Data Science and Analytics for Health MRes

Course overview

This part-time, two-year course is open to NHS staff only.

From understanding diseases better to improving the appointment process, data science and analytics are integral to the way the healthcare system works in society today.

Our Data Science and Analytics for Health MRes degree provides a comprehensive training in the management, modelling and interpretation of health and healthcare data used by clinical, behavioural and organisational sources.

The course draws on recent advances in information technology, data management, statistical modelling (for description/classification and prediction), machine learning and artificial intelligence. It’s designed to enable you to develop both the technical and applied skills required for addressing real‐world challenges in real‐world health and healthcare contexts.

Studying in our School of Computing gives you access to a whole range of specialist facilities, whilst being taught by academics who are experts in their fields. We’re responsible for producing internationally excellent research and have long-established links with the NHS, in industry and with the Leeds Institute for Data Analytics (LIDA) which put us at the forefront of health data research.

Once you’ve graduated, you’ll be fully equipped with the most up-to-date practices and techniques, alongside the technical skill set you’ll need to pursue an exciting career as a trained Data Scientist.

Why study at Leeds:

• Research produced by the Leeds Institute for Data Analytics and our School’s globally-renowned research conducted right here on campus feeds directly into the course, shaping your learning with the latest thinking.
• Benefit from studying at a university that's partnered with the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.
• Advance your knowledge and skills in key areas of computing including data science and analytics, machine learning and artificial intelligence.
• Build practical real-world NHS experience by conducting a workplace-based project which focuses on applying and evidencing data science competencies within the context of an unsolved healthcare topic, giving you the chance to put your learned technical skills into practice.
• Experience expert theoretical and practical teaching delivered by a programme team made up of academics who specialise in a wide range of computing topics.
• Study in the Sir William Henry Bragg building which provides excellent facilities and teaching spaces for an outstanding student experience.

Course details

This course recognises and utilises recent advances in information technology, data management, statistical modelling (for description/classification, causal inference and prediction), machine learning and artificial intelligence. It intends to equip health data scientists and health data analysts with the skills required to: harness the empirical insights available within large and varied data sources; and apply these to pressing clinical, social and organisational questions within the broad and varied context of health and healthcare services.

This course draws together:

• established expertise in applied data science relevant to the statistical modelling of complex data and the use of machine learning and artificial intelligence to accelerate the application of modelling for insight and discovery through causal inference and prediction.
• key public and private sector partners with extensive experience of managing a range of complex health and healthcare data sources, and harnessing these to inform professional practice, service delivery, public policy and commercialisation.

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.

For more information and a full list of typical modules available on this course, please read Data Science and Analytics for Health MRes Part Time in the course catalogue

Year 1 compulsory modules

Data Science – 15 credits

The aim of the module is for you to understand methods of analysis that allow people to gain insights from complex data. You’ll cover the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

Machine Learning – 15 credits

On completion of this module, you should be able to: list the principal algorithms used in machine learning, and derive their update rules; appreciate the capabilities and limitations of current approaches; evaluate the performance of machine learning algorithms; use existing implementation(s) of machine learning algorithms to explore data sets and build models.

Programming for Data Science – 15 credits

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by online courseware.

Workplace-based Data Science and Analytics Research and Development Project (Short Form) - 105 credits

A distinctive feature of this course is the inclusion of hands‐on data science practice working on an applied and collaborative workplace‐based project in a health and healthcare service. The aim of this module is to support the development of independent and team science practical competencies in applied health data science research and innovation. This is within the context of real-world systems, challenges and opportunities.

You'll be under the supervision of service‐specific specialists and academic experts in the management, analysis and interpretation of health and healthcare data.

The project will offer you the opportunities to:

• apply, test and further refine your skills in data science and analytics
• experience working within established data science teams addressing pressing and pertinent health and healthcare problems
• develop invaluable transferable skills relevant to interdisciplinary team science
• generate analytical tools, empirical findings, and evidence‐based insights with the potential to have tangible impacts on health and healthcare policy and practice.

Year 1 optional modules (selection of typical options shown below)

Deep Learning – 15 credits

This module will equip you with a state-of-the-art understanding of Deep Learning, and highly practical skills and expertise in the construction of AI systems, including those that integrate multiple modalities.

Data Mining and Text Analytics – 15 credits

This module will provide you with an introduction to linguistic theory and terminology. On competition you should be able to: demonstrate an understanding and how to use algorithms and resources for implementing and evaluating text mining and analytics systems; develop solutions using open-source and commercial toolkits; and consider the applications of data mining and text analytics through case studies in information retrieval and extraction.

Business Analytics and Decision Science – 15 credits

This module aims to introduce you to key concepts in business analytics, with a special emphasis on common areas of application. It also explores the links between the behavioural (decision science) perspective on decision support and the management science/business analytics perspective.

Innovation Management in Practice – 15 credits

On this module you’ll expand your knowledge of the concepts and principles studied in other innovation modules. You'll also work on your critical understanding of specific innovation principles, practices, tools and processes needed for the management and development of innovation.

Statistical Learning – 15 credits

Statistical learning is at the core of the modern world. Areas such as: online advertising; automated vehicles; stock market trading; and transport planning all use statistical models to learn from past data and make decisions about the future. Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. It’s how we translate data into knowledge. In this module you'll be introduced to the fundamental concepts of statistical learning and will learn to use several key statistical models widely employed in science and industry.

Foundations of Health Data – 15 credits

This module will introduce you to what, when, how, why and by whom health data is collected, processed and shared in the health domain. The different categories of health data (e.g. prescriptions, procedures, referrals) and dimensions of health (e.g. patients and time) will be covered. You'll then be introduced to some of the key data sources and data flows in the health domain and will consider how the data source can impact data quality and subsequent use.

Human Factors in Health Data Science – 15 credits

Throughout this module, you'll use 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. You'll also explore safety and usability, as well as stakeholder involvement and behaviour change.

Visualisation for Health Data – 15 credits

This module will introduce 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 practical experience of the data visualisation pipeline, from the selection and ‘wrangling’ of health data to the generation of static and interactive visual representations.

Artificial Intelligence and Machine Learning in Health – 15 credits

In this module you’ll be introduced to methods for the training and testing of various machine learning models using healthcare data. On completion of the module, you should be able to run an analysis independently and critique the work of others. Fundamental concepts of machine learning theory will also be covered.

Learning and teaching

Campus‐based blended learning with workplace‐based research project supervision.

Programme team

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.

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

You’ll be assessed using a range of techniques including case studies, technical reports, presentations, in-class tests, assignments and exams. Optional modules may also use alternative assessment methods.

Applying

Entry requirements

A bachelor (or Masters degree) in computer science, science, technology, engineering, mathematics, medicine or a quantitative health discipline.

Either a 1st class degree at bachelor or Masters level or 2:1 (hons) plus (minimum 3 years) first‐hand work‐related experience in one or more quantitative science or healthcare settings.

A-level: AAA including Mathematics or Computing or equivalents.

International

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

English language requirements

IELTS 6.5 overall, with no less than 6.5 in any 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

This course is open to NHS Digital staff only.

Please read our How to Apply page for full details, including application deadlines and what to include with your application.

Application deadlines

Applicants are encouraged to apply as early as possible.

12 September 2025 – UK applicants

Click below to access the University’s online application system and find out more about the application process.

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

English Language

TOEFL iBT – score of 91 overall, with listening and reading components no less than 24, writing component no less than 24 and speaking component no less than 24;

Pearson PTE Academic – score of 64 overall, with no component less than 64.

Important information

Prior to being made an offer, you will be invited to attend an online interview in order to assess your suitability for this course.

As part of your application you will need to include the following information:

• details of the organisation you hope to undertake your extended Workplace-Based Research and Development project (if known) including name, email address and telephone number of your contact
• two references (one academic, one professional)
• contact details in order for us to organise the online interview.

Skills

Applicants must be able to demonstrate an ability to programme through their degree, work experience or supporting statement.

Motivation

For applicants meeting all of the above, motivation will be verified by programme team based on the supporting statement, emails or interview as required.

Admissions policy

University of Leeds Admissions Policy 2025

This course is taught by

School of Computer Science

Contact us

Postgraduate Admissions team

Email: pgcomp@leeds.ac.uk
Telephone:

Fees

UK: £14,750 (Total)

Read more about paying fees and charges.

Part-time fees
Fees for part-time courses are normally calculated based on the number of credits you study in a year compared to the equivalent full-time course. For example, if you study half the course credits in a year, you will pay half the full-time course fees for that year.

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.

Career opportunities

Data science and analytics have become key to streamlining the workings of many businesses – especially in healthcare. The healthcare industry looks after huge data sets so rely on qualified data scientists to provide practical insights and help contribute to improving the way the health system works in general. And this demand is only going to grow.

This MSc will give you the advanced and technical skill set in this field that’s in demand across the healthcare industry – and beyond.

Plus, the University of Leeds is in the top 5 most targeted universities in the UK by graduate recruiters, according to High Fliers’ The Graduate Market in 2024 report.

On completion of this course, you'll be strongly positioned to enter an exciting and rewarding career path in one of at least three main areas:

• as skilled data science researchers in research‐intensive settings (including academia) – with good research funding prospects and substantial potential for societal and economic impacts arising out of the outputs from your applied, workplace‐based health data science projects;
• as health and healthcare data science entrepreneurs – developing business ideas based on the application of your advanced data science skills in extended workplace‐based research projects within the health domain; and
• as key research and development staff within public, private/commercial or voluntary sector organisations – generating and capitalising upon the novel insights and discoveries accessed through the application of advanced data science techniques to rapidly expanding clinical, behavioural and operational data sets.

Careers support

At Leeds, we help you to prepare for your future from day one. We have a wide range of careers resources — including our award-winning Employability Team who are in contact with many employers around the country and advertise placements and jobs. They are also on hand to provide guidance and support, ensuring you are prepared to take your next steps after graduation and get you where you want to be.

• Employability events — we run a full range of events including careers fairs in specialist areas and across broader industries — all with employers who are actively recruiting for roles.
• MyCareer system — on your course and after you graduate, you’ll have access to a dedicated careers portal where you can book appointments with our team, get information on careers and see job vacancies and upcoming events.
• Qualified careers consultants — gain guidance, support and information to help you choose a career path. You’ll have access to 1-2-1 meetings and events to learn how to find employers to target, write your CV and cover letter, research before interviews and brush up on your interview skills.
• Opportunities at Leeds — there are plenty of exciting opportunities offered by our Leeds University Union, including volunteering and over 300 clubs and societies to get involved in.

Find out more about career support.