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.
Who is this course for?
This master’s is suitable for you if you have a strong statistical and/or computer science background and want to analyse and address health care problems using data.
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.
If you're able to study part-time, you'll
study the same modules as the full-time master's degree over a longer period, usually 24 months. You
can make studying work for you by arranging your personal schedule
around lectures and seminars which take place during the day. After you
complete all the taught modules, you will complete your final
dissertation or project and will celebrate your achievements
at graduation the following term.
Studying part-time means you can study alongside work or any
other life commitments. You will
study the same modules as the full-time master's degree over a longer period, usually 24 months. You
can make studying work for you by arranging your personal schedule
around lectures and seminars which take place during the day. After you
complete all the taught modules, you will complete your final
dissertation or project and will celebrate your achievements
at graduation the following term.
Semester one
Compulsory modules
Introduction to Health Research, Data and Team 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. 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.
Introduction to Statistics (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 health data for applied epidemiology (DASC503)
Credits: 15 /
Semester: semester 1
Modern Healthcare and Public Health systems are generating an enormous amount of data every year. These data are usually unlinked, coded with different disease ontology frameworks, and biased. On occasion, entities may provide the first layer of processing and curation for some of the routinely generated data, but data will require further layers to be ready for analysis. 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 lab sessions. Learning will be assessed via a practical assessment, and a poster and pre-recorded oral presentations.
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
Database and Information Systems (COMP518)
Credits: 15 /
Semester: semester 1
This module focuses on how databases are used in modern information systems. They are at the heart of almost all systems, such as supermarket checkouts, online banking, home rentals, and much more. One of the most successful data definition and manipulation languages is SQL, which will be covered in detail. The module will also introduce some of the fundamental concepts in computer science, as well as the mathematical underpinnings of relational databases and the techniques used to support concurrency and reliability in large information systems.
Qualitative Research Methods (ENVS441)
Credits: 15 /
Semester: semester 1
This module is intended to explore qualitative research methods in a holistic manner; moving from research philosophy, through design to individual research methods and analysis. The module covers a range of qualitative research methods through a mixture of lectures and workshops. In undertaking this module students will consider how research design and individual research methods need careful selection to suit the specific research problems or questions under investigation.
Programme details and modules listed are illustrative only and subject to change.
Semester two
Compulsory modules
21st Century 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 evaluations of health care interventions, and where further improvements are needed. 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 discussion groups. Learning will be assessed via a coursework and a practical assessment.
Actionable Analytics (DASC505)
Credits: 15 /
Semester: semester 2
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 bring together data science and informatics engineering approaches to building technical and human systems that underpin both research and business intelligence for health systems. Students will learn about the core elements of Learning Health Systems and the principles of developing and evaluating an informatics intervention. 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 an oral presentation in which students will communicate actionable findings 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
Advanced Biostatistics I - Joint Longitudinal & Survival Data Analysis and Prediction Modelling (DASC506)
Credits: 15 /
Semester: semester 2
Healthcare data consists of a range of different data types, such as blood pressure over time, time until death, or family history of a disease. The goal is usually to make predictions for the future to facilitate more informed treatment choices, improve patient care, and raise patient quality of life. However, simple approaches remain widespread amongst researchers leading to inefficient analysis and presentation of results, and therefore potential for important predictive relationships to be missed. The aim of this module is to provide an awareness of the potential issues occurring in data from clinical and observational settings, and teach a variety of techniques to conduct statistical analyses for the estimation and clinical prediction using real world 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 Statistical Analysis Plan, oral presentation, and written report.
Advanced Biostatistics II - Analysis Methods for Complex Data Structures (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 risk of developing disease may be based on combinations of underlying factors. In designing clinical studies, researchers should consider how data structures affect the choice of analysis method to answer clinical questions. 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. 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 an oral presentation and a practical assessment.
Advanced Biostatistics III - Statistical Genetics and Pharmacogenetics (DASC508)
Credits: 15 /
Semester: semester 2
As our understanding of the genetic basis of disease improves, genetic information is increasingly used for prediction, prognosis and diagnosis of disease, and for predicting response to treatment. Identifying genetic variants which are useful for these purposes involves the analysis of datasets from studies including genome-wide association, whole-exome and whole-genome sequencing studies. Such datasets are large, can include both common and rare variants, and have diverse outcomes, including binary, quantitative and time to event. Due to the scale of data involved, specialist methods and software are required for processing and analysis. The aims of this module are to introduce genetic terminology and key genetic concepts, share methods for processing and analysing different types of genetic data through the use of practical sessions, and introduce freely available software tools to support their analysis. 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 an oral presentation and 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
To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining. As part of the module students program with Python selected data mining algorithms and experiment using real-world datasets. Google search framework and IBM Watson QA system and various other industrial level data mining applications are discussed.
Skills: Communication skills (listening andquestioning, respecting others, contributing to discussions, communicating in a foreign language, presenting own work in form of a talk) This skill is not evaluated in the module. However, students are encouraged to verbally participate in the numerous in-class quizzes about data mining concepts.
Problem solving
Two Python programming assignments (accounting for 25% of the total mark for the module) arecirculated. The students are expected to implement a selected group of data mining algorithms from the scratch by themselves and experiment using real-world datasets.
Business and customer awareness (basic understanding of the key drivers for business success – including the importance of innovationand taking calculated risks – and the need to provide customer satisfaction and build customer loyalty) Google search framework, IBM Watson QA system and various other industrial level data mining applications are discussed in the class as specific implementations of the algorithms introduced in the module.
Information Technology (IT) skills (IT skills, including familiarity with word processing, spreadsheets, file management, use of internet search engines, use of specific software and/or IT and programming paradigms) Students are required to use industry-level data processing libraries such as numeric python library, scientific python library and scikit-learn machine learning library during the lab sessions.
Computer science principles
Examples: Formal tools for building and verifying complex electronic-commerce systems (name some concrete software). Formal methods for deriving classification algorithms that focus on different loss functions such as the cross-entropy loss (logistic regression), hinge loss (support vector machines) are taught in the module.
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.
Programme details and modules listed are illustrative only and subject to change.
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.
Programme details and modules listed are illustrative only and subject to change.
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 School of Health Sciences 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
£10,850
Part-time place, per year
£5,425
International fees
Full-time place, per year
£21,550
Part-time place, per year
£10,775
Fees stated are for the 2023-24 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.
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.
You will normally need a 2:1 honours degree or above, or equivalent. This degree should include substantial quantitative methods content in statistics and/or computer science.
We also encourage applications from those working in health-related professions or those with non-standard qualifications. Each application will be assessed on its own merits.
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.
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.
