Why study this course?
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.
- Prepare yourself for a career in data science and digital health
- Develop a more rounded understanding of digital health questions and concepts through interdisciplinary study
- Gain practical skills in medical data analysis and the use of digital technologies to address healthcare challenges
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.
On the MSc you will learn about the theoretical underpinnings of digital health. You will look at:
- different forms of health data and the technologies that generate them
- methods used for processing and analysis
- 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.
Teaching
Seminars, workshops, lectures, tutorials and independent study.
Degree project
An extended piece of written work that demonstrates a high level of understanding of your chosen area of study.
Assessment
Essays, reports, presentations, practical exercises, reflective exercises and exams.
Modules
The St Andrews degree structure is designed to be flexible. You study compulsory modules delivering core learning together with optional modules you choose from the list available that year.
If you choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma (PGDip) instead, finishing the course at the end of the second semester of study.
For more details, including weekly contact hours, teaching methods and assessment, please see the module catalogue. The modules are examples from previous academic years and may be subject to change before you start your course.
-
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, focusing 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.
-
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
-
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.
What it will lead to
Careers
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
We are committed to supporting your career aspirations, whatever stage your career is at.
Our Careers Centre can help connect you to our extensive global alumni community for advice and mentoring, as well as offering career coaching, bespoke workshops, employer connections, experiences, and application support.
Our International Education and Lifelong Learning Institute can also support you with academic and professional skills development.
Elevate your career
The University of St Andrews' global reputation makes our graduates highly valued by employers.
Further your education
Graduates may continue their education by enrolling for a PhD in St Andrews or worldwide.
Go your own way
Our Entrepreneurship Centre offers training and start-up support, gives you access to experienced and expert mentors and an investor network, and one-to-one advice to help you realise your commercial potential.
Why St Andrews?
Interdisciplinary courses draw on experts material from across the University so students have a more rounded understanding of contemporary issues.
The Graduate School is a vibrant, stimulating postgraduate community. Graduate School events bring students together and help foster interdisciplinary identity. Students make social and intellectual connections within and across their Masters degree groups.
Alumni
When you graduate you become a member of the University's worldwide alumni community. Benefit from access to alumni clubs, the Saint Connect networking and mentoring platform, and careers support.
Ask a student
If you are interested in learning what it's like to be a student at St Andrews you can speak to one of our student ambassadors. They'll let you know about their top tips, best study spots, favourite traditions and more.
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.
Fees and funding
- UK: £12,030
- Rest of the world: £29,990
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.
Scholarships and funding
We are committed to supporting you through your studies, regardless of your financial circumstances. You may be eligible for scholarships, discounts or other support:
- 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.
- St Andrews Sanctuary Scholarship
- St Leonard's funding opportunities
- Graduate discount (15% off tuition fees)
Contact us
Start your journey
Legal notices
Admission to the University of St Andrews is governed by our Admissions policy
Information about all programmes from previous years of entry can be found in the course archive.
Curriculum development
As a research intensive institution, the University ensures that its teaching references the research interests of its staff, which may change from time to time. As a result, programmes are regularly reviewed with the aim of enhancing students' learning experience. Our approach to course revision is described online.
Tuition fees
The University will clarify compulsory fees and charges it requires any student to pay at the time of offer. The offer will also clarify conditions for any variation of fees. The University’s approach to fee setting is described online.
Page last updated: 13 March 2025