MSc in Health Data Science
University of St Andrews
Key Information
Campus location
Saint Andrews, United Kingdom
Languages
English
Study format
On-Campus
Duration
1 year
Pace
Full time
Tuition fees
GBP 29,950 *
Application deadline
08 Aug 2024
Earliest start date
Sep 2024
* overseas / home: £11,680
Introduction
The MSc in Health Data Science explores the principles and practice of digital health as well as applied skills commonly needed for digital health careers.
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 practices 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
- We are introducing a fully-funded PhD in 2024, 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 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 enables 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, analyze 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 generates them, methods used for processing and analysis, and how digital data is integrated into clinical decision-making. In particular, you will develop an appreciation of the challenges in handling, storing, and analyzing 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 modeling 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 translating 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 data mining 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.
Admissions
Curriculum
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 program.
Semester 1
The MSc is structured around a mixture of compulsory and optional modules:
- 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
Students will normally be required to complete the following modules unless they have significant experience in statistics and programming:
- Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant to commercial analysis
and one of the following:
- Object-oriented modeling, Design, and Programming: introduces and reinforces object-oriented modeling, design, and implementation to provide a common basis of skills, allowing students to complete programming assignments within other MSc modules. The module assumes a substantial amount of prior programming experience equivalent to having completed an undergraduate degree in Computer Science
- Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience.
Semester 2
- 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. Finally, 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.
Optional
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 visualization and visual analytics
- Machine learning
- Programming principles and practice.
Degree project
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 one of the following:
- A policy report that emphasizes your ability to critically assess digital health policy and make convincing recommendations for policy changes
- A multi-media portfolio that emphasizes your ability to present digital health concepts in exciting and engaging ways
- A written dissertation that emphasizes 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. 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
Teaching format
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
Assessment methods used may include essays, reports, presentations, practical exercises, reflective exercises, and examinations.
Scholarships and Funding
The University of St Andrews is committed to attracting the very best students, regardless of financial circumstances.
The University of St Andrews offers postgraduate scholarships and other financial awards. These may be held in addition to external funding or awards from a government body. These may also cover (fully or partially) tuition fees, maintenance (living costs including accommodation), or both.
Scholarships are available based on academic merit and financial need. There are scholarships available for both home and overseas fee status. The scholarship team recommends reading the terms of each award carefully and applying to a range of funding sources.
Postgraduate scholarships
Postgraduate study is an investment in your intellectual development and career potential. The University of St Andrews provides scholarships to help as many students as possible continue in higher education.
Scholarship availability may depend on your area of study or fee status (for example, whether you are a 'Home' or 'Overseas' student).
Program Tuition Fee
Career Opportunities
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 inquiry, 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 teamwork.
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 through a substantial thesis of original research.
English Language Requirements
Certify your English proficiency with the Duolingo English Test! The DET is a convenient, fast, and affordable online English test accepted by over 4,000 universities (like this one) around the world.