MSc Health Data Science / Course details

Year of entry: 2022

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Course description

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, patients and costs. 

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.

Students from a variety of professional backgrounds will benefit from the course, as the structure of the MSc ensures that you will share this knowledge with each other and learn to work in multidisciplinary teams, rather than in specialist silos.

The course has eight taught units covering key skills for health data science. Seven units are core and there is one optional unit depending on training needs and background. For those studying for an MSc, there is also a 60-credit research project.


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.

Special features

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.

Teaching and learning

The course covers four main areas that bring together technical, modelling and contextual skills to apply 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 CHI . 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.

Coursework and assessment

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 (10,000 to 15,000 words) that focuses on a specific aspect of health data science.

Course unit list

The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.

TitleCodeCredit ratingMandatory/optional
Principles of Digital Biology COMP60532 15 Mandatory
Introduction to Health Informatics COMP60542 15 Mandatory
Modern Information Engineering IIDS61311 15 Mandatory
Fundamental Mathematics & Statistics for Health Data IIDS67631 15 Mandatory
Statistical Modelling and Inference for Health IIDS67641 15 Mandatory
Introduction to Health Data Science IIDS67681 15 Mandatory
Principles of Digital Biology COMP60532 15 Optional
Introduction to Health Informatics COMP60542 15 Optional
Decision Support Systems IIDS61402 15 Optional
Decision Support Systems IIDS61412 15 Optional
Mathematical Computing for Medical Imaging IIDS67462 15 Optional
Tutorials in Advanced Statistics IIDS67612 15 Optional
Machine Learning and Advanced Data Methods IIDS67682 15 Optional
Design and Analysis of Randomised Controlled Trials IIDS68112 15 Optional
Multi-omics for Healthcare IIDS68122 15 Optional
Displaying 10 of 15 course units

Course collaborators

Our course is embedded in the rich ecosystem of world-class research in the field across the University, and is at the forefront of driving the professionalisation and skills agenda.

The MSc is hosted by the Centre of Health Informatics (CHI), which has built an internationally recognised multi- and trans-disciplinary research base attracting over £50 million of funding from research councils, the EU and industry. 

CHI was originally founded in 2012 through funding from the Medical Research Council. It is part of the Health Data Research UK Northern Partnership and is leading research in the Learning Healthcare System in which data analysis is used to directly improve health care (including improvements in care for elderly and antibiotic prescribing). CHI previously led the Department of Health funded programme Connected Health Cities which piloted the Learning Health System. CHI is also conducting key research projects such as the EPSRC Wearable Clinic and innovative projects such as ClinTouch, a mobile early warning system. CHI has experience of delivering research-led innovative education that remains relevant to the needs of the market.

This centre runs a programme of research and education that brings experts together from a wide range of academic, NHS and industrial partners. In particular, it has a UK-wide remit to professionalise the field.


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.

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service .

CPD opportunities

If you cannot commit to a full postgraduate course, or need to focus on a particular skill set, we offer units from this MSc for continuing professional development (CPD).

You can submit assessments to receive university credits, and then apply for admission to the full postgraduate course.

It is expected that CPD students will take these on an ad hoc basis, but will have the opportunity to work towards an exit award within five years of sitting the first unit. This will be in accordance with AP(E)L regulations and will allow students to be able to transfer credits from other universities that deliver similarly focused courses (eg those belonging to Health Data Research UK). 

The units do not need to be taken in a specific order (except where pre-requisite units have been specified). However, you will be required to demonstrate that they have sufficient background knowledge to sit the individual unit requested.