Discover how to collect, analyse, interpret and present health data on this MSc. You’ll combine this knowledge with the fundamentals of computer science. Using statistical analysis, data visualisation and digital technology, you’ll learn how to identify data-driven enhancements to health care interventions and produce a significant piece of health data science research.
Introduction
Offering specialist training for current and aspiring health data scientists, this MSc combines research-focused teaching, training and development in an emerging discipline.
Whether you’re an experienced professional, recent graduate or intercalating medical student, you’ll benefit from our collaborative team-based approach. We’ll tackle important health research questions and work with new forms of health data, you’ll discover how health data science can enhance our understanding of disease and health care.
You’ll receive a comprehensive overview of statistical concepts and explore the role of databases in modern information systems. A combination of theory and practice will prepare you for analysing, manipulating and interpreting the vast amounts of data generated in health care settings.
We’ll also reveal the exciting potential of digital technology for enhancing health care interventions. This includes focusing on actionable analytics, thinking about how to transform data from information into actions that drive real-world improvements in health care settings. Further specialisation in advanced biostatistics, artificial intelligence and data mining is possible.
The culmination of the MSc is a significant research project that enables you to make an original contribution to knowledge in health data science.
This programme is supported by Health Data Research UK – the national institute for health data science.
This MSc has strong links to Civic Health Innovation Labs (CHIL), which has built an internationally recognised, multi and trans-disciplinary research centre tackling global health challenges with civic data and technology. CHIL provides dissertation research projects for students, focusing on areas such as healthcare data analytics, digital health solutions, public health informatics, and the application of technology in community health initiatives. These projects offer students the opportunity to work on cutting-edge research, contributing to meaningful advancements in global health.
The programme opens up a multitude of career opportunities globally, including in the health sector, industry, and academia. In the UK alone, demand for data scientists and data engineers has more than tripled over the past years, increasing by 231%, which translates to approximately 52,000 new jobs.
Who is this course for?
This master’s is suitable for you if you have a quantitative background (for example, a background in mathematics, statistics, computer science, physical science, biomedical science including epidemiology, biological sciences, or medicine*) and want to analyse and address health care problems using data.
Plus, if it suits you better, you can study some of the course modules as standalone CPD (Continuing Professional Development) modules. For more information contact: hdsseo@liverpool.ac.uk
*Please note these are examples only and are not the exhaustive list of backgrounds we accept. Please see our entry requirements for full details.
What you'll learn
How to use health data to better understand disease and improve care.
The benefits and challenges of applying data science to real-world health problems.
Key statistical concepts, including variability, sampling and statistical inference.
How to collect, analyse, interpret and present data.
How to manipulate and evaluate health data sources.
The use of databases in modern information systems.
Fundamental concepts of computer science.
Effective communication and teamwork skills.
The role of digital technology in improving health care interventions.
How to produce a significant piece of health data science research.
Course content
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
Semester one
Compulsory modules
Introduction to Health Data Science (DASC501)
Credits: 15 /
Semester: semester 1
Using health data for research can help us to better understand the causes, prevalence, symptoms, and treatment of disease, as well as understanding how to improve health care systems. Health data science can improve our knowledge of health and care, and is an emerging discipline, arising at the intersection of statistics, computer science, and health. The health data science team can generate data-driven solutions through comprehension of complex real-world health problems, employing critical thinking and analytics to derive knowledge from data. This module will provide an understanding of the potential benefits and challenges in the application of data science to healthcare. Students will benefit from research-connected teaching with academic staff, and have opportunities for active learning including communication skills. The module will be taught via formal lectures, seminars from guest speakers, and practical communication sessions. Learning will be assessed via a coursework and a practical assessment.
Statistics for Health Research (DASC502)
Credits: 15 /
Semester: semester 1
Statistics is the science concerned with the collection, analysis, interpretation and presentation of data to extract knowledge. Understanding key statistical concepts is fundamental to the health data scientist. In this module students will be introduced to the concepts of variability and sampling and the different paradigms for statistical inference. The essential skills of reading data, structuring data, conducting statistical analyses and the importance of data visualisation will be covered to gain an in-depth knowledge and understanding of statistical methods used in the analysis and presentation of health data. Students will benefit from research-connected teaching with academic staff, and have opportunities for active learning. The module will be taught via formal lectures, seminars from guest speakers, and computer practical sessions. Learning will be assessed via a poster presentation and a practical assessment.
Using Routine Data for Public Health (DASC503)
Credits: 15 /
Semester: semester 1
The volume of data generated by modern healthcare and public health systems is immense. Unfortunately, this data is often disconnected, carries biases, and is encoded with diverse disease ontology frameworks. However, the opportunity to transform this reality is within our grasp. We can forge a more interconnected and fair health system through collaboration and collective effort. This module offers research-focused teaching, empowering students to effect change and emerge as leaders in the field. The curriculum comprises formal lectures, seminars from guest speakers, and hands-on computer lab sessions, ensuring students are well-prepared for the challenges ahead. Through a practical evaluation and a pre-recorded oral poster presentation, students will be equipped to make a tangible impact on real-world healthcare and public health challenges. Learning will be assessed via a practical assessment, and a poster and pre-recorded oral presentations.
Data and Engineering for Health Research (DASC509)
Credits: 15 /
Semester: semester 1
This cutting-edge module is designed to equip students with the essential skills to thrive in the rapidly evolving field of health data science. The module integrates modern data and engineering techniques mastering the art of efficient data querying and management within relational database systems (SQL), and programming in Python and R to clean and preprocess data addressing challenges such as missing values and duplicates, as well as how to use of version control (GiT) to ensure the reproducibility and traceability of data-related projects. The module is structured to provide a comprehensive understanding of data handling techniques, ensuring students are well-prepared for the challenges in the health data science workflow. Assessment for the module is coursework based, consisting of two assessments designed to address authentic data analysis problems encountered by health data scientists.
Dissertation (DASC500)
Credits: 60 /
Semester: whole session
Independent research is the defining feature of a postgraduate student. In this module, students will conduct a mix of applied and methodological research study under the supervision of one member of staff from Biostatistics, Computer Science or Public Health, with the potential addition of a domain expert or Health Care Professional if it is required by the subject of the dissertation. Students will identify the research question and use appropriate methodologies to answer a specific gap in existing knowledge in health data science. There will be a minimum of 12 hours of supervisory meetings to assist each student in achieving this. The written assessment is a 10,000 word dissertation.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
Semester two
Compulsory modules
Actionable Healthcare Data Analytics (DASC505)
Credits: 15 /
Semester: semester 2
This module aims to equip students with the knowledge and skills required to transform health data into actionable information, which can be used to enhance healthcare services, policies, and outcomes. By integrating data science and informatics engineering, the module will focus on building technical and human systems that support research and business intelligence for health systems. It will cover the core elements of Learning Health Systems and the principles involved in developing and evaluating an informatics intervention through lectures, guest speakers, data labs, and workgroup discussions. This module will provide students with the informatics knowledge and skills to turn health data into information that can be actioned in health systems, producing improved care, better data, and better policies – a so-called Learning Health System. It will combine data science and informatics engineering approaches to building technical and human systems that underpin research and business intelligence for health systems. The students will learn about and assessed on the core elements of Learning Health Systems and the principles of developing and evaluating an informatics intervention. Learning will be assessed through an oral presentation where students communicate actionable findings based on a close-to-reality synthetic dataset and a written analysis report on six-week collaborative Data Lab exercise.
Dissertation (DASC500)
Credits: 60 /
Semester: whole session
Independent research is the defining feature of a postgraduate student. In this module, students will conduct a mix of applied and methodological research study under the supervision of one member of staff from Biostatistics, Computer Science or Public Health, with the potential addition of a domain expert or Health Care Professional if it is required by the subject of the dissertation. Students will identify the research question and use appropriate methodologies to answer a specific gap in existing knowledge in health data science. There will be a minimum of 12 hours of supervisory meetings to assist each student in achieving this. The written assessment is a 10,000 word dissertation.
Optional modules
Prediction Modelling & Joint Longitudinal and Survival Data Analysis (DASC506)
Credits: 15 /
Semester: semester 2
In a rapidly evolving data landscape, the ability to predict outcomes and understand complex longitudinal and time-to-event data is of key significance. The module equips students with the skills to develop robust prediction models, unravel patterns within datasets to foresee future trends and outcomes, and employ statistical and machine learning techniques. It also delves into understanding how variables evolve over time by exploring the intricacies of survival outcomes and temporal dependencies through joint modelling of the two outcomes. Students will benefit from research-connected teaching with hands-on practical applications, translating theory into real-world practice. The curriculum also incorporates the latest research and advancements in the field through formal lectures and seminars featuring guest speakers.
High-dimensional Data Structures and Learning Algorithms (DASC507)
Credits: 15 /
Semester: semester 2
Real world health data often has complex structures, which can impact on individual health outcomes. Outcomes may depend on existing administrative or geographic structures, or the risk of developing disease may be based on complicated combinations of underlying factors. With growing access to high-dimensional datasets, both in health research, and across the data-science spectrum, suitable statistical methods are essential to harness the information within these datasets. This module will provide an understanding of how appropriate statistical methods can be selected, and teach the skills necessary to conduct analyses on real world data. In this module, students will learn a variety of statistical modelling and learning algorithms, and be introduced to state-of-the-art machine learning methods for data mining and classification. The tools developed during this module will provide students with the understanding and skills needed to perform complex analysis as part of a data science team in their future careers. Students will benefit from research-connected teaching with academic staff, and develop hands-on experience of data analysis in data-labs.
Statistical Genetics and Pharmacogenomics (DASC508)
Credits: 15 /
Semester: semester 2
In today’s healthcare landscape, understanding which genomic factors influence disease risk and treatment response is paramount. Our module delves into the pivotal role of genomic data in deciphering disease aetiology and tailoring treatment plans to individuals, aligning with the ambition of healthcare providers globally to integrate genomics into patient care. Through a blend of theoretical teaching and hands-on exercises, you’ll gain proficiency in specialized statistical methods and programming necessary to navigate complex genomic datasets. From genotype quality control to polygenic risk scores, our comprehensive curriculum covers key analysis techniques essential for genomic research, including both traditional statistical techniques and machine learning methods. It assumes no prior knowledge of genomic data, with an introduction to the terminology and structure of the data, as well as to linux programming during the first two weeks. With a focus on practical application, and supplemented by guest lectures from leading experts, our module prepares you to be able to confidently analyse and interpret the huge and complex datasets that are typical within statistical genetics and pharmacogenomics research. Learning will be assessed via two practical assessments.
Computational Intelligence (COMP575)
Credits: 15 /
Semester: semester 2
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
Data Mining and Visualisation (COMP527)
Credits: 15 /
Semester: semester 2
The module covers a range of topics and techniques for analyzing data. Students will learn about different types of data mining problems, including classification, clustering, association pattern mining, and social network analysis, as well as algorithms to solve them.
Students will program selected data mining algorithms from scratch using Python. This hands-on approach will allow them to gain a deeper understanding of how the algorithms work and how they can be applied to real-world datasets. They will experiment with different datasets to see how the algorithms perform and learn how to interpret the results.
Machine Learning and BioInspired Optimisation (COMP532)
Credits: 15 /
Semester: semester 2
This module teaches you about bio-inspired algorithms for optimisation and machine learning. The algorithms are based on reinforcement learning, DNA computing, brain or neural network models, immune systems, the evolutionary version of game theory, and social insect swarm behaviour such as ant colonies and bee colonies. These techniques are extremely useful for searching very large solution spaces (optimisation) and they can be used to design agents or robots that have to interact and operate in dynamic unknown environments (e.g. a Mars rover, a swarm of robots or network of satellites). The idea of learning optimal behaviour, rather than designing, algorithms and controllers is especially appealing in AI.
Evaluation of Healthcare Interventions (DASC504)
Credits: 15 /
Semester: semester 2
Digital technology offers great potential for improving the design, conduct and analysis of studies evaluating health care interventions. Recent evidence shows the utility of long-term follow-up of clinical trial patients through the electronic health record. Information collected directly from trial participants, through wearables, apps, and online patient-reported outcome measurement, can supplement routinely collected clinical data. Searching electronic health records for eligible patients that could benefit from a particular trial may improve the assessment of feasibility of trial recruitment and address known challenges. The aim of this module is to provide an awareness of how today’s technology could improve the efficiency of randomised evaluations of health care interventions, and where further improvements are needed. Students will benefit from research-connected teaching, and have opportunities for active learning. The module will be taught via formal lectures, seminars from guest speakers, and discussion groups. Learning will be assessed via a coursework and a practical assessment.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
Final project
Compulsory modules
Dissertation (DASC500)
Credits: 60 /
Semester: whole session
Independent research is the defining feature of a postgraduate student. In this module, students will conduct a mix of applied and methodological research study under the supervision of one member of staff from Biostatistics, Computer Science or Public Health, with the potential addition of a domain expert or Health Care Professional if it is required by the subject of the dissertation. Students will identify the research question and use appropriate methodologies to answer a specific gap in existing knowledge in health data science. There will be a minimum of 12 hours of supervisory meetings to assist each student in achieving this. The written assessment is a 10,000 word dissertation.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
How you'll learn
Each 15-credit module involves around 150 hours of study.
You can expect to spend 2-3 hours a week per module in taught study and 3-5 hours a week per module in self-managed independent study. The programme has a blended format with a mix of face-to-face and online lectures, workshops and practical sessions.
Full-time students will complete the programme in three semesters and part-time students will complete the programme in six semesters.
How you're assessed
You’ll be assessed through a variety of written critiques and reports, software practical exercises and written exams. You’ll also be asked to present your work in a variety of formats, from oral presentations to a conference poster. All modules have active learning embedded within them.
Liverpool Hallmarks
We have a distinctive approach to education, the Liverpool Curriculum Framework, which focuses on research-connected teaching, active learning, and authentic assessment to ensure our students graduate as digitally fluent and confident global citizens.
Course options
Studying with us means you can tailor your degree to suit you. Here's what is available on this course.
The Institute of Population Health draws on over 100 years of teaching delivered by dedicated staff with real-world, practical experience. We are a hub for an extensive network of professionals, academics and researchers, you can be confident that a degree from us will prepare you for a lifelong career in healthcare services.
Support for students with differing needs from the Disability advice and guidance team. They can identify and recommend appropriate support provisions for you.
An exciting place to study
Major centre for education and research in health sciences
State-of-the-art facilities
Opportunities for in-depth study and development of advanced clinical skills
Flexible programmes for postgraduate study
Active research environment
Provides specialist training with taught and clinical placement components.
What students say...
What I enjoyed the most was conducting a genome-wide study on real-life data from the UK Biobank, which I undertook as part of my dissertation. It was great to work with academics in and across departments on this project, learn valuable research skills and work on developing the research idea into a full publication.
Developing transferable skills to enhance your employability is a key theme of the programme.
Potential employers are involved in the delivery of the course and you will be able to attend careers events with representation from higher education institutions, the NHS, industry and government agencies. This will ensure you have a variety of opportunities to network and build useful contacts.
Whenever possible, your dissertation project will be linked with external partner organisations, connecting you to potential employment and career progression opportunities.
Career support from day one to graduation and beyond
The health sector is a fast-growing employment sector around the world. There is an increasing need for professionals with strong quantitative skills to evaluate health care interventions and information systems.
The MSc Health Data Science is tailored to develop the statistical and computational skills needed to pursue a successful career as a data scientist working in academia, healthcare or biopharmaceutical sectors.
99% of health sciences students from the University of Liverpool find their main activity after graduation meaningful.
Graduate Outcomes, 2018-19.
Fees and funding
Your tuition fees, funding your studies, and other costs to consider.
Tuition fees
UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)
Full-time place, per year
£12,500
Part-time place, per year
£6,250
International fees
Full-time place, per year
£29,100
Part-time place, per year
£14,550
Fees stated are for the 2025-26 academic year.
Tuition fees cover the cost of your teaching and assessment, operating facilities such as libraries, IT equipment, and access to academic and personal support.
If you're a UK national, or have settled status in the UK, you may be eligible to apply for a Postgraduate Loan worth up to £12,167 to help with course fees and living costs. Learn more about fees and funding.
Additional costs
We understand that budgeting for your time at university is important, and we want to make sure you understand any course-related costs that are not covered by your tuition fee. This could include buying a laptop, books, or stationery.
We accept a 2:2 honours degree from a UK university, or an equivalent academic qualification from a similar non-UK institution. This degree should include substantial quantitative methods content in statistics and/or computer science.
As part of your application, you will be required to provide a personal statement outlining your learning ambitions, past achievements in academic or professional activities relevant to the programme and data science experience to date.
Please note, some of the optional modules on the course require programming skills of a standard equivalent to a first degree in computer science.
International qualifications
If you hold a bachelor’s degree or equivalent, but don’t meet our entry requirements, you could be eligible for a Pre-Master’s course. This is offered on campus at the University of Liverpool International College, in partnership with Kaplan International Pathways. It’s a specialist preparation course for postgraduate study, and when you pass the Pre-Master’s at the required level with good attendance, you’re guaranteed entry to a University of Liverpool master’s degree.
International applicants who do not meet the minimum required standard of English language can complete one of our Pre-Sessional English courses to achieve the required level.
English language qualification
Requirements
IELTS
6.5 overall, with no component below 6.0
TOEFL iBT
88 overall, with minimum scores of listening 19, writing 19, reading 19 and speaking 20. TOEFL Home Edition not accepted.
Duolingo English Test
120 overall, with no component below 105
Pearson PTE Academic
61 overall, with no component below 59
LanguageCert Academic
70 overall, with no skill below 65
PSI Skills for English
B2 Pass with Merit in all bands
INDIA Standard XII
National Curriculum (CBSE/ISC) - 75% and above in English. Accepted State Boards - 80% and above in English.
WAEC
C6 or above
PRE-SESSIONAL ENGLISH
Do you need to complete a Pre-Sessional English course to meet the English language requirements for this course?
The length of Pre-Sessional English course you’ll need to take depends on your current level of English language ability.
Liverpool bursts with diversity and creativity which makes it ideal for you to undertake your postgraduate studies and access various opportunities for you and your family.
To fully immerse yourself in the university experience living in halls will keep you close to campus where you can always meet new people. Find your home away from home.
Discover what expenses are covered by the cost of your tuition fees and other finance-related information you may need regarding your studies at Liverpool.
Contact us
Have you got a question about this course? Email the student experience team.
