Health Data Science (MSc) 2025 entry

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.

• Semester 1

  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.

• 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. 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

  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

• 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 a written dissertation that emphasises your ability to plan and execute academically rigorous research.