The MRes in Data Science for Health features a particular focus on research and research methods to address important questions, which are pertinent to global, societal and cultural health care needs.
Introduction
This programme is for those with an interest in developing data science research skills within the field of health and gives direct authentic experience in developing and delivering a novel empirical research project to answer a specific gap in existing knowledge in health data science. You will be able to choose from a series of taught modules suited to your research interests which will develop of your academic background and skills.
Modules cover a range of core health research, epidemiological and statistical topics, with the option to follow various specialist statistical pathways.
Who is this course for?
The MRes in Data Science for Health is for current and future health data scientists, at all career stages, including those in public and private sectors. This course is designed for anyone with an interest in data science for health, irrespective of previous training or qualifications.
What you'll learn
Communicate information, ideas, problems, and solutions to both specialist and non-specialist audiences
Select and apply relevant methods to analyse data and interpret their validity
Critically appraise literature, concepts, and research techniques related to research in data science
Apply advanced statistical methods to the analysis of health data
Use originality of thought and self-directed learning skills to tackle and solve problems
Plan and conduct an original piece of research in health data
Course content
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
Semester one
Compulsory module
DASC510
Data Science for Health Research Project
This module is the first of a two-part research project and gives direct authentic experience in developing and planning a novel empirical research project. Students will use appropriate methodologies to answer a specific gap in existing knowledge in health data science. Students can choose from a selection of applied and/or methodological research studies or may choose to define their own research question/study, with supervision provided by a member of academic staff.
Optional 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.
AN INTRODUCTION TO QUALITATIVE RESEARCH (PUBH160)
Credits: 15 /
Semester: semester 1
This module covers all aspects of qualitative research including qualitative research design, qualitative methodologies and methods, and analysis of qualitative data. During the module students will be introduced to the key theories and concepts of qualitative research; gain an in-depth understanding of the philosophy of qualitative research, and how it differs from quantitative research.
They will also develop an essential understanding of the ethics of carrying out qualitative research.
Students will develop their knowledge and experience of the skills needed in qualitative research by designing their own piece of qualitative research which they will then carry out.
Students will also gain a theoretical and practical understanding of the different methods used in qualitative research including interviews, focus groups, photovoice and participant observation before looking at the various different ways of analysing qualitative data. They will also develop the skills to critically appraise published qualitative research.
Lectures will be delivered via weekly sessions which will involve a lecture element, in-class discussions and in-class group work. Students are expected to do self-directed learning (SDL) and will be expected to prepare for the contact sessions in advance using materials and readings which will be placed on the VLE one week before each session. Students will need to do the reading and any activities prior to the session and the sessions will then be used to consolidate this learning.
For the summative assessment of this module, students will be expected to conduct a micro, in-class research project which will allow them to try out the skills involved in conducting Qualitative Research. It will also provide them with the opportunity to participate in another research project so they can experience what it is like to be a research participant and then reflect on that experience.
PRACTICAL RESEARCH SKILLS (PSYC644)
Credits: 30 /
Semester: semester 1
To provide students with the knowledge necessary to decide whether or not to pursue a career in academia and, if so, to obtain a PhD place, following by a Postdoctoral Fellowship and Full-time academic appointment.
To provide students with the skills necessary for writing successful PhD and research grant proposals.
To provide students with the knowledge and skills required for effectively communicating their research in a variety of forms, journal articles, posters, oral presentations, and to understand the importance of publication in high quality journals.
To provide students with the knowledge required for research consultancy work, and for dealing with the media.
To provide knowledge and training in the use of relevant computer software e.g. Excel, Refworks, Endnote.
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 module
DASC511
Data Science for Health Research Project
This module is the second of a two-part research project and gives direct authentic experience in conducting and reporting on a novel empirical research project. Students will use appropriate methodologies to answer a specific gap in existing knowledge in health data science. Students can choose from a selection of applied and/or methodological research studies or may choose to define their own research question/study, with supervision provided by a member of academic staff.
Optional modules
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.
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.
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.
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 module
DASC511
Data Science for Health Research Project
This module is the second of a two-part research project and gives direct authentic experience in conducting and reporting on a novel empirical research project. Students will use appropriate methodologies to answer a specific gap in existing knowledge in health data science. Students can choose from a selection of applied and/or methodological research studies or may choose to define their own research question/study, with supervision provided by a member of academic staff.
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
The learning and teaching strategy for the programme comprises a mixture of formal lectures, practical and tutorial sessions, discussion groups, student centred learning, and project work. Additional support is sought from online materials, selected textbooks and directed reading of research literature (taken from scientific journals and conference proceedings). Each module (except the research project) is worth 15 credits and thus totals approximately 150 hours, 25-50 of which are in taught sessions. The research project modules are split into a 30 credit module (during semester 1 to plan the project) and 90 credit module (in semesters 2 and 3 to conduct and complete the scientific report on the project).
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.
Your experience
Each day will involve a series of interesting, research focussed, lectures and practical sessions with time to digest content and prepare for later sessions and assessments. Students will also spend time in supervisory meetings and working independently on their research projects.
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.
99% of health sciences students from the University of Liverpool find their main activity after graduation meaningful
Career support from day one to graduation and beyond
Graduates from the MRes in Data Science for Health are likely to enter a variety of careers opportunities. These include:
• PhD student
• Research Assistant
• Trial statistician
• Epidemiologist
• Data Scientist
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
£4,786
Part-time place, per year
£2,393
International fees
Full-time place, per year
£31,250
Part-time place, per year
£15,650
Fees stated for UK students are for the academic year 2024/25 and are subject to change for 2025/26. Fees stated for international students are for the academic year 2025/26. Some MRes courses incur additional bench fees. You will be notified of any fee which applies to you in your offer letter.
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.
Applicants with any academic background will be considered, as students will be trained on basic statistical and computing skills. As such, there is no minimum entry requirement for this programme, but an acceptable English language qualification (IELTS 6.5 or equivalent, with no band less than 6.0) is required to ensure students can access the programme material which is all delivered in English.
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
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
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