blended modules run in Term 3 and reassessment in these modules may lead to a delay in your award.

Dissertation

All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).

Examples of past projects:

  • Generating and Evaluating Synthetic Mixed-type Structured Electronic Health Records Based on State-of-the-art Generative Adversarial Networks
  • Prediction of Alzheimer’s Disease (AD) from MRI using a Convolutional Neural Network
  • Predicting Patients with Diabetes at Risk of 30-day Emergency Readmission Using Supervised Machine Learning

You will be taking five compulsory taught modules and three optional modules and completing a compulsory dissertation over two to five years.

Taught modules are mostly delivered face-to-face, although some blended learning modules are available as options.

Compulsory modules

Year One

  • Principles of Health Data Science - CHME0012
  • Software Development with Python for Health Data Science - CHME0031
  • Basic Statistics for Medical Sciences - IEHC0046
  • Data Methods for Health Research - CHME0013

Year Two

  • Regression Modelling - IEHC0050

Final Year

  • Dissertation - CHME0021

Optional modules

Year Two to Five

  • Machine Learning in Healthcare and Biomedicine - CHME0016
  • Advanced Machine Learning for Healthcare - CHME0035
  • Computational Genetics of Healthcare - CHME0034
  • Advanced Statistics for Records Research (Blended) - CHME0015
  • Public Health Data Science (Blended) - CHME0017
  • Essentials of Informatics for Healthcare Systems (Blended) - CHME0025

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. Some of our blended modules run in Term 3 and reassessment in these modules may lead to a delay in your award.

Dissertation

All students undertake an independent research project which culminates in a dissertation. Project Proposal 20% (2,000 words); Journal Article 80% (6,000 words).

Examples of past projects:

  • Generating and Evaluating Synthetic Mixed-type Structured Electronic Health Records Based on State-of-the-art Generative Adversarial Networks
  • Prediction of Alzheimer’s Disease (AD) from MRI using a Convolutional Neural Network
  • Predicting Patients with Diabetes at Risk of 30-day Emergency Readmission Using Supervised Machine Learning

Compulsory modules

Principles of Health Data Science


Data Methods for Health Research


Dissertation in Health Data Science


Software Development with Python for Health Data Science


Basic Statistics for Medical Sciences


Regression Modelling

Optional modules

Advanced Statistics for Records Research


Machine Learning in Healthcare and Biomedicine



Essentials of Informatics for Healthcare Systems


Computational Genetics of Healthcare


Advanced Machine Learning for Healthcare


Database and Information Management Systems

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.

Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Health Data Science.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the .

All open days and events

Online - Open day

Graduate Open Events: Health Informatics

Listen to our virtual graduate open event to learn more about our Health Informatics programmes, meet our programme leads and hear the answers to the questions you may have about studying at the UCL Institute of Health Informatics.

Online - Open day

Graduate Open Events: Applying for Graduate Study at UCL

The Applying to UCL for graduate study session took place in December 2021. The session, covered by our Graduate Admissions and Student Recruitment teams, provides helpful information about the process of applying for graduate study, as well as offering an insight into what we consider to be a competitive application.

Watch on demand event

Fees and funding

Fees for this course

UK students International students

Fee description Full-time Part-time
Tuition fees (2022/23) £12,900 £6,450
Tuition fees (2022/23) £29,400 £14,700

Programme fees on a modular (flexible) basis.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees.

Additional costs

There are no additional costs for this programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.

Funding your studies

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

See all the ways you can fund your studies

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 £90 for online applications and £115 for paper applications. Further information can be found at Application fees.

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.

Please note that you may submit applications for a maximum of two graduate programmes in any application cycle.

This programme requires two references. Further information regarding references can be found on this Selecting your references page.

UCL is regulated by the Office for Students.

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