MSc Health Data Science and AI

Please apply via our online application form, following any instructions for completion carefully.

There is high demand for this course and we operate a staged admissions process with selection deadlines throughout the year. Application deadlines for 2027 are to be confirmed.

Please visit the Application and Selection section for details of deadlines and the supporting documents that we require.

We operate a staged admissions process with selection deadlines throughout the year. We give preference to applicants from high-ranking institutions with grades above our minimum entry requirements.

Please submit supporting documentation with your application before the application deadline to avoid a delay in processing. Incomplete applications will roll over to the next stage and will not be reviewed until all documentation is received.

Application deadlines for 2027 entry are to be confirmed.

While we aim to give you a decision on your application by the deadline date, in some instances due to the competition for places and the volume of applications received, it may be necessary to roll your application forward to the next deadline date. If this is the case we will let you know after the deadline date. Applications received after our final selection deadline will be considered at our discretion.

Applicants who are made a conditional offer of a place must demonstrate they have met all the conditions of their offer.

Supporting documents

We require the following documents before we can consider your application:

• Official Bachelor degree transcripts, including official translations and original language copies if study not undertaken in English. 2+2 and 3+1 applicants must provide official transcripts and certificates from both institutions.
• An official document from your university verifying your current weighted average mark (not arithmetic average) if this information is not included in your transcript of study. Where grades are given as a percentage, the weighted average mark must also be recorded as a percentage.
• Degree certificate if you have already graduated. If you are still studying, please provide an official list of all the units you are taking in your final year.
• Personal statement.
• A CV.
• Two academic references dated and signed within six months of your application.
  Personal statement of approximately 300-500 words about why you wish to take this course and how it will affect your personal and professional development.

If English is not your first language, we also require proof of your English language ability. If you have already taken an English language qualification, please include your certificate with your application.

Our MSc Health Data Science and AI course aims to create a new breed of scientist who can understand the healthcare sector and medicine, how data is collected and analysed, and how this can be communicated to influence various stakeholders.

The current models of healthcare delivery worldwide are subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable.

This, coupled with a drive towards personalised medicine and the advent of AI, presents an opportunity for a step change in healthcare delivery.

To do this, we need to make the best use of the health data we collect, and create a better understanding of the relationship between treatments, outcomes and patients.

This MSc promotes the need for translational thinking to provide the knowledge, skills and understanding that will be applied across new challenges within healthcare delivery.

A multidisciplinary approach to health data science is the focus of this course, with students from a variety of professional backgrounds. The structure of the MSc ensures that you will share knowledge with each other and learn to work in multidisciplinary teams, rather than in specialist silos.

You will be taught by world-leading professionals and academics in the field of health data science, statistics, machine learning and AI, information engineering, omics and digital biology, and digital transformation of the healthcare system.You will also mix with students from a range of disciplines from all over the world.

The current structure of the course (subject to change) involves four mandatory units in the first semester including:

• Introduction to Health Data Science
• Programming for Health Data
• Statistics for Health Data Science
• Machine Learning for Health Data Science

The second semester offers 11 optional units, of which four need to be completed (for those studying for a MSc).

The optional units include:

• Design and Analysis of Randomised Controlled Trials
• Clinical Prediction Models
• Introduction to Clinical Bioinformatics
• Computational Methods for Multi-Modal Data
• Statistical Modelling and Inference for Health
• Modelling Simulations for Medical Images and Anatomies
• Decision Support Systems
• Digital Transformation Project
• Introduction to Health Informatics
• Principles of Digital Epidemiology
• Deep Learning for Medical Image Computing

There is also a 60-credit research project, which involves writing a dissertation.

PhD with integrated master's

If you're planning to undertake a PhD after your master's, our Integrated PhD programme will enable you to combine your postgraduate taught course with a related PhD project in biology, medicine or health.

This course will allow you to:

• gain key background knowledge and an understanding of the healthcare system, from the treatment of individuals to the wider population;
• gain an understanding of the governance structures and frameworks used when working with health data and in the healthcare sector;
• experience key technical skills and software for working with and manipulating health data;
• understand the breadth and depth of application methods and the potential uses of health data;
• comprehend key concepts and distinctions of the disciplines that need to be synthesised for effective health data science;
• appreciate the role of the health data scientist and how they fit into the wider healthcare landscape;
• understand the importance of patient-focused delivery and outcomes;
• develop the in-depth knowledge, understanding and analytical skills needed to work with health data effectively to improve healthcare delivery;
• develop a systematic and critical understanding of relevant knowledge, theoretical frameworks and analytical skills to demonstrate a critical understanding of the challenges and issues arising from heterogeneous data at volume and scale, and turn them into insight for healthcare delivery, research and innovation;
• apply practical understanding and skills to problems in healthcare;
• work in a multi-disciplinary community and communicate specialist knowledge of how to use health data to a diverse community;
• evaluate the effectiveness of techniques and methods in relation to health challenges and the issues addressed;
• extend your knowledge, understanding and ability to contribute to the advancement of healthcare delivery knowledge, research or practice through the systematic, in-depth exploration of a specific area of practice and/or research.
  

Research project options

MSc students have the opportunity to conduct their research project in collaboration with individuals from the NHS and the biopharmaceutical industry.

The course brings together technical, modelling and contextual skills and applies these to real world problems when harnessing the potential of health data.

In each of the units that deliver the key skills, both the importance of the patient and the governance surrounding working in the healthcare environment (especially structures around information governance) is embedded throughout.

Each unit will use case studies from existing work and research in our Division. The course will focus on large and complex health datasets (often routinely collected) in environments that safeguard patient confidentiality.

The course will encourage intellectual curiosity, creativity, and critical thinking, providing transferable skills for lifelong learning and research and cultivation of reflective practice.

Through the development of these innovation, critical, evaluative, analytical, technical, problem solving and professional skills, you will be able to conduct impactful work and advance healthcare delivery.

We see learning and teaching as collaborative knowledge construction, which recognises the contribution of all stakeholders (academic staff, service users and carers, and students). This is demonstrated in the course through contributions made by these stakeholders through case studies, examples, invited seminars and participation in group work.

A variety of teaching methods will be used within the constraints of the method of delivery. The course will be student centred and will be delivered from the outset using a combination of face-to-face, distance learning and blended learning units.

A range of assessments are used within each course unit and across the course as a whole.

All assessments require you to integrate knowledge and understanding, and to apply this to case studies and the outcomes of each unit.

Assessment will occur in a variety of forms including (but not exclusively) essays, case studies, examinations, assessed seminar/tutorial presentations and literature reviews.

Written assignments and presentations have a formative role in providing feedback (particularly in the early stages of course units) as well as contributing to summative assessment.

Online quizzes provide a useful method of regular testing, ensuring that you actively engage with the taught material.

The assessment of tutorials contains an element of self and peer evaluation, so you can learn the skills needed for managing and taking part in collaborative activity.

The course also places an emphasis on group work, as this is a vital skill for professionals operating in a multidisciplinary area such as health data science, and this is shown in the teaching methods and assignments.

Each unit has a different emphasis on the group work assessment based on the nature of the material being covered, how they are to apply the knowledge and the work they are to complete.

The dissertation for the MSc requires you to undertake an extended written piece of work (approx. 8,000 words) that focuses on a specific aspect of health data science.

You will have access to our Division's state-of-the-art research and teaching facilities.

In addition, you will have full access to the University's IT and library facilities. This will include Canvas and other e-learning facilities.

Each student will have an identified personal tutor who can provide advice and assistance throughout the course. During the research project, you will be in regular contact with your research supervisor.

Data science is increasing in importance in the fields of medicine and public health, enabling evidence-based decision making in areas such as disease prevention, diagnosis, treatment development and healthcare resource allocation. As healthcare systems generate ever-growing volumes of data, there is increasing demand for professionals who can extract meaningful insights and translate them into improved patient outcomes (UK Department for Science, Innovation and Technology, AI Labour Market Survey 2025)

For students completing this MSc, there are a wealth of career opportunities available in healthcare (including the NHS), industry and academia. Data science and AI skills are among the most sought-after capabilities in the global workforce, with demand for specialists continuing to grow across both public and private sectors (World Economic Forum, Future of Jobs Report 2025).

Graduates will be well placed to pursue careers as data scientists, biostatisticians, clinical informaticians, research analysts and AI specialists. Students completing the master's course will also be eligible for consideration for PhD programmes within the Faculty of Biology, Medicine and Health, and will be encouraged and supported to develop relevant research proposals.

Many of our graduates have gone on to study for a PhD, or secured roles in healthcare, life sciences, technology and commercial organisations.

Some of our recent MSc Health Data Science graduates are employed at:

• UK Health Security Agency
• The Christie NHS Foundation Trust
• Health Services Laboratories
• Merck Sharp & Dohme (MSD), China
• The Institute of Cancer Research
• Imperial College London