MSc Health Data Science / Course details

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
• Programming for Health Data
• Fundamentals of Mathematics and Statistics
• Statistical Modelling and Inference in Health.

The second semester offers ten 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
• Digital Epidemology

There is also a 60-credit research project, which involves writing a dissertation.

PhD with integrated master's

If you're planning to undertake a PhD after your master's, our Integrated PhD programme will enable you to combine your postgraduate taught course with a related PhD project in biology, medicine or health.

You can also visit this page for examples of projects related to integrated master's courses.

This course will allow you to:

• gain key background knowledge and an understanding of the healthcare system, from the treatment of individuals to the wider population;
• gain an understanding of the governance structures and frameworks used when working with health data and in the healthcare sector;
• experience key technical skills and software for working with and manipulating health data;
• understand the breadth and depth of application methods and the potential uses of health data;
• comprehend key concepts and distinctions of the disciplines that need to be synthesised for effective health data science;
• appreciate the role of the health data scientist and how they fit into the wider healthcare landscape;
• understand the importance of patient-focused delivery and outcomes;
• develop the in-depth knowledge, understanding and analytical skills needed to work with health data effectively to improve healthcare delivery;
• develop a systematic and critical understanding of relevant knowledge, theoretical frameworks and analytical skills to demonstrate a critical understanding of the challenges and issues arising from heterogeneous data at volume and scale, and turn them into insight for healthcare delivery, research and innovation;
• apply practical understanding and skills to problems in healthcare;
• work in a multi-disciplinary community and communicate specialist knowledge of how to use health data to a diverse community;
• evaluate the effectiveness of techniques and methods in relation to health challenges and the issues addressed;
• extend your knowledge, understanding and ability to contribute to the advancement of healthcare delivery knowledge, research or practice through the systematic, in-depth exploration of a specific area of practice and/or research.

Research project options

MSc students will have an opportunity to conduct their research project in other settings such as the NHS and the biopharmaceutical industry, as well as academia.

The course brings together technical, modelling and contextual skills and applies these to real world problems when harnessing the potential of health data.

In each of the units that deliver the key skills, both the importance of the patient and the governance surrounding working in the healthcare environment (especially structures around information governance) is embedded throughout.

Each unit will use case studies provided by existing work and research at The University of Manchester. The course will focus on large and complex health datasets (often routinely collected) in environments that safeguard patient confidentiality.

The course will encourage intellectual curiosity, creativity, and critical thinking, providing transferable skills for lifelong learning and research and cultivation of reflective practice.

Through the development of these innovation, critical, evaluative, analytical, technical, problem solving and professional skills, you will be able to conduct impactful work and advance healthcare delivery.

We see learning and teaching as collaborative knowledge construction, which recognises the contribution of all stakeholders (academic staff, service users and carers, and students). This is demonstrated in the course through contributions made by these stakeholders through case studies, examples, invited seminars and participation in group work.

A variety of teaching methods will be used within the constraints of the method of delivery. The course will be student centred and will be delivered from the outset using a combination of face-to-face, distance learning and blended learning units.

A range of assessments are used within each course unit and across the course as a whole.

All assessments require you to integrate knowledge and understanding, and to apply this to case studies and the outcomes of each unit.

Assessment will occur in a variety of forms including (but not exclusively) essays, case studies, assessed seminar/tutorial presentations and literature reviews.

Written assignments and presentations have a formative role in providing feedback (particularly in the early stages of course units) as well as contributing to summative assessment.

Online quizzes provide a useful method of regular testing, ensuring that you actively engage with the taught material.

The assessment of tutorials contains an element of self and peer evaluation, so you can learn the skills associated with the effective management of and participation in collaborative activity.

The course also places an emphasis on group work, as this a vital skill for professionals operating in a multidisciplinary area such as health data science, and this is shown in the teaching methods and assignments.

Each unit has a different emphasis on the group work assessment based on the nature of the material being covered, how they are to apply the knowledge and the work they are to complete.

The dissertation for the MSc requires you to undertake an extended written piece of work (8,000 to 10,000 words) that focuses on a specific aspect of health data science.

You will have access to CHI's state-of-the-art research and teaching facilities.

In addition, you will have full access to the University's IT and library facilities . This will include Blackboard and other e-learning facilities.

Each student will have an identified personal tutor who can provide advice and assistance throughout the course. During the research project, you will be in regular contact with your research supervisor.