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 provide solutions.
Our 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, our course is designed for both recent graduates and professionals looking to advance their careers. The course will develop your knowledge and understanding of health informatics, health data science techniques, and 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. With us you will 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 the School of 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.
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, and are involved in a variety of ongoing research projects to improve and redesign health services to better serve patients.
More information
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 the NHS West Yorkshire Integrated Care Board, deliver engaging talks to the course. Leeds is also home to a thriving digital economy, including leading healthcare technology providers TPP (SystmOne) and EMIS.
Explore Leeds
Discover our campus, facilities and accommodation with our 360 virtual experience platform.
With immersive 360 experiences, image galleries, virtual tours, videos and day-in-the-life content, you can take a personalised, interactive tour of campus and explore the University's academic, social and residential environments from anywhere in the world.
You can get a feel for what it's really like to live, learn and belong at the University of Leeds – all from the comfort of home.
Course details and modules
You will study modules totalling 120 credits. These are made up of six core (compulsory) taught modules, 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
- AI and Machine Learning
A choice of optional modules allows you to tailor your study to areas of interest.
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.
Year 1 compulsory modules
| Module Name | Credits |
|---|---|
| Statistics and Modelling for Health Sciences | 15 |
| Foundations of Health Data | 15 |
| Human Factors in Health Data Science | 15 |
| Informatics and Data Science in Health Care and Research | 15 |
| Law, Ethics and Governance for Health Data Science | 15 |
| Artificial Intelligence and Machine Learning in Health | 15 |
Year 1 optional modules (selection of typical options shown below)
| Module Name | Credits |
|---|---|
| Applied Qualitative Health Research | 15 |
| Introduction to Health Economics | 15 |
| Key Issues in International Health | 15 |
| Monitoring and Evaluation of Health Programmes | 15 |
| Visualisation for Health Data | 15 |
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.
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.
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.
Applied Qualitative Health Research (15 credits)
This module will provide you with a thorough understanding of the role and application of qualitative approaches in applied health and social care research. Through interactive teaching sessions, you'll engage in sessions that cover the principles and design of qualitative research, as well as the practicalities of conducting a qualitative study. Topics include: designing a qualitative research question, study design, ethical considerations, sampling, data collection methods (including interviews, observation, and visual methods), analysing qualitative data, and critical appraisal. You'll have the opportunity to design a qualitative study with guidance of our experienced tutors and to engage with qualitative data.
Learning and teaching
Our course is taught through a variety of lectures, practical classes, tutorials, and seminars. 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 summatively through written work. You will develop your knowledge and skills through diverse assessment approaches, such as critical appraisal, evaluation of real-world scenarios, reviewing data sources for research, statistical analysis, data coding and visualisations of data, machine learning and a review of digital tools used in clinical practice.
The majority of our modules offer a formative presentation opportunity (group or individual; poster or oral), where you will receive verbal feedback from the module lead and from peers. All modules offer an opportunity to submit a written piece of work for detailed individual written formative feedback.
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 with quantitative component. Clinical subjects (MBChB / MBBS), Nursing, Pharmacy, Dentistry, other allied health professions.
Subjects with explicit maths elements (such as Maths, Statistics, Computing / Computer Science, Engineering, Physics, Economics, Biomedical Science)
Health Informatics / Public Health subjects (such as Health Information Management, Population Studies, Public Health)
Other subjects with numerate or quantitative component (such as Psychology, Geography, Nutrition and Social Sciences)
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.
Applications are considered on the basis of the applicant’s qualifications and experience, and their relevance to this programme.
We accept a range of international equivalent qualifications. For information contact the Admissions Team.
English language requirements
IELTS 6.5 overall, with no less than 6.0 in each component. For other English qualifications, read English language equivalent qualifications.
How to apply
Application deadline is 30 July 2027 for all applicants.
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.
Please see our How to Apply page for information about application deadlines.
Documents you must upload with your application:
- Your degree certificate (if you have graduated) and your official degree transcript. Your degree transcript must be signed and stamped. If you have not yet graduated, please provide your official interim transcript, showing the modules you are currently studying and the marks you have achieved to date.
- A CV which covers all relevant work experience in detail will be requested (this is for applicants who do not have a relevant first degree).
- A Supporting Statement is required (maximum 300 words) covering three question prompts.
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.
Read about visas, immigration and other information in International students. We recommend that international students apply as early as possible to ensure that they have time to apply for their visa.
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 and our 10 week online pre-sessional.
You can also study pre-sessionals for longer periods – read about our postgraduate pre-sessional English courses.
Admissions policy
University of Leeds Admissions Policy 2027
Contact us
School of Medicine Postgraduate Admissions
Email: pgmed-admissions@leeds.ac.uk
Fees
UK: £8,800 (Total)
International: £20,666 (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
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 PGDip in Health Informatics with Data Science fully addresses the key skill areas identified in Annex C of the NIHR Academy’s guidance document here.
Richard Jones Early Career Clinician Scholarship
Up to two scholarships are provided each year to early career clinicians looking for a career in a health data or digital health related area. Successful candidates receive a fee remission of up to £6,000. Eligible offer holders are shortlisted in May, and successful applicants will be notified.
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 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.
Top 10 most targeted for 10+ years
by the UK's leading employers
Careers support
At Leeds, we help you to prepare for your future from day one.
Our Careers Service has the global expertise and sector-spanning industry partnerships that, combined, go well beyond simply helping you get a job. They provide you with the resources you need to upskill and achieve the future you aspire towards – even in the face of lightning-paced change.
- Dedicated Employability Team – meet with our qualified careers consultants and specialist employability and placements officers, on hand to help you choose the right path and develop the skills to get there. They can support with CV and cover letter writing, LinkedIn profile building, mock interviews and navigating AI: get confident with tailored advice and techniques.
- Employability and networking events – get involved in a full range of events, including careers fairs and specialist industry talks with employers who are actively recruiting. You’ll also benefit from mentoring and a supportive careers community, helping you make connections, build your network, and shape your journey as a future professional.
- Skills training – we embed transferable skills training in all our programmes to support your transition to the workplace. Gain expertise employers value, such as communication, problem-solving and research and data analysis.
- MyCareer system – access a dedicated portal where you can book careers appointments, view helpful resources and browse vacancies and events. Access digital tools, including LinkedIn support, video interview preparation, plus global job market and visa guidance. We also offer pre‑arrival support to help you prepare for career planning in the UK and make the most of your time with us.
- Exclusive opportunities – bring your enterprise to market with our award-winning business advice service, Spark. Apply for vacancies only available to our students. And hone your skills further with Leeds University Union, home to volunteering opportunities and over 300 clubs and societies.
Explore more about your employability opportunities at the University of Leeds.