This programme, supported by Health Data Research UK (HDR UK), aims to train a new generation of world-leading health data scientists, to work in both the public and private sector. Teaching will focus on building strong quantitative, computational and practical data management skills, while providing opportunities to develop key professional skills required to be a successful health data scientist.
Health Data Science is an emerging discipline, combining mathematics, statistics, epidemiology and informatics. This programme will equip graduates with the tools and skills to manage and analyse very large diverse datasets across healthcare systems.
The programme will enable you to:
- apply statistical and machine learning approaches to analyse health-related data
- acquire the tools and skills to manage very large diverse datasets across healthcare systems
- develop the professional skills – including teamwork, project management, and presentation skills – to work as a successful data scientist in the public or private sector
- understand the varied roles of a health data scientist within the wider health and health research environment
- learn about the key sources of health data, and the context in which these data are collected, implications of the context on issues such as data quality, accessibility, bias and the appropriateness of use to address specific questions
- study the key issues related to ethics, security and information governance
Support and partnerships
This programme is supported by Health Data Research UK – the national institute for health data science.
The programme will be delivered with the support of a number of partners, drawn from across the health data science landscape, including international healthcare consultancies (IQVIA, Panalgo), pharmaceutical companies (GSK), multinational technology companies (Microsoft Research), health-tech SMEs (Biosensors Beyond Borders), governmental agencies (National Institute for Health Protection), and national clinical audit providers (Royal College of Surgeons – Clinical Effectiveness Unit, Intensive Care National Audit & Research Centre and Deloitte).
These partners will help ensure that our programme fits the needs of prospective employers, both within academia and in industry. They will help us offer students on this programme hands-on experience with data arising from the whole health spectrum, from the molecular to the population.
Duration: one year full-time; part-time or split-study over two years. Ways to study explained.
Health Data Science
Watch Programme Directors Keith Tomlin and Damien Tully talk about the programme.
"LSHTM provides the perfect combination of programming, statistics and epidemiology. The course is also supported by Health Data Research UK".
Do you want to better understand the causes of disease and identify new ways to prevent, treat and cure disease?
The increasing amounts of electronically captured and stored health related data provide enormous opportunities to achieve these goals. Making optimal use of these data requires people with wide-ranging expertise in areas including statistics, programming, informatics and epidemiology.
Health Data Science at LSHTM
LSHTM is a world leader in the use of health data for research, with expertise in the creation, linkage and analysis of a wide range of data sources, encompassing data on environmental and social factors as well as ‘omic data, both human and pathogen. In addition, LSHTM has global reach and a large international network of partnerships enabling data science collaborations worldwide.
Our electronic health records (EHR) work encompasses pharmacoepidemiology, phenotyping, vaccine effectiveness/safety, health policy assessment, and infectious disease surveillance. We have expertise in using national audits for quality improvement, and developing NHS performance indicators. Our work is underpinned by an internationally recognised group of biostatistical methodologists.
Is this the right programme for me?
Medical Statistics, Epidemiology, and Health Data Science are closely related disciplines. We offer Master's degrees in each of these disciplines. Here are some of the differences in emphasis between them:
- MSc Health Data Science
- explores a range of machine learning techniques
- has a greater focus on computational data skills, including programming and tools for data management
- has a greater focus on professional skills training (e.g. teamwork, project management, presentation skills)
- MSc Medical Statistics
- has a greater focus on the theoretical underpinnings of the statistical methods studied
- explores study design, for both clinical trials and observational studies
- includes a more in-depth exploration of certain statistical methods (e.g. models for hierarchical data)
- MSc Epidemiology
- has a greater focus on developing the research question
- includes an in-depth exploration of study design, protocol development and conducting appropriate statistical analyses
- emphasises the ability to critically appraise studies and interpret results
- offers the opportunity to learn about concepts and techniques specific to the study of infectious diseases
What are my career prospects as a health data scientist?
The demand for well-trained health data scientists is high and likely to increase over time. We anticipate our graduates may pursue careers in:
- National health services
- The pharmaceutical industry
- Contract research organisations
- Governmental institutions (such as the Health Protection Agency and the World Health Organization)
- Non-governmental organisations
- Health-tech SMEs (small and medium-sized enterprises)
The below structure outlines the proposed modules for this programme. Programme and module specifications provide full details about the aims and objectives of each module, what you will study and how the module is assessed.
- Structure of the year
Term 1 (September - December) consists of ten teaching weeks for AB1 slot modules, plus one Reading Week* in the middle of the term. Followed by the Winter break.
Term 2 (January - March) consists of a further ten weeks of teaching for C and D slot modules, plus a Reading Week in the middle of the term. C modules are taught in five half-week blocks before Reading Week. D modules are taught in five half-week blocks after Reading Week. Followed by the Spring break.
Term 3 (April - September) consists of the project report.
*Reading Week is a week during term where no formal teaching takes place. It is a time for private study, preparing for assessments or attending study/computer skills workshops. There are two Reading Weeks at LSHTM: one in November and the other in February.
- Term 1
All students take five compulsory AB1 modules:
- Introduction to Health Data Science
- Health Data Management
- Epidemiology for Health Data Science
- Statistics for Health Data Science
- Term 2
Students take a total of four study modules, one from each timetable slot (C1, C2, D1, D2).
- Machine Learning (compulsory)
- Data Challenge (compulsory)
- Analysis of Hierarchical and Other Dependent Data
- Genomics Health Data
- Modelling & the Dynamics of Infectious Diseases
- Spatial Epidemiology in Public Health
- Analysis of Electronic Health Records
- Environmental Epidemiology
- Survival Analysis and Bayesian Statistics
- Term 3: Project report
Students will start working on their summer project mid-April for submission by early September. The project will typically involve identifying appropriate data to tackle a particular research question, extracting and cleaning the data, analysing the data and creating suitable visualisations of the results. Students will describe the whole project in a detailed written report.
Please note: Should it be the case that you are unable to travel overseas or access laboratories in order to complete your project, you will be able to complete an alternative desk-based project allowing you to obtain your qualification within the original time frame. Alternatively, you will be able to defer your project to the following year.
As well as traditional lectures followed by problem-based practical sessions, with or without computers, teaching will include:
- Flipped classroom approaches where students are provided with materials to read/watch independently, followed by formative assessment in class to assess understanding (e.g. via Moodle-based multiple choice questions), allowing contact time to focus on practical problem-based learning.
- Interactive lectorials, alternating lecture-based and hands-on practical sessions.
- Panel discussions and workshops, to stimulate debate particularly for current live controversies such as the ethics of algorithms.
- Teamwork, particularly in the team-based module and the datathon.
- Opportunities to develop and practice professional skills, including a range of student-led presentations, modules which require student teams to interact with a client (someone who is not a data scientist working outside of the LSHTM who wishes to “employ” our students to address a particular research question).
Changes to the programme
LSHTM will seek to deliver this programme in accordance with the description set out on this programme page. However, there may be situations in which it is desirable or necessary for LSHTM to make changes in course provision, either before or after registration. For further information, please see our page on changes to courses.
*Mobile users, scroll right to view fees
In order to be admitted to an LSHTM master's degree programme, an applicant must:
- hold either a first degree at Second Class Honours (2:2) standard in a relevant discipline, or a degree in medicine recognised by the UK General Medical Council (GMC) for the purposes of practising medicine in the UK, or another degree of equivalent standard awarded by an overseas institution recognised by UK ENIC or the GMC.
- hold a professional qualification appropriate to the programme of study to be followed obtained by written examinations and judged by LSHTM to be equivalent to a Second Class Honours (2:2) degree or above.
- have relevant professional experience or training which is judged by LSHTM to be equivalent to a Second Class Honours (2:2) degree or above.
If you have not previously studied in the UK, you can check our guide to international equivalent qualifications for our master's degrees.
Relevant subjects and appropriate qualifications for the MSc Health Data Science include mathematics, statistics, physics, engineering and computer science. Other life science qualifications will be considered subject to evidence within transcripts of sufficient quantitative exposure (please make this evidence clear within the application). Free online courses (e.g. Coursera Data Science) undertaken by the applicant will also be considered at the application stage.
After submitting an application, the Programme Director will email a pre-entry assessment to evaluate the applicants quantitative and programming knowledge. This assessment must be completed to move further in the application process. The 60-90 minute online assessment will review the applicant's knowledge in Probability and Statistics, Linear Algebra, Functions, Calculus, Combinatorics and Programming/Programming logic (prior exposure to writing programming syntax is essential). View the previous pre-entry assessment as an example.
Applicants who do not meet the minimum entry requirement, but who have relevant professional experience may still be eligible for admission. Qualifications and experience will be assessed from the application.
English language requirements
If English is not your first language, you will need to meet these requirements: Band B
Please see our English language requirements for more information.
You will need the equivalent of a bachelor's degree to undertake an MSc. This will usually require you to have a BSc degree or have completed the first three years of your medical degree. More information on intercalating an MSc at LSHTM.
Access and widening participation
At LSHTM we are committed to ensuring that students from all backgrounds feel encouraged to apply to study with us. To that end, we have introduced an innovative contextual admissions system that is designed to consider any barriers applicants may have faced and take account of the circumstances in which their grades have been achieved, rather than relying on results alone. More information on widening participation at LSHTM.
Applications should be made online and will only be considered once you have provided all required information and supporting documentation.
Please also read LSHTM's Admissions policies prior to submitting your application.
You can apply for up to two master's programmes. Make sure to list them by order of preference as consideration will be given to your top choice first.
All applicants are encouraged to apply as early as possible to ensure availability of a place and a timely decision on their application. This is particularly important for applicants with sponsorship deadlines.
We strongly advise that you apply early as popular programmes will close earlier than the stated deadline if they become full.
The final closing dates for all taught Master’s applications for entry in the 2023/24 academic year is:
- Sunday 23 July 2023 at 23:59 UK time for all students requiring a Student visa
- Thursday 31 August 2023 at 23:59 UK time for all UK, Irish and non-Student visa students
Applicants will be required to meet the conditions of their offer and provide all necessary documents by the date of their Offer of Admission.
A standard non-refundable application fee of £50 applies to all taught Master’s degree programmes and is payable upon application submission. Income generated from the application fee is shared between scholarships and student hardship fund.
Tuition fee deposit
Applicants are required to respond to their Offer of Admission and pay the £500 deposit within 28 days of receipt, or their place will be released and the offer automatically declined. The deposit is deductible from tuition fees upon full registration with LSHTM.