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Data science plays a vital role in all aspects of the modern world. Our programme will ensure you have a strong foundation in this rapidly expanding, highly in-demand field to achieve your career aspirations.
Our programme delivers a broad yet rigorous grounding in computer science and statistics, adopting both theoretical and practical learning approaches. You will gain cutting-edge knowledge and skills through state-of-the-art equipment and excellent teaching offered by both the School of Computing and Communications and the School of Mathematical Sciences.
This engaging programme, and our reputation for excellence in research, means that you will receive high quality teaching delivered by academics who are experts in their field. Throughout the three years, you will develop a range of discipline specific skills and gain specialist knowledge that will prepare you for your chosen career.
During your first year, you will gain a comprehensive understanding of the fundamental principles of computer science, and will simultaneously develop your knowledge and understanding of mathematical methods and concepts. Bringing these two fields together into data science fundamentals, you will also enhance your data analysis, problem-solving and quantitative reasoning skills.
In the second year, you will further deepen your knowledge in linear algebra, probability and statistics, as well as your foundational understanding, programming, and software design skills. While studying these topics, you will complete our Project Skills module, which will provide you with the chance to enhance your research and employment skills through individual and group projects designed for data scientists. This will give you experience of approaching a data science problem in the real-world.
You will study in your third year at one of our overseas partner universities, building your global outlook and connectivity. You will choose specialist Data Science and Computing modules as well as modules from across the host institute, allowing you to gain cultural and personal skills as well as expanding your professional network. You will study the equivalent of 120 credits of which 30 credits of subject-specific modules will contribute credit towards your Lancaster degree.
Your final year will also give you the opportunity to specialise in a range of enriching research-informed optional modules, as well as undertaking a substantial data science individual project. In this project you will work closely with one of our academics, expand your problem-solving abilities, and draw upon the skills and knowledge that you have gained throughout your degree. This will be great experience for you to draw upon in an interview and in your career.
Lancaster University will make reasonable endeavours to place students at an approved overseas partner university that offers appropriate modules. Occasionally places overseas may not be available for all students who want to study abroad or the place at the partner university may be withdrawn if core modules are unavailable. If you are not offered a place to study overseas, you will be able to transfer to the equivalent standard degree scheme and would complete your studies at Lancaster. Lancaster University cannot accept responsibility for any financial aspects of the year abroad.
We are working to satisfy the emerging requirements for Data Science accreditation as defined by the appropriate professional bodies (such as the British Computer Society, Royal Statistical Society).
The gathering, interpretation, and evaluation of data is fundamental to all aspects of modern life. As a result, data science can lead to a career in a wide range of industries. Data Science graduates are very versatile, and have in-depth, specialist knowledge and a wealth of skills.
Upon completion of this degree, you will graduate with a comprehensive skill set, including data analysis and manipulation, logical thinking, problem-solving and quantitative reasoning, as well as adept knowledge of the discipline. As a result, data scientists are sought after in a range of industries, such as business and finance, defence, education, infrastructure and power, and IT and communications.
Particular graduate destinations may include data analyst, data scientist, or machine learning engineer. Many of our students also elect to continue in higher education by studying for MSc or PhD qualifications. Lancaster is home to the Data Science Institute (DSI), which creates a world-class Data Science research capability, setting the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges.
We provide careers advice and host a range of events throughout each academic year. These include our annual careers fair, attended by exhibitors who are interested in providing placements and vacancies to data science students and graduates. You can also speak face-to-face with employers such as Network Rail, Oracle, and Johnson and Johnson, in addition to a broad range of SMEs. Our graduates have gone on to work with major technology companies such as IBM, Google, BBC, and BAE, while others have chosen to take their software design, development, and management skills to SMEs, or have set up their own technology-centric businesses.
Lancaster University is dedicated to ensuring you not only gain a highly reputable degree, you also graduate with the relevant life and work based skills. We are unique in that every student is eligible to participate in The Lancaster Award which offers you the opportunity to complete key activities such as work experience, employability/career development, campus community and social development. Visit our Employability section for full details.
A Level AAA
Required Subjects A level Mathematics or Further Mathematics grade A
GCSE English Language grade C or 4
IELTS 6.0 overall with at least 5.5 in each component. For other English language qualifications we accept, please see our English language requirements webpages.
International Baccalaureate 36 points overall with 16 points from the best 3 Higher Level subjects including 6 in HL Mathematics (either analysis and approaches or applications and interpretations)
BTEC May be considered alongside A level Mathematics or Further Mathematics at grade A
We welcome applications from students with a range of alternative UK and international qualifications, including combinations of qualifications. Further guidance on admission to the University, including other qualifications that we accept, frequently asked questions and information on applying, can be found on our general admissions webpages.
Contact Admissions Team + 44 (0) 1524 592028 or via ugadmissions@lancaster.ac.uk
Delivered in partnership with INTO Lancaster University, our one-year tailored foundation pathways are designed to improve your subject knowledge and English language skills to the level required by a range of Lancaster University degrees. Visit the INTO Lancaster University website for more details and a list of eligible degrees you can progress onto.
Contextual admissions could help you gain a place at university if you have faced additional challenges during your education which might have impacted your results. Visit our contextual admissions page to find out about how this works and whether you could be eligible.
Lancaster University offers a range of programmes, some of which follow a structured study programme, and some which offer the chance for you to devise a more flexible programme to complement your main specialism.
Information contained on the website with respect to modules is correct at the time of publication, and the University will make every reasonable effort to offer modules as advertised. In some cases changes may be necessary and may result in some combinations being unavailable, for example as a result of student feedback, timetabling, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes and new research. Not all optional modules are available every year.
Students are provided with an understanding of functions, limits, and series, and knowledge of the basic techniques of differentiation and integration. Examples of functions and their graphs are presented, as are techniques for building new functions from old. Then the notion of a limit is considered along with the main tools of calculus and Taylor Series. Students will also learn how to add, multiply and divide polynomials, and will learn about rational functions and their partial fractions.
The exponential function is defined by means of a power series which is subsequently extended to the complex exponential function of an imaginary variable, so that students understand the connection between analysis, trigonometry and geometry. The trigonometric and hyperbolic functions are introduced in parallel with analogous power series so that students understand the role of functional identities. Such functional identities are later used to simplify integrals and to parametrise geometrical curves.
The creation of the microprocessor revolutionised global innovation and creativity. Without such hardware we would have no laptops, no smartphones, no tablets. Life changing technologies from MRI scanners to the Internet would simply not exist.
This module provides an introduction to the field of Digital Systems – the engineering principles upon which all contemporary computer systems are based. Students will study the elements that work together to form the architecture of digital computers, including computer processors, memory, data storage, and input/output. They will unearth the ways in which these are enabled by digital logic – where George Boole’s theory of a binary based algebra meets electronics. Building on SCC.111, students also discover how the software programs we write translate to, and interact with, such hardware. Finally, students will explore the effects of multi-process operating systems, and how these interplay with the capabilities and architecture of modern computers to optimise performance and robustness.
Computing and data drive many critical elements of modern society, directly or indirectly. It’s vital that there is a strong theoretical foundation to computer science. This module begins by examining the hard questions central to computer science and reasoning itself to prepare students for the in-depth critical thinking and discussion required at university level. Students will cover the fundamentals in logic, sets, and mathematics of vectors, matrices, and linear algebra which have practical applications in software such as computer graphics. Algorithms, abstract data types, and analysis of algorithms is introduced to allow our students to make reasoned decisions about the design of their programs. Finally, they will get the chance to investigate and apply the principles of Data Science to select, process, and analyse data, and examine the way programs and systems can be designed to efficiently support work with data and question the limits of conclusions that can be drawn from such systems.
This course extends ideas of MATH101 from functions of a single real variable to functions of two real variables. The notions of differentiation and integration are extended from functions defined on a line to functions defined on the plane. Partial derivatives help us to understand surfaces, while repeated integrals enable us to calculate volumes.
In mathematical models, it is common to use functions of several variables. For example, the speed of an airliner can depend upon the air pressure and temperature, and the direction of the wind. To study functions of several variables, we introduce rates of change with respect to several quantities. We learn how to find maxima and minima. Applications include the method of least squares. Finally, we investigate various methods for solving differential equations of one variable.
Introducing the theory of matrices together with some basic applications, students will learn essential techniques such as arithmetic rules, row operations and computation of determinants by expansion about a row or a column.
The second part of the module covers a notable range of applications of matrices, such as solving systems of simultaneous linear equations, linear transformations, characteristic eigenvectors and eigenvalues.
The student will learn how to express a linear transformation of the real Euclidean space using a matrix, from which they will be able to determine whether it is singular or not and obtain its characteristic equation and eigenspaces.
Probability theory is the study of chance phenomena, the concepts of which are fundamental to the study of statistics. This module will introduce students to some simple combinatorics, set theory and the axioms of probability.
Students will become aware of the different probability models used to characterise the outcomes of experiments that involve a chance or random component. The module covers ideas associated with the axioms of probability, conditional probability, independence, discrete random variables and their distributions, expectation and probability models.
This module is designed to provide students with a strong foundation in principles of responsible computing, covering the legal, social, ethical and professional challenges that a practising computer scientist regularly faces. It is heavily research-led, delivered by staff actively researching these issues, and draws upon contemporary examples of where technology has resulted in both benefits and harm to people and society. Students will develop an understanding of the legal frameworks, professional codes, working practices and civil licenses designed to provide protection from these harms. Particular emphasis is placed on considerations relating to the need for computer systems to be trusted and trustworthy.
As a part of this module, students will study the use of participatory research methods in exposing real-world requirements for computing systems and ensuring equitable distribution of benefits and harms of digital innovation across the population, in alignment with a changing legal landscape. Inclusive design practices through the development phases from research to implementation are reviewed, examining the prevalence and impact of the gender data gap, accessibility constraints and exploring the benefits of diversity in the workplace through real-world examples. They will also discover ethical ways to practice personal and professional development for career progression.
Software now forms a central aspect of our lives. From the applications we run on our phones to the satellites in space, all modern technology is enabled by software. This module provides an introduction to the field of Software Development - the processes and skills associated with designing and constructing computer programs. Students are not expected to have any previous experience with the field of computing, and will study the contemporary knowledge, skills and techniques needed to develop high-quality computer software. This includes a thorough treatment of the principles of computer programming and how these principles can be applied using a range of contemporary and established languages such as Python, JavaScript and C. They will discover how programming languages can be classified and how to choose the best language for the task at hand.
Students will also investigate and apply the practical Software Engineering skills needed to ensure software is correct, robust and maintainable. These include techniques for problem analysis, design formulation, programming conventions, software commenting and documentation, testing and test case design, debugging techniques and version control.
To enable students to achieve a solid understanding of the broad role that statistical thinking plays in addressing scientific problems, the module begins with a brief overview of statistics in science and society. It then moves on to the selection of appropriate probability models to describe systematic and random variations of discrete and continuous real data sets. Students will learn to implement statistical techniques and to draw clear and informative conclusions.
The module will be supported by the statistical software package ‘R’, which forms the basis of weekly lab sessions. Students will develop a strategic understanding of statistics and the use of associated software, which will underpin the skills needed for all subsequent statistical modules of the degree.
In this module, students will build upon the foundations of algorithms and their complexity to develop a deeper understanding of algorithmic approaches to computational problem solving. They will explore computational complexity theory, which allows us to consider the very nature of computability – including non-deterministic polynomial (NP) complexity classes such as NP-hard, NP-complete and the classes of problems which cannot be solved. Students will be introduced to classical approaches to problem solving such as divide and conquer, recursion, and parallel approaches, emphasizing their relative benefits and weakness to different classes of problem. They will study advanced data structures in depth, such as tries, heaps, suffix arrays, k-d trees, and distributed hash tables, and explore the approaches for their efficient construction and use.
These theoretical aspects are grounded through practical work in the lab and placed in the context of case studies of extreme scale and embarrassingly parallel computing, derived from real-world problem domains introduced by invited speakers where possible. Finally, students explore key implications of algorithm performance including their impact on energy efficiency and sustainability to provide a coherent interface with other modules.
This module introduces the key ideas and fundamental principles of artificial intelligence (AI) and the types of problems that can be addressed by AI. Students will be introduced to the core concepts and philosophy of AI, including its history and definitions, classify the various approaches to AI, and discuss its presence in the modern world alongside its ethical considerations. They will unearth the underlying principles of search spaces, knowledge representation, and inference logic that form the core of rule-based systems.
Students will then go on to learn the principles of machine learning, emphasising clustering (e.g. k-means), classification (e.g. k-nearest neighbour) algorithms, linear regression, and neural networks. This deep dive provides the essential grounding necessary to progress to modules in topics such as Machine Learning, Computer Vision, and Natural Language Processing.
This module aims to teach and enhance skills, including both subject-related and transferable skills, appropriate to Part II students in Data Science. These skills include the preparation of mathematical documents and presentation materials, scientific writing, oral presentations and group work. As a part of this module, students will undertake both a group and individual project, both of which will involve the investigation of a topic relevant to Data Science. Students will also get the opportunity to utilise LaTex, becoming familiar with how to display formulae and mathematical symbols, as well as learning how to create sections, tables of contents, tables, and figures within the program.
Students will be provided with the foundational results and language of linear algebra, which they will be able to build upon in the second half of Year Two, and the more specialised Year Three modules. This module will give students the opportunity to study vector spaces, together with their structure-preserving maps and their relationship to matrices.
They will consider the effect of changing bases on the matrix representing one of these maps, and will examine how to choose bases so that this matrix is as simple as possible. Part of their study will also involve looking at the concepts of length and angle with regard to vector spaces.
Probability provides the theoretical basis for statistics and is of interest in its own right.
Basic concepts from the first year probability module will be revisited and extended to these to encompass continuous random variables, with students investigating several important continuous probability distributions. Commonly used distributions are introduced and key properties proved, and examples from a variety of applications will be used to illustrate theoretical ideas.
Students will then focus on transformations of random variables and groups of two or more random variables, leading to two theoretical results about the behaviour of averages of large numbers of random variables which have important practical consequences in statistics.
Statistics is the science of understanding patterns of population behaviour from data. In the module, this topic will be approached by specifying a statistical model for the data. Statistical models usually include a number of unknown parameters, which need to be estimated.
The focus will be on likelihood-based parameter estimation to demonstrate how statistical models can be used to draw conclusions from observations and experimental data, and linear regression techniques within the statistical modelling framework will also be considered.
Students will come to recognise the role, and limitations, of the linear model for understanding, exploring and making inferences concerning the relationships between variables and making predictions.
This module provides broader exposure to alternative programming language paradigms beyond imperative and object-oriented programming. Particular emphasis is given to functional programming languages, and their unique constraints and features. More specifically, students will investigate how introducing the concept of absolute immutability into programming languages enables a suite of expressive mechanisms within programming languages including pure functions, lambdas, higher order functions, pattern matching, currying, map/reduce, and pattern matching.
As a part of this module, students will also explore why functional languages bring about increased reliability and scalability, and how they are now experiencing a resurgence within the software industry. Finally, through hands-on laboratory sessions, students see how functional programming concepts are being integrated in mainstream programming languages such as Java, Python and JavaScript, to create versatile multi-paradigm programming environments
The internet and the world wide web have now pervaded every aspect of our lives, from e-commerce and entertainment to logistics and social media. Increasingly, application software is no longer written for specific devices, but for internet web browsers. The internet has replaced operating systems as the de facto platform for application development, making an already global phenomenon truly ubiquitous.
This module studies the various approaches to internet applications development, investigating both the client side and server-side approaches, discussing the trade-off of performance, scalability, privacy, and trust associated with these approaches. Students will review the role of “cloud infrastructures” (federated distributed computation) in the provision and management of internet applications. Through interactive lectures and small group practical sessions, students study common frameworks for client-side application development and create and deploy an internet application from first principles.
Your third year will be spent at one of our partner institutions in Europe, USA, Canada or Australia, where you will undertake modules that are equivalent to those offered at Lancaster. This is an opportunity to extend your horizons and experience another culture.
The Third Year Project’s aim is to consolidate, integrate, and further develop the data scientific skills gained during the BSc Data Science programme. Projects will vary from student to student, but all will focus upon a substantive data scientific issue / research question (including but not limited to design, implementation, and/or evaluative projects).
The academic supervisor will provide structured guidance and support throughout the project to ensure the requisite level of academic content and rigour is being maintained. This module will consist of the capstone project for the BSc Data Science degree, and will provide students with the opportunity to produce a substantial piece of work which they can present to others as evidence of their academic achievement at Lancaster.
Bayesian statistics provides a mechanism for making decisions in the presence of uncertainty. Using Bayes’ theorem, knowledge or rational beliefs are updated as fresh observations are collected. The purpose of the data collection exercise is expressed through a utility function, which is specific to the client or user. It defines what is to be gained or lost through taking particular actions in the current environment. Actions are continually made or not made depending on the expectation of this utility function at any point in time.
Bayesians admit probability as the sole measure of uncertainty. Thus Bayesian reasoning is based on a firm axiomatic system. In addition, since most people have an intuitive notion about probability, Bayesian analysis is readily communicated.
Computer graphics is an interdisciplinary field which deals with visual and image aspects of computing. It underpins the development of video games, use of computer-generated imagery in movies and has helped advance machine learning, cryptography, and parallel computing.
In this module, students explore the fundamental concepts related to visual content generation through relevant theory, such as the essential mathematics, graphics data structures and algorithms, kinematics, collisions, colour, and light. In particular, students will investigate the practical aspects of graphical scenes and rendering including virtual cameras, materials, mesh manipulation, scene-graphs, animation and modelling. They will learn about hardware-specific concepts designed to improve the quality and performance of graphics applications, such as GPU programming, mobile and cloud render-pipelines, shaders, stereoscopic and volumetric rendering. Emerging technologies and trends in research are also introduced with an analytical lens to identify future challenges, opportunities, and solutions.
Computer vision is a branch of artificial intelligence, in which we aim to develop computer-based systems that can interpret and draw meaningful deductions from digital images.
This module covers the fundamentals to understanding image formation and information relating to the human visual system and some fundamental image interpretation methodologies, including convolution, edge detection and feature extraction and comparison. Students will tackle key problems in current research, including semantic segmentation, object detection, and three-dimensional image interpretation. They will cover a range of approaches, from low-level image processing to convolutional neural networks. At the end of the module, students will be equipped to construct software components that implement contemporary image processing and computer vision algorithms and recognise issues within computer vision in order to develop and evaluate solutions.
This module will explore machine learning, which sits within the field of artificial intelligence and enables a computer to learn how to perform a task from data rather than traditional programming.
Students will study the key ideas and techniques of machine learning, which will help students to develop practical skills in problem solving and to understand the implications and potential of machine learning in business and society. They will begin by looking at real-world machine learning problems, challenges, and fundamental techniques in current machine learning methodology. Building on this, the module will cover a variety of approaches to machine learning, from decision trees to a wide range of deep neural networks, including multilayer perceptrons, convolutional neural networks, long short-term memory, autoencoder and generative adversarial networks.
Digital Health concerns the utilisation of digital technologies for health and care. It has a key and ever-growing role to play in improving health systems and public health, as well as increasing and improving the equity of access to health services. It has the potential to transform health and care delivery and support individuals to improve their health.
In this module, students will discover the practical applications, implications, and enabling technologies of digital health. They will survey the sensor technologies that permit remote and automated patient monitoring, study the technologies and processes that enable patient-driven healthcare. This module also investigates the structure of health data in electronic health records, and methods for the evaluation of digital health solutions. Alongside these applied topics, students will also learn about data governance and the ethical issues surrounding digital health technologies, policy, and regulation.
All programming languages are based on theoretical principles of formal language theory. In this module, students take a deep dive into formal languages representation and grammars, and how relate to programming language compilers and interpreters.
Students will study formal language syntax and semantics, phrase structure grammars and the Chomsky Hierarchy. They will learn how to classify languages and explore the concepts of ambiguity in Context Free grammars and its implications. In particular, they will learn about the compilation process including lexical analysis and syntactic analysis, recursive descent parsers, and semantic analysis. Finally, students get to investigate the synthesis phase, where intermediate representations, target languages, and structures lead to code generation. In the School, we blend lectures with small group lab sessions where students gain hands-on experience of applying such theory.
Statistical inference is the theory of the extraction of information about the unknown parameters of an underlying probability distribution from observed data. Consequently, statistical inference underpins all practical statistical applications.
This module reinforces the likelihood approach taken in second year Statistics for single parameter statistical models, and extends this to problems where the probability for the data depends on more than one unknown parameter.
Students will also consider the issue of model choice: in situations where there are multiple models under consideration for the same data, how do we make a justified choice of which model is the 'best'?
The approach taken in this course is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.
Using the classical problem of data classification as a running example, this module covers mathematical representation and visualisation of multivariate data; dimensionality reduction; linear discriminant analysis; and Support Vector Machines. While studying these theoretical aspects, students will also gain experience of applying them using R.
An appreciation for multivariate statistical analysis will be developed during the module, as will an ability to represent and visualise high-dimensional data. Students will also gain the ability to evaluate larger statistical models, apply statistical computer packages to analyse large data sets, and extract and evaluate meaning from data.
This module formally introduces students to the discipline of financial mathematics, providing them with an understanding of some of the maths that is used in the financial and business sectors.
Students will begin to encounter financial terminology and will study both European and American option pricing. The module will cover these in relation to discrete and continuous financial models, which include binomial, finite market and Black-Scholes models.
Students will also explore mathematical topics, some of which may be familiar, specifically in relation to finance. These include:
Throughout the module, students will learn key financial maths skills, such as constructing binomial tree models; determining associated risk-neutral probability; performing calculations with the Black-Scholes formula; and proving various steps in the derivation of the Black-Scholes formula. They will also be able to describe basic concepts of investment strategy analysis, and perform price calculations for stocks with and without dividend payments.
In addition, to these subject specific skills and knowledge, students will gain an appreciation for how mathematics can be used to model the real-world; improve their written and oral communication skills; and develop their critical thinking.
The aim is to introduce students to the study designs and statistical methods commonly used in health investigations, such as measuring disease, causality and confounding.
Students will develop a firm understanding of the key analytical methods and procedures used in studies of disease aetiology, appreciate the effect of censoring in the statistical analyses, and use appropriate statistical techniques for time to event data.
They will look at both observational and experimental designs and consider various health outcomes, studying a number of published articles to gain an understanding of the problems they are investigating as well as the mathematical and statistical concepts underpinning inference.
This module provides a broad introduction to Natural Language Processing (NLP), a branch of Artificial Intelligence where we develop computational methods to analyse and understand human languages.
Students will be exposed to the core concepts of the NLP pipeline covering methods and techniques for data collection, cleaning, tokenisation, and annotation using a hierarchy of linguistic levels (e.g. morphology, syntax, and semantics). They will experiment with and comparatively evaluate different methods and techniques, including rule based, probabilistic, machine learning and deep learning approaches. Students will also learn to apply and adapt NLP pipelines and tools to real world text mining scenarios and problems, including examples such as health and finance. Key issues such as ethical data collection, bias in language models, and employing sustainable computing methods are also emphasised throughout the learning and teaching in this module.
The concept of generalised linear models (GLMs), which have a range of applications in the biomedical, natural and social sciences, and can be used to relate a response variable to one or more explanatory variables, will be explored. The response variable may be classified as quantitative (continuous or discrete, i.e. countable) or categorical (two categories, i.e. binary, or more than categories, i.e. ordinal or nominal).
Students will come to understand the effect of censoring in the statistical analyses and will use appropriate statistical techniques for lifetime data. They will also become familiar with the programme R, which they will have the opportunity to use in weekly workshops.
Important examples of stochastic processes, and how these processes can be analysed, will be the focus of this module.
As an introduction to stochastic processes, students will look at the random walk process. Historically this is an important process, and was initially motivated as a model for how the wealth of a gambler varies over time (initial analyses focused on whether there are betting strategies for a gambler that would ensure they won).
The focus will then be on the most important class of stochastic processes, Markov processes (of which the random walk is a simple example). Students will discover how to analyse Markov processes, and how they are used to model queues and populations.
Modern statistics is characterised by computer-intensive methods for data analysis and development of new theory for their justification. In this module students will become familiar with topics from classical statistics as well as some from emerging areas.
Time series data will be explored through a wide variety of sequences of observations arising in environmental, economic, engineering and scientific contexts. Time series and volatility modelling will also be studied, and the techniques for the analysis of such data will be discussed, with emphasis on financial application.
Another area the module will focus on is some of the techniques developed for the analysis of multivariates, such as principal components analysis and cluster analysis.
Lastly,students will spend time looking at Change-Point Methods, which include traditional as well as some recently developed techniques for the detection of change in trend and variance.
We set our fees on an annual basis and the 2025/26 entry fees have not yet been set.
As a guide, our fees in 2024/25 were:
Home | International |
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£9,250 | £28,675 |
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.
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 2024, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2025 have not yet been set.
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.
In addition to travel and accommodation costs, while you are studying abroad, you will need to have a passport and, depending on the country, there may be other costs such as travel documents (e.g. VISA or work permit) and any tests and vaccines that are required at the time of travel. Some countries may require proof of funds.
In addition to possible commuting costs during your placement, you may need to buy clothing that is suitable for your workplace and you may have accommodation costs. Depending on the employer and your job, you may have other costs such as copies of personal documents required by your employer for example.
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.
Home fees are subject to annual review, and may be liable to rise each year in line with UK government policy. International fees (including EU) are reviewed annually and are not fixed for the duration of your studies. Read more about fees in subsequent years.
We will charge tuition fees to Home undergraduate students on full-year study abroad/work placements in line with the maximum amounts permitted by the Department for Education. The current maximum levels are:
International students on full-year study abroad/work placements will be charged the same percentages as the standard International fee.
Please note that the maximum levels chargeable in future years may be subject to changes in Government policy.
Details of our scholarships and bursaries for students starting in 2025 are not yet available. You can use our scholarships for 2024-entry applicants as guidance.
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.
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.
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