Application deadline
Thursday 7 August 2025
Applicants should apply as early as possible to be eligible for certain scholarships.
Entry requirements
- A 2.1 Honours undergraduate degree. If you studied your first degree outside the UK, see the international entry requirements.
- You should some have experience in statistical data analysis and some familiarity with methods such as sampling and regression. This might be through one of the following:
- an advanced secondary school or high school level qualification in statistics or another quantitative scientific subject
- undergraduate-level modules in a quantitative scientific subject
- relevant professional experience.
- Experience in computer programming is useful but is not essential.
- English language proficiency.
The MSc in Health Data Science welcomes applicants from a range of disciplinary backgrounds including, but not limited to:
- Computer Science
- Mathematics
- Medicine
- Public Health
- Software Engineering
- Statistics.
The qualifications listed are indicative minimum requirements for entry. Some academic Schools will ask applicants to achieve significantly higher marks than the minimum. Obtaining the listed entry requirements will not guarantee you a place, as the University considers all aspects of every application including, where applicable, the writing sample, personal statement, and supporting documents.
Application requirements
- A CV that includes your personal details with a history of your education and employment to date
- A personal statement explaining:
- why you have applied for this course
- how it relates to your personal or professional ambitions
- how your academic and professional background show you have the skills needed to work effectively at postgraduate level
- Two original signed academic references
- Academic transcripts and degree certificates
For more guidance, see supporting documents and references for postgraduate taught programmes.
English language proficiency
If English is not your first language, you may need to provide an English language test score to evidence your English language ability. See approved English language tests and scores for this course.
Course details
Healthcare is being transformed by digital technologies and big data analytics. On the MSc in Health Data Science, you will explore the principles and practice of digital health implementation.
Highlights
- Aimed at students intending to follow a career in data science and digital health.
- Interdisciplinary character helps you to develop a more rounded understanding of digital health questions and concepts.
- Applied components provide practical skills in medical data analysis and the use of digital technologies to address healthcare challenges.
- Links with the Sir James Mackenzie Institute for Early Diagnosis bring you into contact with current digital health research across different disciplines.
- In 2024, we introduced a fully-funded PhD, which is available exclusively for our Health Data Science students to apply for.
The MSc in Health Data Science is distinguished by its interdisciplinary character and an emphasis on applied skills that will be of particular value if you are looking to follow a career in digital health.
Digital technology is transforming healthcare. It is enabling faster diagnosis and better treatment of illnesses, supporting improvements in patient care, and making healthcare settings more efficient. That transformation is creating a need for professionals who understand existing medical technologies and who have the skills and expertise to develop new technologies, analyse medical data, and inform policy on medical data analytics. Students from the MSc in Health Data Science will be able to fill those roles.
On the MSc you will learn about the theoretical underpinnings of digital health. You will look at different forms of health data, the technology that generate them, methods used for processing and analysis, and how digital data is integrated in clinical decision making. In particular, you will develop an appreciation of the challenges in handling, storing and analysing big data in healthcare contexts.
An understanding of these principles provides a basis for studying the practical applications of digital health and developing your understanding of how digital health concepts can be applied to solve real-world medical problems. You will learn practical skills in medical data analysis and the use of digital technologies to address healthcare challenges. You will develop your understanding of techniques for programmatically processing medical data such as genetic data, medical images, and patient vital signs. You will also learn about digital health governance and the ethical considerations that can arise when designing and executing medical data analysis studies.
Particular attention is paid to training in medical image analysis, bioinformatics and modelling and analysis of medical data such as patient records. Theoretical learning is applied to real-world case studies, and you will develop an understanding of practitioner and industry perspectives and the work that is needed across academia and other sectors to advance digital health. More broadly, you will develop practical skills in explaining digital health concepts to different audiences and the translation of academic thinking on digital into recommendations for policymakers and practitioners.
Digital health is inherently interdisciplinary. This MSc brings together academic staff, National Health Service (NHS) colleagues, and industrial partners providing a greater breadth of learning that encompasses real clinical problems as well as the solutions that digital health can provide.
In this way you will engage with critical perspectives on digital health principles and practice. You will be encouraged to develop a more rounded, interdisciplinary understanding of digital health questions and concepts. Through research-led teaching from scholars working in subjects including computer science, medicine, and statistics you will gain an appreciation of the technical, clinical, and analytical aspects of digital health and learn how to critically discuss digital health solutions from multiple disciplinary perspectives.
Optional modules allow you to explore topics such as knowledge discovery and datamining that will broaden your learning in key areas and further develop the interdisciplinary character of your studies.
The MSc in Health Data Science has close links with the Sir James Mackenzie Institute for Early Diagnosis. The Institute brings together researchers from a range of disciplines and builds on St Andrews’ international reputation in digital diagnosis, health data research, and biophotonics. These links will bring you into contact with current digital health research, giving your studies a remarkable richness and depth.
Modules
The modules published below are examples of what has been taught in previous academic years and may be subject to change before you start your course. For more details of each module, including weekly contact hours, teaching methods and assessment, please see the module catalogue.
The MSc is structured around a mixture of compulsory and optional modules.
Students will normally be required to complete the following modules unless they have significant experience in statistics and programming:
- Health Data Science Principles: explores the theoretical underpinnings of health data science and digital health; students consider different forms of health data, technologies and methods for processing and analysis, and the integration of digital data in clinical decision making.
- Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis.
Students will also be required to complete one of the following:
- Software for Data Analysis: covers the practical computing aspects of statistical data analysis, focussing on packages most widely used in the commercial sector, and the manipulation, checking, assessment, interpretation and presentation of data using various statistical models.
- Programming Principles and Practice: introduces computational thinking and problem solving skills to students who have no or little previous programming experience covering general programming concepts used in the development of software applications, such as data structures, functions, choice, iteration and recursion.
- Health Data Science Practice: looks at the practical applications of health data science and digital health; students learn practical skills in medical data analysis and the use of digital technologies to address healthcare challenges.
- Biomedical imaging and sensing: covers the fundamentals of image and signal processing, with how the different types of medical imaging modalities work (such as MRI, CT, PET, ultrasound and optical imaging) along with their uses and limitations in a clinical setting. Convolutional neural networks (CNNs) are introduced as a way to classify medical images.
All students will normally take modules in programming and quantitative methods in Semester 1 unless they have a sufficient background in computer science and data analysis or statistics. These modules complement the core modules.
All students will normally take modules in programming and quantitative methods in Semester 1 unless they have a sufficient background in computer science and data analysis or statistics. These modules complement the core modules.
Alongside the compulsory modules and the programming and quantitative methods modules, you will complete one or two other optional modules. Optional modules allow you to shape the degree around your own personal and professional interests.
Optional modules are expected to be offered in the following areas:
- data analysis
- information visualisation and visual analytics
- machine learning
- programming principles and practice
Optional modules are subject to change each year and require a minimum number of participants to be offered; some may only allow limited numbers of students (see the University’s position on curriculum development).
The final part of the MSc is the end of degree project. This takes the form of a period of supervised research where you will explore a health data science topic in depth.
Through the project you will show your ability to undertake sustained critical analysis, develop and improve your research skills, and produce an extended piece of written work that demonstrates a high level of understanding of your area of study.
You can choose to present your end of degree project as a written dissertation that emphasises your ability to plan and execute academically rigorous research.
If students choose not to complete the project requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a postgraduate diploma (PGDip). By choosing an exit award, you will finish your degree at the end of the second semester of study and receive a PGDip instead of an MSc.
Teaching
The taught modules are taken over two semesters – September to December (Semester 1) and January to May (Semester 2). The period from June to August is used to complete the end-of-degree project.
Each taught module will use teaching and learning methods appropriate to its aims. These may include seminars, workshops, lectures, tutorials, and independent study.
Assessment methods used may include essays, reports, presentations, practical exercises, reflective exercises, and examinations.
Fees
Home - £12,030
Overseas - £29,990
Application fee
Before we can begin processing your application, a payment of an application fee of £50 is required. In some instances, you may be eligible for an application fee waiver. Details of this, along with information on our tuition fees, can be found on the postgraduate fees and funding page.
Funding and scholarships
The University of St Andrews is committed to attracting the very best students, regardless of financial circumstances.
Commonwealth Shared Scholarship Scheme (CSSS)
The Commonwealth Shared Scholarships are a joint initiative between the Commonwealth Scholarship Commission (with funding from FCDO) and UK universities to support scholarships for students from least developed and lower middle-income Commonwealth countries who would not otherwise be able to study in the UK.
15% Recent Graduate Discount
If you have graduated from the University within the last three academic years, you may be eligible for a 15% discount on postgraduate taught tuition fees. Terms and conditions apply.
After your degree
Careers
The University of St Andrews’ global reputation makes its graduates highly valued by employers. The MSc in Health Data Science is aimed at students intending to follow a career in digital health, and you will develop skills commonly needed for digital health-related careers in healthcare settings, pharmaceutical companies, medical technology industries, and government.
In addition to broadening your subject knowledge and applying established techniques of research and enquiry, you will develop and demonstrate essential skills including:
- critical thinking and creativity
- analysis and appraisal
- problem solving and decision making
- personal leadership and project management
- interpersonal communication and team working
The University also offers two awards for all students known as the Skills Spotlight Award. You can develop a range of skills through the Saints Skills Awards or learn how to best articulate them through the Skills Builder Award. These skills are highly valued by employers and the awards are a good addition to your CV.
The Careers Centre offers one-to-one advice to all students as well as a programme of events to assist students in building their employability skills.
Further study
St Andrews offers a vibrant and stimulating research environment. One of the great strengths of our research degrees is the collegiate atmosphere, which enables access to expertise beyond your formal supervisors and the ability to conduct interdisciplinary research.
Research students are supported by a supervisory team throughout their studies and are assessed by means of a substantial thesis of original research.
What to do next
Information sessions
Meet our staff, learn more, and ask questions about how our courses can work for you.
Contact us
- Phone
- +44 (0)1334 46 2032
- gradschool@st-andrews.ac.uk
- Address
- Graduate School