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Health Data Science at University of St Andrews - UCAS

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2024 - 2025
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Course summary

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.
  • Integrated training programme connects your academic learning with the development of personal and professional competencies.
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.

Modules

Semester 1: The MSc is structured around a mixture of compulsory and optional modules: Digital Health Principles: explores the theoretical underpinnings of 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 for commercial analysis. and one of the following: Object-Oriented Modelling, Design and Programming: introduces and reinforces object-oriented modelling, design and implementation to provide a common basis of skills, allowing students to complete programming assignments within other MSc modules. Programming Principles and Practice: introduces computational thinking and problem-solving skills to students who have no or little previous programming experience. Semester 2: Digital Health Practice: looks at the practical applications of 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 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 one of the following: a policy report that emphasises your ability to critically assess digital health policy and make convincing recommendations for policy changes a multi-media portfolio that emphasises your ability present digital health concepts in exciting and engaging ways 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. 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.

Assessment method

Assessment methods used may include essays, reports, presentations, practical exercises, reflective exercises, and examinations.

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. - 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. If English is not your first language, you may need to provide an English language test score to evidence your English language ability. See the University's approved English language tests and scores for this course.

English language requirements

For the current English Language requirements please visit the English language requirements for postgraduate students on the University of St Andrews website.

English language requirements for postgraduate students

https://www.st-andrews.ac.uk/subjects/entry/language-requirements/postgraduate/

Fees and funding

Tuition fees

No fee information has been provided for this course

Additional fee information

For the most current information on course fees please visit https://www.st-andrews.ac.uk/study/fees-and-funding/postgraduate/taught/.

Sponsorship information

The University of St Andrews is committed to attracting the very best students, regardless of financial circumstances. Find out more about the scholarships (https://www.st-andrews.ac.uk/study/fees-and-funding/scholarships/) and postgraduate loans available (https://www.st-andrews.ac.uk/study/fees-and-funding/postgraduate/loans/).

Health Data Science at University of St Andrews - UCAS