Course summary
Our MSc Health Data Science course aims to create a new breed of scientist who can understand the healthcare sector and medicine, how data is collected and analysed, and how this can be communicated to influence various stakeholders. The current model of healthcare delivery in the UK is subject to unprecedented challenges. An ageing population, the impact of lifestyle factors and increasing costs mean that the existing approaches may become unsustainable. This, coupled with a drive towards personalised medicine, presents an opportunity for a step change in healthcare delivery. To do this, we need to make best use of the health data we collect and create a better understanding of the relationship between treatments, outcomes and patients. This MSc promotes the need for translational thinking to provide the knowledge, skills and understanding that will be applied across new challenges within healthcare delivery. A multidisciplinary approach to health data science is the focus of this course, including students from a variety of professional backgrounds. The structure of the MSc ensures that you will share knowledge with each other and learn to work in multidisciplinary teams, rather than in specialist silos. You will be taught by world-leading professionals and academics in the field of health data science, statistics, machine learning, information engineering, omics and digital biology, and digital transformation of the healthcare system.You will also mix with students from a range of disciplines from all over the world. The current structure of the course (subject to change) involves four mandatory units in the first semester including: Introduction to Health Data Science Modern Information Engineering Fundamentals of Statistics and Mathematics Statistical Modelling and Inference in Health. The second semester offers nine optional units, of which four need to be completed (for those studying for a MSc). The optional units include:
- Statistics for Randomised Trials
- Tutorials in Advanced Statistics
- Principles of Digital Biology
- Healthcare Multi-Omics
- Machine Learning and Advanced Data Methods
- Computational Imaging (including machine learning techniques)
- Clinical Decision Support
- Digital Transformation
- Introduction to Health Informatics
Entry requirements
We require an honours degree (minimum Upper Second) or overseas equivalent in: Mathematics, Statistics, Computer Science Physical Science or Biomedical Science (including Epidemiology, Biological Sciences or Medicine/Nursing). Your degree must have had significant statistical and computational elements and be from a recognised institution or an approved and relevant postgraduate qualification (minimum postgraduate diploma or equivalent). We may also accept the equivalent of previous advanced study, research and/or relevant professional experience that the University accepts as qualifying the candidate for entry. In the case of non-UK applicants, the institution certifying advanced study must be recognised and approved by the University. You may be accepted for entry to the postgraduate diploma track with a Lower Second degree or under subject to interview, and be moved to the master's track on achieving suitable results in the taught part of the course.
Fees and funding
Tuition fees
No fee information has been provided for this course
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
Fees for entry in 2024 have not yet been set.
Provider information
University of Manchester
Oxford Road
Manchester
M13 9PL