Health Data Science

Entry requirements

We require at least 2:2, or overseas equivalent, in a quantitative discipline such as

• Mathematics
• Statistics
• Computing
• Engineering
• Physics
  or
• Biomedical science (including epidemiology, biological sciences, or medicine)

Your degree must have had significant statistical and computational elements or health data/medical background.

• Relevant experience in health data science, or a related field, may be accepted in lieu of a degree and should be evidenced within your applications.

Months of entry

January, September

Course content

Our MSc in Health Data Science is an interdisciplinary programme that is designed to prepare you for the constantly evolving landscape of data-driven health care.

The programme is based on a research and practice-driven approach, which enables you to engage with real health data. This approach helps you learn how to solve complex computational and analytical health-related problems that are relevant to healthcare, epidemiology and biomedical research.

The curriculum is designed to bridge disciplines and reflect the collaborative nature of health data science itself, with content covering statistical modelling, machine learning, and data analysis and management.

From early in the programme, you will work with tools and technologies used in the field, such as R, SQL and Python; developing your computational literacy alongside your knowledge of clinical, health-related data. The projects you will engage with are highly practical, and reflect real-world challenges, ranging from data handling to predictive modelling in clinical settings.

This MSc is designed to support a diverse group of graduates and professionals with backgrounds and expertise in quantitative sciences. It is particularly suitable for computer science, statistics, physics, maths or biosciences graduates, who are moving into, or looking to apply their skills in a health-related profession. It is also an ideal option for healthcare professionals interested in improving their data-related skills, to get to the next stage of their career or apply for their first job in this field.

While prior experience in statistical programming is helpful, it is not essential. You should either possess foundational knowledge or be motivated to develop these skills as part of the programme.

Why Study Health Data Science at Swansea University?

At Swansea, you will benefit from having access to:

• World-leading research and facilities in health data
• Relevant and transferable technical skills and software training, thoroughly preparing you for working with, and manipulating, health data
• In-depth insight into how to use and apply advanced statistical and machine learning methods to real-world data
• Teaching that prepares you to be able to analyse and interpret health-related data, and utilising it to help solve healthcare challenges
• A flexible programme that can be studied either full-time or part-time, over 1 or 2 years
• Supervision from one of our many leading experts in health data research, who will guide you through your dissertation project

Your Health Data Science experience

Your experience will be enhanced by access to our state-of-the-art facilities for health data analysis, which are based within our £100million Health Data Science Building, a world-class centre in eHealth and administrative data research, training and development.

Health Data Science is home to our Patient and Population Health and Informatics, and Population Data Science research and projects. Activities within the department have attracted £30million of UK Government funding, making the building one of the main data linkage sites for anonymised health data in the country.

The curriculum on this programme integrates task-based applied learning and self-directed study. Compulsory themes of study cover computer programming, health data modelling, machine learning and data linkage, which are taught through a combination of lectures and hands-on practical, lab sessions. A clear focus is placed on statistical methods and programming tools used in the analysis of health-related data.

Modules are taught in blocks. Most involve one or two full-day sessions per week, while one module in teaching block one is delivered during a single intensive week.