Health Informatics with Data Science MSc

Course overview

Demand for individuals qualified in health informatics and data science is on the rise. The demand for healthcare services is currently exceeding supply worldwide, and health providers and leading multinationals are heavily investing in information technology to generate solutions.

This forward-thinking course provides insightful training into how modern applications of data and informatics in health management and planning can both use and generate evidence to influence policy and practice.

Created by experienced academics and professionals, the course is designed for both recent graduates and professionals looking to advance their careers. It will develop your knowledge and understanding of health informatics, health data science techniques and the real-world application of research methods – skills that are highly sought after by employers.

We combine health, data and social science expertise with a research focus to develop knowledge, skills and awareness of sources and uses of evidence in healthcare.

Developing research capacity in health informatics and data science is a priority area internationally. Staff contribute expertise to the Research Methods Incubator of the UK National Institute for Health and Social Care Research (NIHR) Academy. You’ll be actively involved in listening to and informing the informatics and data science agenda for health.

Leading expertise

• Learn from experts in health informatics and data science, including in machine learning and AI (MSc and PGDip only).
• Multidisciplinary research expertise is embedded within the curriculum.
• Learn from a curriculum informed by the latest understanding and practice, with academic teams in the Faculty of Medicine and Health, Institute of Health Sciences, plus strong collaborations with Computer Science.

Flexible learning

• Both full and part-time options are available so you can apply your learning as you complete the course.
• Create a bespoke learning journey and choose optional modules to reflect your own interests.
• Tailor your degree to your specific career ambitions, or the needs of your professional sector, including a choice of research projects (MSc only).

To prepare you for these unique challenges ahead, we’ll support you to:

• explore a new and innovative approach to health informatics, statistics and computer science that focuses on patient benefit and evidence-based, high-quality healthcare
• address human and technical challenges in healthcare and health data science
• develop your knowledge of fundamental statistical, social and governance concepts
• study a multidisciplinary approach to health informatics.

Through years of teaching and research, we’ve developed a strong reputation, both nationally and internationally, in health informatics and data science. Our staff are actively engaged in delivering education and skills training; they’re involved in a variety of ongoing research projects to improve and redesign health services to better serve patients.

More information

You’ll benefit from our excellent location, too. The Leeds Teaching Hospitals NHS Trust is the largest UK hospital Trust. Leeds is also the headquarters for many Department of Health and Social Care organisations, including NHS England. Guest speakers from regional and national organisations, such as the Office of the National Data Guardian and the NHS West Yorkshire Integrated Care Board, contribute engaging talks to the course. Leeds is also home to a thriving digital economy, including leading healthcare technology providers TPP (SystmOne) and EMIS.

Course details

You will study modules totalling 180 credits. These are made up of six core (compulsory) taught modules and a research project, plus two optional modules from a range offered in Health Informatics, Data Science or Health Sciences.

Key topics relating to health data include:

• Informatics and Data Science
• Foundations of Health Data
• Statistics and Modelling
• Human Factors
• Law, Ethics and Governance
• Machine Learning
• AI

A choice of optional modules allows you to tailor your study to areas of interest. The research project will be your opportunity to apply your learning to practice, to work with a supervisor and customise a project within an area that is relevant to your own personal and professional development. This is an opportunity to demonstrate focused expertise to transform your career outlook.

Course structure

The list shown below represents typical modules/components studied and may change from time to time. Read more in our terms and conditions.

Compulsory modules

Statistics and Modelling for Health Sciences (15 credits) – This module introduces you to statistical testing, generalised linear models (GLMs) and survival models, which are the foundation for analysing observational healthcare data. By the end of the course, you'll be able to model various healthcare outcomes of interest on real-life datasets including 30-day mortality, treatment costs, length of stay in hospital, from NHS digital etc. The module will also convey best practice in model evaluation and validation, based on the TRIPOD and STAR-D guidelines for reporting of statistical models in medical journals.

Foundations of Health Data (15 credits) – This module explores what, when, how, why and by whom health data is collected, processed and shared in the health domain. The different categories of health data (eg prescriptions, procedures, referrals) and dimensions of health (eg patients and time) will be described. You'll be introduced to some of the key data sources and data flows in the health domain and will consider how the provenance of data can impact data quality and subsequent usage. Data standards will be described as a mechanism to achieve syntactic and semantic interoperability in the health domain.

Informatics and Data Science in Health Care and Research (15 credits) – You'll be introduced to the central supporting role of Health Informatics and Health Data Science in the broad and complex activities involved in delivery quality evidence driven health care. This draws on the evidence base and the research methodologies supporting innovation and research.

Law, Ethics and Governance for Health Data Science (15 credits) – This module introduces you to the legal, ethical and governance frameworks that are applicable to health data science. You'll also be introduced to technical and organisational safeguards that can be used in health data science projects. You'll develop the ability to analyse health data science projects with respect to legal, ethical and governance implications and will be encouraged to consider some of the key legal, ethical and governance challenges posed by health data science.

Human Factors in Health Data Science (15 credits) – Behind any dataset and using any digital health system, are people. They’re responsible for designing systems, for entering data, for interpreting it and acting on the information. This module uses concepts and research from a range of disciplines to show why data is never just numbers and that health data science needs to be about respecting limitations as well as exploiting opportunities. The module will explore safety and usability, as well as stakeholder involvement and behaviour change.

Artificial Intelligence and Machine Learning in Health (15 credits) – This module will introduce you to a variety of different machine learning algorithms for supervised and unsupervised learning problems. These include random forest, support vector machines, k-means clustering, and neural networks with use-cases identified from across the healthcare domain. You'll also be introduced to techniques for feature selection, dimensionality reduction, and in avoiding overfitting. This builds upon knowledge gained in the core module on statistical modelling. By the end of the module, you'll be familiar with a variety of alternative approaches to traditional statistical modelling and will have gained experience in using them within Python.

Research Project (60 credits) – This module enables you to select, refine and undertake a research project in the health domain, relevant to your parent course. You can choose from a range of project topics, then devise a research question and appropriate study design with support from a supervisor. The methods chosen should enable you to demonstrate independent application of knowledge, skills and techniques acquired during the taught elements of your course, by carrying out and reporting on a piece of health-related research. All projects will include a review of the literature; for some, a systematic or scoping review may make up the entire project, for others a literature review may be combined with additional data analysis work.

Optional modules

Introduction to Health Economics (15 credits) – High income countries spend a considerable proportion of their GDP on health care services and technologies. This module considers how health care interventions can be assessed using economic tools to aid the decision making of healthcare agencies and improve the efficiency of health care systems. The module provides you with a grounding in the role and application of economics in health and health care. The application of the economic concepts and theory within the module will provide students with a greater understanding of the challenges facing the health sector today and how they may be both explained and addressed. Topics include health care markets, the role of government, health financing; equity in health (care); and financing and distribution of health care.

Key Issues in International Health (15 credits) – This module will provide a foundation and a vocabulary of ideas and concepts, an understanding of the key issues in international health. You'll develop an understanding of the key players in international health, and of the historical developments, current priorities, and emerging issues, and how these shape international health practice.

Monitoring and Evaluation of Health Programmes (15 credits) – This module explores a critical yet under-valued component of successful health programmes, monitoring and evaluation. You’ll learn about a logical framework for monitoring and evaluation of health programmes.  This will involve applying theory of health planning and management to a specific case study and assess the processes of: setting objectives; defining stakeholders;  exploring criteria for monitoring and evaluation; defining indicators to measure performance; collecting and analysing data; dissemination of information; and awareness of gender and poverty equity when planning for monitoring and evaluation of health programmes.

Health Promotion (15 credits) – You'll learn about the dimensions of health promotion, including its various definitions and history. You'll examine the social determinants of health and influences on health behaviours; approaches and models of health promotion, including theories of behaviour change and empowerment models.

Visualisation for Health Data (15 credits) – This module introduces you to visualisation as technique for communication of and interaction with health data. You'll learn the key principles of data visualisation and gain the knowledge required to determine appropriate visualisations for different communication and interaction scenarios in the health domain.You'll gain familiarity with and practical experience of the data visualisation pipeline, from the selection and ‘wrangling’ of health data to the generation of static and interactive visual representations.

Learning and teaching

Our course is taught through a variety of lectures, practical classes, tutorials, seminars and supervised research projects. We supplement face-to-face classes with extensive use of our virtual learning environment, meaning that materials will be available to support your studies at your own pace and in your own time.

In addition to group learning, you’ll also be able to use University facilities for independent study. These include computing facilities and four campus libraries as well as access to an extensive collection of online journals.

On this course you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.

Assessment

The modules are assessed by a variety of methods including essays, reports and presentations. Some of the modules involve planning and executing analysis and validation of modelling using real-world health datasets.

Your results for every module contribute to your final degree classification and you must pass all compulsory and optional modules for course progression or award.

Applying

Entry requirements

A 2:1 in a relevant undergraduate degree e.g. Social Sciences, STEMM, Nursing (or equivalent) OR previous work experience (minimum 2 years) of handling and/or analysing health data.

This is an academically rigorous course. Applicants with other qualifications may be accepted if they can demonstrate suitable professional experience. Contact the admissions office if you are unsure of your eligibility.

We accept a range of international equivalent qualifications. For information contact the Admissions Team.

The course is also available as an intercalated MSc programme to students who have completed three years of a medical degree and are ranked in the top 50% of their year of study.

English language requirements

IELTS 7.0 overall, with no less than 6.5 in each component. For other English qualifications, read English language equivalent qualifications.

Improve your English

International students who do not meet the English language requirements for this programme may be able to study our postgraduate pre-sessional English course, to help improve your English language level.

This pre-sessional course is designed with a progression route to your degree programme and you’ll learn academic English in the context of your subject area. To find out more, read Language for Science (6 weeks) and Language for Science: General Science (10 weeks). 

We also offer online pre-sessionals alongside our on-campus pre-sessionals. Find out more about our six week online pre-sessional.

You can also study pre-sessionals for longer periods – read about our postgraduate pre-sessional English courses.

How to apply

Application deadline is 31 July 2025 for all applicants.

Please note that this is a very popular programme. If we receive a significant number of applications, and we are unable to process applications within our 6-week turnaround time, we may have to temporarily suspend the receipt of new applications until we are able to meet our turnaround target.

The ‘Apply’ link at the top of this page takes you to information on applying for taught programmes and to the University's online application system.

If you're unsure about the application process, contact the admissions team for help.

Documents and information you’ll need:

• Evidence of work experience in supporting statement or CV as specified in entry requirements, if no relevant first degree.
• References not required as standard, although the programme leader may request a targeted reference on a case basis, especially in instances of non-standard entry
• If not already present, a CV which covers all relevant work experience in detail will be requested for non-standard applications.
• A Supporting Statement is required, covering three question prompts which must be addressed

Applicants are asked to answer the following questions in their Supporting Statement:

1) How have your previous studies and/or work experience prepared you for this course?

2) What knowledge/skills do you expect to acquire from this course?

3) What are your expectations from this course in relation to your future career?

Applications are considered on the basis of the applicant’s qualifications and experience.

Applications may close before the deadline date if numbers accepted reach capacity.

Part-time variant

The part-time variant of this programme is not suitable for international applicants who require a student visa. International applicants who do not require a student visa may be able to access the part-time variant of this programme by special arrangement. Please contact admissions for further information.

Admissions policy

School of Medicine Taught Postgraduate Policy 2024

This course is taught by

School of Medicine

Contact us

School of Medicine Postgraduate Admissions

Email: pgmed-admissions@leeds.ac.uk
Telephone:

Fees

UK: £12,750 (Total)

International: £29,250 (Total)

Additional cost information

There may be additional costs related to your course or programme of study, or related to being a student at the University of Leeds. Read more on our living costs and budgeting page.

Scholarships and financial support

If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There may be help for students in the form of loans and non-repayable grants from the University and from the government.  Find out more at Masters funding overview.

Studying in the School of Medicine at Leeds is an amazing opportunity, but we know that the cost can be difficult for many people to meet. If you are keen to join us, a range of funding opportunities are available.

DSE Award

Developing research capacity in Health Data Science (HDS) is a strategic priority for the UK National Institute for Health and Care Research (NIHR) Academy.

Their DSE Award is “a post-doctoral level funding opportunity aimed at supporting early to mid-career researchers in gaining specific skills and experience to underpin the next phase of their research career.”

It is open to applicants from clinical and non-clinical backgrounds and can be used to fund MSc-level modules or full courses in HDS. The Leeds MSc in Health Informatics with Data Science fully addresses the key skill areas identified in Annex C of the NIHR Academy’s guidance document here.

Career opportunities

97% of our recent Health Informatics with Data Science graduates feel they've taken meaningful next steps since university.

This exciting course provides superb training for:

• Graduates looking to specialise in health informatics or health data science.
• Health service employees seeking to enhance their careers by gaining skills in data science.

Our graduates have gone on to have successful cross-industry careers in health management, analytics or informatics, with some founding businesses in digital health and others pursuing research degrees. Our graduates are employed in a wide range of roles at various levels of seniority, in health, industry, government and NGOs.