MSc Health Data Science

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.

For fees and funding options, please visit website to find out more

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 your current career stage.

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, focussing 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

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). 

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.

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 (PGDip). 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.