Overview
Why Lancaster?
- Develop and consolidate foundational skills in the three main areas of Health Data Science: epidemiology, statistics, and computer science.
- Become part of the health informatics team and make a real impact at national and international public health organisations during your project placement.
- Have the opportunity to experience hands-on fieldwork at our summer school on the idyllic Pemba Island in Tanzania.
Why Health Data Science?
In today’s world data is everything. We’re using it to make better, evidence-based decisions in every walk of life. When it comes to health, good data – and optimal use of that data – has the potential to improve health outcomes on an individual level, at a global scale.
As technology advances and ‘big data’ gets even bigger, and as we look for new ways to address health inequalities, this is an exciting time to join the world of health data science.
Is Health Data Science for me?
Every aspect of healthcare relies on the advanced technical skills of data scientists, trained specifically to combine deep statistical thinking with their expertise in health and computer science. If you want to harness the power of data to transform healthcare treatments and services, a Health Data Science MSc will provide you with the skills required to use data for good.
You’ll cover the fundamentals of Health Data Science and gain a solid understanding of statistics and computer programming, while building your knowledge of applied epidemiology.
Building on this introduction, you will specialise your studies, choosing to follow either the Global Health pathway, focusing on how data can be used to model disease transmission in different communities; or the Health Informatics pathway, giving you the skills to analyse routinely collected data and undertake economic analysis of healthcare.
You’ll apply your new skills and knowledge in a real-life context during your 12-week project placement, working as part of a multidisciplinary team with one of our high-profile partners. In the past, these have included the NHS, Bill & Melinda Gates Foundation, Clinton Health Access Initiative and Yale University. Our collaboration with the World Health Organization (WHO) also means you’ll also have the unique opportunity to apply to attend our summer school hosted by Fondazione Ivo de Carneri at their Public Health Laboratory on the picture-perfect Pemba Island in Tanzania – one of the parts of the world most affected by parasitic disease.
Where will Health Data Science take me?
As the volume and complexity of health data available to us continues to grow, there’s a serious demand for health data scientists across the globe. You could end up working in healthcare, pharmaceuticals, medical research or development, for technology companies, governments or NGOs – or you could choose to move into academic research with a PhD.
Whether you take the Global Health or Health Informatics pathway, you’ll be ready to lead, with a solid grounding in all aspects of Health Data Science and the skills to interpret, analyse and utilise data to tackle healthcare challenges across a wider variety of sectors.
Entry requirements
Academic Requirements
2:1 Hons degree (UK or equivalent) in a relevant discipline including: Statistics, Epidemiology, Computer Science, Economics, Biomedical Sciences, Physical Sciences, or similar.
We may also consider non-standard applicants based upon experience and merit. Please contact the Programme Director for information.
If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.
English Language Requirements
We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.
We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We also consider other English language qualifications.
If your score is below our requirements, you may be eligible for one of our pre-sessional English language programmes.
Contact: Admissions Team +44 (0) 1524 592032 or email pgadmissions@lancaster.ac.uk
Pre-master’s programmes
Delivered in partnership with INTO Lancaster University, our one-year tailored pre-master’s pathways are designed to improve your subject knowledge and English language skills to the level required by a range of Lancaster University master’s degrees. Visit the INTO Lancaster University website for more details and a list of eligible degrees you can progress onto.
Course structure
You will study a range of modules as part of your course, some examples of which are listed below.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research. Not all optional modules are available every year.
Core
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This module provides students with a formal understanding of research methods, and develops their ability to critically reflect on research approaches and practices in the field. On completion of this module students will be able: to understand what the data science role entails, and how that individual performs their job within an organisation on a day-to-day basis; to understand how research is performed in terms of formulating a hypothesis and the implications of research findings, and be aware of different research strategies and when these should be applied; to gain an understanding of data processing, preparation and integration, and how this enables research to be performed; to critique research proposals in terms of their ethical implications; to learn how data science problems are tackled in an industrial settings, and how such findings are communicated to people within the organisation.
This is an introductory module on epidemiology with a strong focus on applied and quantitative topics. The module introduces key epidemiological concepts, including types of health outcomes, definitions of exposure and risk, and metrics used to quantify disease in a population. The students will be given an overview of the most commonly used epidemiological study designs: case-control studies, randomized control trials and cohort studies. Limitations and issues arising from recruitment and sampling biases will be discussed for each design. An important topic of the module will be how to draw inferences from epidemiological studies, defining association, causality and how these differ. In the lab sessions of the module, the students will analyse epidemiological data using R statistical programming language.
A three-month dissertation period (June to September) will involve the structured and innovative application of project management, problem solving, computing, statistical and analytical skills and communication to address a substantive research question / data science issue. The project aims to consolidate, integrate and further develop the data science skills gained during the taught course component. The dissertation is conducted as a full-time project.
Projects will vary but will broadly focus on public health problems which will require the use of advanced statistical methodology. You will undertake a project with an associated 'external placement', facilitated by an health care provider or research organisation working in partnership with Lancaster University. For some, an 'academic project' (i.e. without placement with an external partner) may better align with your career aspirations, such as for progression towards a PhD, or your skills level. In addition, if it is pedagogically or practically beneficial to both the student and/or company, the dissertation work could be undertaken at the University.
The module will teach the fundamentals of computing: historical development of computational machines, data storage, computational processing, and the development of computer science. This will be done using the R language for programming with focus on: simple calculations; structured programming with loops and conditions; data structures; writing functions. The students will be taught how to handle the R language for data file input/output; selection and filtering; exploratory graphics. Throughout the module, the students will also learn how to develop good computational research practice: project management for data and code; dynamic documents for reproducible research; source code management. In the second part of the module the students will learn the Python language basics: fundamentals for calculation and programming; writing functions and modules for code re-use; creating new classes and programming with objects; Python including command line and notebooks. The module will conclude with an introduction to numerical Python: using NumPy and Pandas for data analysis; plotting basic graphics.
This module introduces basic methods and models for statistical analysis with a strong focus on epidemiological applications. The module starts with the use of graphical tools for exploratory analysis: scatter plots; box plots; transformation of the response and outcome variables. The linear regression modelling framework is introduced with a focus on: critical evaluation of assumptions; link between regression analysis and ANOVA; interpretation of regression coefficients; multicollinearity and dealing with confounding factors; analysis of residuals. Building on this, the module covers Binomial and Poisson regression, and students will learn how to carry out hypothesis-testing through the analysis of deviance and the use of regression diagnostics. In the final part, the module gives an introduction to linear mixed models (random intercept and slope) and how to deal with overdispersion of count data.
Optional
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The module will begin by outlining the reasons for conducting economic evaluations with reference to economic concepts such as opportunity costs and marginality. Students will then how to conduct economic evaluations. The focus will be on how to analyse data from a trial-based economic evaluation, on how to conduct a model-based economic evaluation using, for example, decision trees or Markov models, and on how to characterise uncertainty using, for example, probabilistic sensitivity analysis.
The module will give students an understanding of the nature of routinely collected NHS data including ways of coding it (such as the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10)). Furthermore there will be exploration of other routinely collected data sets such as those held by the Office of National Statistics. Alongside this the module will cover relevant structures within the NHS with particular reference to data collection, coding and analysis as well as information governance and ethics. Students will also explore stakeholder engagement in the NHS including business intelligence and clinical staff. The module will cover approaches to identifying research priorities in the NHS. Methodologically, students will learn about methods such as systematic review and meta-analysis and standard methods for data cleaning and preparation (such as methods for identifying missing data and data engineering).
Almost every set of data, whether it consists of field observations, data from laboratory experiments, clinical trial outcomes, or information from population surveys or longitudinal studies, has an element of missing data. For example, participants in a survey or clinical trial may drop-out of the study, measurement instruments may fail, or human error invalidate instrumental readings. Missingness may or may not be related to the information being collected; for instance, drop out may occur because a patient dislikes the side-effects of an experimental treatment or because they move out of the area or because they find that they no longer have the time to attend follow up appointments. In this module you will learn about the different ways in which missing data can arise, and how these can be handled to mitigate the impact of the missingness on the data analysis. Topics covered include single imputation methods, Bayesian imputation, multiple imputation (Rubin's rules, chained equations and multivariate methods, as well as suitable diagnostics) and modelling dropout in longitudinal modelling.
Using motivating examples from environmental and health sciences, this module first introduces key data concepts of geostatistical analysis. The students will then learn how to perform the different stages of a geostatistical analysis, including: spatial exploratory analysis, model formulation, parameter estimation and spatial prediction. Building on the students' knowledge of standard linear regression, they will learn how to formulate and apply linear geostatistical models using maximum likelihood and Bayesian methods of estimation. The module also introduces generalized geostatistical models, with a special focus on Binomial logistic and Poisson log-linear models and their applications to disease prevalence mapping and health surveillance.
This module will start by introducing the mathematical aspects of deterministic and stochastic infectious disease models. The theoretical lectures will be complemented with lab sessions for the implementation and simulations of mathematical models in R. The module will then focus on fitting of infectious disease models to observed epidemiological data in R.
Management of infectious disease datasets in R, display and presentation of complex model-based outputs is a key component of the module.
Building upon your existing knowledge, this module starts by illustrating the limitations of standard linear regression to analyse time series data and how to question the assumption of independence through the analysis of residuals. In this context, you will learn how to use graphical tools for exploring time series analysis, such as lag-plots and autocorrelation plots. The modelling of seasonal effects, inherent to many environmentally driven diseases, will be taught based on harmonic regression methods. Following this, the module will give an introduction to the theory of discrete time series models and will focus on simple autoregressive processes. Continuous time series models based on Gaussian processes will be introduced to analyse time series for counts data. Finally, the module demonstrates how the methods learned can be used for forecasting problems, with applications to the detection of disease outbreaks.
Fees and funding
Location | Full Time (per year) | Part Time (per year) |
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Home | £14,140 | £7,070 |
International | £30,310 | £15,155 |
Additional fees and funding information accordion
There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.
Specific additional costs for studying at Lancaster are listed below.
College fees
Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small College Membership Fee which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.
For students starting in 2025, the fee is £40 for undergraduates and research students and £15 for students on one-year courses.
Computer equipment and internet access
To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.
The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.
For most taught postgraduate applications there is a non-refundable application fee of £40. We cannot consider applications until this fee has been paid, as advised on our online secure payment system. There is no application fee for postgraduate research applications.
For some of our courses you will need to pay a deposit to accept your offer and secure your place. We will let you know in your offer letter if a deposit is required and you will be given a deadline date when this is due to be paid.
The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your fee status.
If you are studying on a programme of more than one year’s duration, tuition fees are reviewed annually and are not fixed for the duration of your studies. Read more about fees in subsequent years.
Scholarships and bursaries
You may be eligible for the following funding opportunities, depending on your fee status and course. You will be automatically considered for our main scholarships and bursaries when you apply, so there's nothing extra that you need to do.
Unfortunately no scholarships and bursaries match your selection, but there are more listed on scholarships and bursaries page.
If you're considering postgraduate research you should look at our funded PhD opportunities.
Scheme | Based on | Amount |
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We also have other, more specialised scholarships and bursaries - such as those for students from specific countries.
Browse Lancaster University's scholarships and bursaries.
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Data, Computing and Communications
- Artificial Intelligence MSc
- Artificial Intelligence, Society, and Global Challenges MA
- Communication Systems MSc by Research
- Communication Systems PhD
- Computer Science MPhil/PhD
- Computer Science MSc by Research
- Cyber Security MSc
- Cyber Security Executive MBA MBA
- Data Science MSc
- Data Science PgCert
- Data Science PgDip
- Health Data Science PhD
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Mathematics and Statistics
- Data Science MSc
- Data Science PgCert
- Data Science PgDip
- Health Data Science PhD
- Mathematics PhD
- Natural Sciences MSc by Research
- Natural Sciences PhD
- Social Statistics PhD
- Statistics MSc
- Statistics PGDip
- Statistics PhD
- Statistics PhD (Integrated)
- Statistics and Epidemiology PhD
- Statistics and Operational Research MRes
- Statistics and Operational Research (STOR-i) PhD
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Medicine
- Anaesthesia and Peri-Operative Sciences PgDip
- Clinical Psychology DClinPsy
- Clinical Research MSc
- Clinical Research PgCert
- Clinical Research PgDip
- Health Data Science PhD
- Medical Education PgCert
- Medical Ethics and Law PhD
- Medical Sciences MSc by Research
- Medicine M.D.
- Medicine PhD
- Social and Behavioural Sciences in Medicine PhD
- Sports and Exercise Sciences PhD
- Statistics and Epidemiology PhD
Important Information
The information on this site relates primarily to 2025/2026 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.
The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.
More information on limits to the University’s liability can be found in our legal information.
Our Students’ Charter
We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. View our Charter and other policies.