QS World University Rankings by Subject 2024
Lancaster University has been ranked 51-70 for Data Science and Artificial Intelligence in the QS World University Rankings by Subject 2024.
51-70 for Data Science and Artificial Intelligence in the QS World University Rankings by Subject 2024
State-of-the-art labs and immersive data suite
Supported by the Data Science Institute with over £50M funding to support UK research
The MSc Data Science has been designed by leading academics and industry experts to deliver an education that matches the needs of employers. The world of data science and AI is rapidly evolving, and so are we. Lancaster University’s Data Science Institute plays a leading role in UK data science research, which means we continually refine and update our curriculum to make sure you are learning the most modern tools and techniques. As demonstrated by our hundreds of alumni, we develop graduates with the skills and expertise to become professionals that design, test and implement solutions to drive innovation and business decision making. You will gain the confidence to apply data science techniques that enable companies to use artificial intelligence to gain insights and make better decisions.
This flexible Master’s allows you to select modules, guided by pathways, to develop an enhanced understanding of modern data science technologies. The pathways are:
Who is this programme for?
For those interested in gaining insights from data to provide solutions, this Master’s in Data Science is open to anyone with an undergraduate degree, providing you have a good grasp of statistical analysis, ideally to A level standard. You may be a recent graduate or you may wish for a career change.
Looking ahead to employability
The strength of Lancaster’s MSc Data Science is producing graduates with demonstrable experience on their CV. Throughout the course you will network and interact with data science industry professionals through guest lectures, group projects and professional skills workshops. Crucially, you will have the opportunity to work on a real-time project during your 14-week placement with one of our industry partners, choosing techniques and approaches from your data science toolkit to provide answers to their questions.
You will also:
What to expect
Your learning and practical experience is split into three parts.
Study the core modules that span the breadth of data science including the fundamentals of statistics and programming in Python; modern machine learning; and artificial intelligence. This term is essential in providing the foundations for you to advance your knowledge and technical skills in your chosen pathway.
Choose your specialist pathway, selecting optional modules that align with your interests and career goals.
Apply the knowledge and skills you've gained with a 14-week placement or dissertation, either within industry or as part of an academic research project. Our students really value this experience, with many offered jobs at the end. You will develop your ability to formulate a project plan, gather and analyse data, interpret your results, and present findings in a professional environment. This research will be an opportunity to bring together everything you have learnt over the year, expand your problem-solving abilities and manage a significant project.
Three things our students would like you to know
A 2:1 Hons degree (UK or equivalent) in any discipline, provided that the applicant has had exposure to quantitative methods such as statistics, or mathematical modelling.
Applicants with a 2:2 Hons degree in Computer Science, Mathematics, Statistics, Engineering, Physics, Life Sciences, Economics, Finance and Linguistics are strongly encouraged to apply, provided that their undergraduate degree included at least one module in statistics, mathematics, mathematical modelling or machine learning (e.g. Neural networks).
If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.
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
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.
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.
The main goal of this module is to equip you with essential Python programming skills and foundational mathematical concepts crucial for AI and Data Science. Through hands-on learning, you'll develop the ability to solve real-world problems, process complex data sets, and apply key mathematical techniques like probability and matrix operations. Formative assessments will support your learning, leading to a final practical assessment that prepares you for advanced studies. Perfect for those new to computing or mathematics, this module sets the stage for your success in AI and Data Science.
The main goal of this module is to explore the essence of AI and Data Science, their origins, and their roles in solving real-world challenges. You'll delve into the duties and skills of data professionals, emphasising effective communication and ethical considerations. The module also covers the legal and societal impacts of AI, while promoting teamwork through hands-on projects that tackle AI and Data Science challenges. Supported by industry talks, you'll learn to formulate problem statements, select appropriate methods, and communicate findings effectively, preparing you for a successful career in this dynamic field.
A large part of the Master's involves completing the industry or research related project. This starts with the students selecting an industry or research partner, undertaking a placement in June - July, and then submitting a written dissertation of up to 20,000 words in early September.
This is primarily a self-study module designed to provide the foundation of the main dissertation, at a level considered to be of publishable quality. The project also offers students the opportunity to apply their technical skills and knowledge on current world class research problems and to develop an expert knowledge on a specific area.
The topic of the project will vary from student to student, depending on the data science specialism (eg computing may involve the design of a system, while specialism in data analytics, health or environment, are likely to be more applied, perhaps focusing upon inherent data structure and processes).
This module provides a practical introduction to statistical learning from model training to deployment. The student will learn about optimisation as a means for model training; the statistical principles and methods that underpin model selection, and the application of classification and regression models to real data problems.
A variety of supervised learning models and their estimation will be covered, including linear models and their connection to kernel based methods; feed-forward neural networks; and tree based models. Tree based models will be extended to their use in random forests and gradient boosted models. In addition topics relevant to big-data problems including dimensionality reduction variable selection; and stochastic gradient descent will be covered.
Students will be introduced to the widely-used statistical computing package R, which will be the primary tool for data analysis and modelling in this module. In addition to learning how to use R effectively and efficiently, students will also be encouraged to compare and contrast with their existing or developing knowledge of general-purpose languages such as Python.
This module aims to equip students with a deep understanding of the rapidly evolving field of Artificial Intelligence (AI), focusing on both cutting-edge methods and applications. You'll explore AI's role in areas like cybersecurity, ethical considerations, human-AI interaction, and emerging technologies like Quantum AI. The module prepares you to apply AI to real-world challenges or pursue research in innovative AI techniques, ensuring alignment with current industry and academic trends. Additionally, you'll develop skills in implementing AI solutions, making ethical decisions, and effectively communicating your findings in professional settings.
This module will immerse students in advances in ecological research and conservation that provide key skills for working as an ecologist in the era of Big Data. Teaching is delivered by world-leading researchers who are experts in biodiversity from coral reefs to tropical forests and freshwater lakes, ensuring a deep understanding of how data science can generate actionable insights for global conservation. Throughout the course, students will gain an understanding of the principles behind data science tools and techniques, which will help them develop both a fundamental understanding of the natural world and urgent solutions to the global biodiversity crisis. The curriculum is dynamic and will adapt annually to address contemporary issues.
Indicative topics include:
1. Why do we need data science for biodiversity?
2. Big Data: advantages, challenges and solutions
3. Automating species ID for citizen science (AI, machine learning)
4. Tracking animal movements underwater (acoustic telemetry)
5. Quantifying 3D habitat structure (photogrammetry)
6. Biodiversity soundscapes in a noisy world (bioacoustics)
7. The ecological role of colour (machine learning)
8. Scaling up: from animal behaviour at global species distributions (geospatial)
9. Extended reality for ocean empathy (XR)
10. Responsible data science for biodiversity
Workshops offer in-depth exploration of advanced topics, such as AI’s role in predictive ecology, cutting-edge ecological technologies, biodiversity beyond species richness, data visualization strategies, and innovative data-driven solutions to the biodiversity crisis. Our interdisciplinary approach blends ecological and computational perspectives, preparing you for in-demand roles in the evolving ecology sector.
Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore, forecasting plays a vital role in enhancing managerial decision-making.
After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.
This module introduces students to the fundamental principles of Geographical Information Systems (GIS) and Remote Sensing (RS) and shows how these complementary technologies may be used to capture/derive, manipulate, integrate, analyse and display different forms of spatially-referenced environmental data. The module is highly vocational with theory-based lectures complemented by hands-on practical sessions using state-of-the-art software (ArcGIS & ERDAS Imagine).
In addition to the subject-specific aims, the module provides students with a range of generic skills to synthesise geographical data, develop suitable approaches to problem-solving, undertake independent learning (including time management) and present the results of the analysis in novel graphical formats.
The main goal of this module is to explore the development and optimisation of intelligent, autonomous agents capable of outperforming human capabilities in various tasks. You'll learn the core concepts of intelligent agents, from fundamental AI paradigms like rule-based systems, planning, and learning, to advanced decision-making algorithms. The module emphasises both classical and modern AI techniques, showing how traditional ideas continue to inspire powerful innovations. Through practical exercises, you'll design, implement, and validate AI algorithms, enhancing your skills in problem-solving, critical thinking, and translating complex algorithms into functional code.
The module provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. It covers the entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the module is classification and uses the R programming language.
The main goal of this module is to equip students with the expertise to design and implement robust technology platforms essential for effective AI and Data Science systems. You’ll explore a range of technologies like Hadoop, Spark, and PyTorch Distributed, learning how to select, configure, and optimise them for large-scale, high-performance computing. The module focuses on principles of system architecture, distributed machine learning, and scalability, with real-world case studies and industry insights. By the end, you'll be able to architect and engineer data-driven systems, critically evaluate enterprise-scale IT solutions, and implement distributed machine learning models effectively.
The main goal of this module is to provide students with cutting-edge knowledge in natural language processing (NLP) as applied in both industry and research. You'll learn how to collect, clean, and analyse language data at scale, using methods ranging from rule-based to deep learning techniques. The module covers key applications like machine translation, sentiment analysis, and summarisation, alongside discussions on language models, ethics, and bias in NLP. By the end, you'll be able to create scalable solutions for language data challenges, understand current NLP research trends, and enhance your skills in independent study, critical thinking, and effective communication.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
This module will equip the student with the understanding and skills to use statistical methods to solve current ecological challenges in a robust manner. By gaining familiarity with both frequentist and Bayesian inference, students will learn to translate statistical uncertainty into decision-making processes
Students will have the opportunity to experiment with different ecological data types. By examining case study data sets through the lens of visualisation and descriptive analysis, students will learn why specific statistical models are required for the different ecological data types.
Examples of challenges that will be investigated include species abundance, and the effects of heterogeneity on this; the use of demographic parameters to model population dynamics; application of statistical models for spatial data; and modelling emerging data types such as citizen science data, environmental DNA and multi-species data.
The knowledge and skills gained in this module are highly sought-after by conservation charities and non-governmental organisations.
The purpose of this course is to understand and use mathematical models in making strategic, tactical, and operational logistics decisions. Emerging logistical concepts will be introduced and the associated mathematical modelling needs will be discussed. Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed. The main topics covered are: facility location, network design, warehousing, vehicle routing and scheduling, and Terminal (airport) capacity management.
Depending on students need and level of programming skill, the computer workshops will focus on either solver languages (e.g. GAMS, AMPL, MPL) and/or programming interfaces (PYOMO, CPLEX Concert, Gurobi Python Interface).
Location | Full Time (per year) | Part Time (per year) |
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Home | £14,140 | £7,070 |
International | £30,310 | £15,155 |
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 2025, the fee is £40 for undergraduates and research students and £15 for students on one-year courses.
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.
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.
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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.
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.