phd applied linguistics lancaster university

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Linguistics

Entry requirements.

Master's degree (UK or equivalent) in a subject relevant to the proposed research, with an average of at least 60% for UK MAs.

2:1 Hons degree (UK or equivalent) in Applied Linguistics or a related subject, for example Linguistics, Foreign Languages, Education.

If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.

Additional Requirements

As part of your application you will also need to provide a viable research proposal. Guidance for writing a research proposal can be found on the department's PhD study page.

Months of entry

January, April, July, October

Course content

Complete your PhD with world-leading experts in Linguistics and be part of a thriving department that is ranked 3rd in the world for Linguistics (QS world rankings 2024).

We welcome proposals for doctoral research across any area of linguistics that falls within our expertise. These include but are not limited to:

• Cognitive Linguistics
• Corpus Linguistics
• Critical Discourse Analysis
• First Language Acquisition
• Forensic Linguistics
• Historical/Diachronic Linguistics
• Language Testing
• Linguistic Typology
• Literacy Studies
• Phonetics and Phonology
• Psycholinguistic
• Sociolinguistics
• Speech Science and Technology
• Second Language Learning and Teaching

If you pursue doctoral research in linguistics at Lancaster you will join a research-intensive department with a large community of doctoral students and a number of different research groups. This provides a rich, stimulating and supportive environment in which to conduct your research with lots of opportunities to share your research with others. The department also hosts an annual post-graduate conference in Linguistics and Language Teaching.

You will work independently under the guidance of one or more supervisors or as part of a team. You may also benefit from additional research methods and academic skills training provided by the faculty Research Training Programme (offered on-site and by distance learning).

You will be fully supported throughout your studies as you move through key milestones in the PhD process.

• Option to study full time (36 months) or part time
• Study on campus in Lancaster or by distance
• Opportunity to undertake additional research methods and academic skills training provided by the faculty Research Training Programme
• Submit in traditional or alternative (by research article) format

Information for international students

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 6.5 in reading and writing and 6.0 in listening and speaking. We also consider other English language qualifications.

We do not offer pre-sessional English language programmes for this programme.

Contact: Admissions Team +44 (0) 1524 592032 or email [email protected]

Fees and funding

Learn more at our fees and funding page.

Qualification, course duration and attendance options

• Campus-based learning is available for this qualification

Course contact details

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Course type

Qualification, university name, postgraduate linguistics courses at lancaster university.

8 courses available

Customise your search

Select the start date, qualification, and how you want to study

Related subjects:

• Linguistics
• Linguistic Studies
• Translation

• Course title (A-Z)
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• Price: high - low
• Price: low - high

Applied Linguistics and TESOL MA

Lancaster university.

Take your MA in Applied Linguistics and TESOL in one of the best Linguistics Departments in the world. We are placed 3rd in the QS World Read more...

• 1 year Full time degree: £11,960 per year (UK)
• 2 years Part time degree: £5,980 per year (UK)

Linguistics PhD

Complete your PhD with world-leading experts in Linguistics and be part of a thriving department that is ranked 3rd in the world for Read more...

• 3 years Full time degree: £4,786 per year (UK)
• 48 months Part time degree: £2,393 per year (UK)

Language and Linguistics MA

Take your MA in Language and Linguistics in one of the best Linguistics Departments in the world. We are placed 3rd in the QS World Read more...

Forensic Linguistics and Speech Science MSc

Take your MSc in Forensic Linguistics and Speech Science in one of the best Linguistics Departments in the world. We are placed 3rd in the Read more...

Corpus Linguistics (Distance) MA

Take your MA in Corpus Linguistics in one of the best Linguistics Departments in the world. We are placed 3rd in the QS World Rankings Read more...

• 2 years Distance without attendance degree: £5,850 per year (UK)

Linguistics (by thesis and coursework) PhD

Take your PhD with one of the best Linguistics departments in the world. We are ranked 3rd in the world for Linguistics in the QS World Read more...

• 36 months Full time degree: £4,786 per year (UK)
• 60 months Part time degree: £2,393 per year (UK)

TESOL (Teaching English to Speakers of Other Languages) with Corpus Linguistics (Distance) MA

Take your Master's in TESOL in one of the best Linguistics Departments in the world. We are placed 3rd in the QS World Rankings 2024. This Read more...

• 24 months Distance without attendance degree: £5,850 per year (UK)

Corpus Linguistics (Distance) PgCert

Take your Postgraduate Certificate in Corpus Linguistics in one of the best Linguistics Departments in the world. We are placed 3rd in the Read more...

• 1 year Distance without attendance degree: £3,900 per year (UK)

Course type:

• Distance learning

Qualification:

Related subjects:.

ESRC Centre for Corpus Approaches to Social Science

Fully Funded PhD Studentship in Linguistics at Lancaster University

The Department of Linguistics and English Language at Lancaster University, UK is delighted to offer a fully funded PhD studentship (UK home-rate fee) as part of the UKRI-funded project, ‘Public Discourses of Dementia: Challenging Stigma and Promoting Personhood’.

The aim of this project is to challenge dementia stigma by changing the ways in which dementia is discussed in the public domain, focusing in particular on the mainstream media, public health bodies and charities in the UK. This project will explore the language and imagery that are used to represent dementia in texts produced by these organisations and compare these against those used by the general public to talk about dementia on social media and by people living with dementia when giving first-hand accounts of their lives with the syndrome. The project team will work closely with media, public health and charity organisations to implement changes to communicative practices around dementia in ways that challenge stigma and promote personhood, through the development of communication guidelines and the delivery of training to public communicators from charities, the media, research and public health. Crucially, the project team will collaborate closely with people with dementia themselves to ensure that their voices are heard and valued not only in future public discourse but also in the research process itself. The Project team will be led by Dr Gavin Brookes and supported by Professor Tony McEnery. The project will also be supported by an externally based academic advisory committee. You can read more about the project here: https://www.lancaster.ac.uk/news/new-research-fellowship-team-to-examine-language-about-dementia .

We are seeking to recruit a strong and enthusiastic PhD candidate with demonstrable knowledge and interest in at least two of the following areas: corpus linguistics; multimodal (visual) discourse analysis; health communication. The successful candidate should hold a Master’s degree in linguistics or a related area. However, clearly outstanding and particularly well-suited candidates who have completed a first degree will be considered. Candidates may be invited to interview (dates and format to be confirmed).

This studentship is open to students eligible for UK Home fees only (for more information, see: https://www.lancaster.ac.uk/study/postgraduate/postgraduate-courses/research-fees/ ).

If you are interested in this opportunity, by way of application please email Gavin Brookes ( [email protected] ) with the following:

• A short CV (max 2 pages) outlining your qualifications, achievements and publications (if applicable)
• A personal statement describing your suitability to the project (focusing in particular on your knowledge and skills, including relating to methodological approaches) (max. 500 words)
• An example of your writing (either an assignment or chapter from a dissertation)

Start date: October 2022 (or as soon as possible thereafter)

Duration: 3 years

Application deadline: 31 st May 2022

Funding information: A stipend of £15,609 for 22/23, which will increase each year in line with the UK Research and Innovation (UKRI) rate, plus Home-rate fee allowance of £4,500 (with automatic increase to UKRI rate each year). The successful candidate will also have access to a generous training and conference budget.

Gavin Brookes

Linguistics PhD Lancaster University

• On campus - Main Site
• Oct 1, 2024 Full-time - 36 Months
• Oct 1, 2024 Part-time - 48 Months

Key Course Facts

• View programme website
• Admission advice for international students

Course Description

We welcome proposals for doctoral research across any area of linguistics that falls within our expertise. These include but are not limited to:

• Cognitive Linguistics
• Corpus Linguistics
• Critical Discourse Analysis
• First Language Acquisition
• Forensic Linguistics
• Historical/Diachronic Linguistics
• Language Testing
• Linguistic Typology
• Literacy Studies
• Phonetics and Phonology
• Psycholinguistic
• Sociolinguistics
• Speech Science and Technology
• Second Language Learning and Teaching

If you pursue doctoral research in linguistics at Lancaster you will join a research-intensive department with a large community of doctoral students and a number of different research groups. This provides a rich, stimulating and supportive environment in which to conduct your research with lots of opportunities to share your research with others. The department also hosts an annual postgraduate conference in Linguistics and Language Teaching.

You will work independently under the guidance of one or more supervisors or as part of a team. You may also benefit from additional research methods and academic skills training provided by the faculty Research Training Programme (offered on-site and by distance learning).

You will be fully supported throughout your studies as you move through key milestones in the PhD process.

Option to study full time (36 months) or part time (48 months)

Study on campus in Lancaster or by distance

Opportunity to undertake additional research methods and academic skills training provided by the faculty Research Training Programme

Submit in traditional or alternative (by research article) format

Assessment Methods

Entry requirements / admissions, requirements for international students / english requirements.

IELTS academic test score (similar tests may be accepted as well)

• Graduate Degrees
• Undergraduate Degrees

Master's degree (UK or equivalent) in a subject relevant to the proposed research, with an average of at least 60% for UK MAs.

2: 1 Hons degree (UK or equivalent) in Applied Linguistics or a related subject, for example Linguistics, Foreign Languages, Education.

As part of your application you will also need to provide a viable research proposal. Guidance for writing a research proposal can be found on the department's PhD study page.

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 6.5 in reading and writing and 6.0 in listening and speaking. We also consider other English language qualifications.

Average student cost of living in the UK

London costs approx 34% more than average, mainly due to rent being 67% higher than average of other cities. For students staying in student halls, costs of water, gas, electricity, wifi are generally included in the rental. Students in smaller cities where accommodation is in walking/biking distance transport costs tend to be significantly smaller.

University Rankings

Positions of lancaster university in top uk and global rankings., about lancaster university.

Lancaster University was established in 1964 and is located in the rural setting of Lancaster. The campus provides a series of amenities, including banks and a post office. Academically, all of this university’s courses and programmes are accredited and approved by MARP, ensuring that the curriculums delivered here are set at a high standard. In terms of facilities, students have access to a variety of study and recreational spaces intended to improve their wellbeing, such as Meditation and Tai Chi classes, and Woodland walks.

List of 452 Bachelor and Master Courses from Lancaster University - Course Catalogue

Student composition of Lancaster University

Where is this programme taught.

Similar courses

Ask a question to lancaster university, request course information or send a question directly to lancaster university.

Cognitive Linguistics Research Group

Linguistics and English Language, Lancaster University

The Department of Linguistics and English Language at Lancaster University is one of the largest and most diverse centres for cognitive linguistics research in the UK. Our research addresses a broad range of topics in cognitive linguistics, including metaphor and figurativity, construction grammar, and usage-based approaches to language acquisition and language change.

Much of our research is concerned with the language-cognition interface .  Under the auspices of the Perception and Learning Lab , our research investigates the embodied and neurological bases of language, the influence of language on memory and perception, and the cognitive consequences of bilingualism.

Our research also takes a more applied form where we have particular strengths in cognitive linguistic approaches to text and discourse analysis , including in stylistics and critical discourse analysis.  Researchers in the Discourse and Text Research Group investigate the forms and functions of metaphor across a range of fields and genres, including literature, health communication, business communication, science communication, political discourse and advertising.  Our research in this area also explores other aspects of meaning construction in discourse from the perspectives of cognitive grammar and discourse space theory.

View our publications and projects.

Research Methods

Our research in cognitive linguistics makes use of a broad range of methods, often in combination.  This includes introspection and qualitative approaches to data analysis.  However, we are also committed to empirical methods in cognitive linguistics.  Our research makes use of corpus linguistic tools to investigate attested patterns of language usage.  We also conduct experimental research that makes use of psycholinguistic methods including eye-tracking, reaction-time measurements and electroencephalography (EEG), as well as offline judgement and perception tasks.

Media and Impact

We are committed to sharing our research with the public and other stakeholders.  Our research regularly features in the media .  Our researchers have written articles for The Conversation, given TED talks, and featured in television and radio programmes about language.  We are frequently interviewed in media contexts when a linguistic issue becomes a news item. We are also committed to working with non-academic partners to ensure our research has real-world relevance.  Our researchers have worked with various organisations in the public and third sectors, including the National Health Service, charities and campaign groups.

Environment

We have hosted major events relating to cognitive linguistics research, including the 5th UK Cognitive Linguistics Conference  and the 5th Implicit Learning Seminar .  Our research group is made up of faculty, PhD students and visiting researchers (see People).

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Data Science

• Entry year 2025 or 2024
• Duration Full time 1 year, Part time 2 - 3 years

Top reasons to study with us

51-70 for Data Science and Artificial Intelligence in the QS World University Rankings by Subject 2024

World Leading Research

Theoretical and Practical Study

Develop your skills and expertise in artificial intelligence (AI) and data science with our flexible Master's programme that allows you to select modules, guided by pathways, to develop an enhanced understanding of modern data science technologies. Designed in co-operation with industry, you will learn how data science and AI work together with real industry-specific projects and through guest lectures from professional data scientists. Upon graduation, you will have the confidence to apply data science techniques that enable companies to use artificial intelligence to gain insights and make better decisions. This Master's has been pivotal in launching the careers of hundreds of in demand data scientists with a high starting salary.

What will I study?

In the first term, you will study 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.

In the second term, you will choose your specialist pathway, selecting optional modules that align with your interests and career goals. Specialist pathways include those in data engineering, biodiversity, and business intelligence.

Apply the knowledge and skills you've gained in the previous 2 terms 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, and many find that it builds confidence and adds weight to their CV. 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.

Your department

• Computing and Communications Faculty of Science and Technology
• Telephone +44 (0)1524 592032

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 .

Discover Data Science at Lancaster

Discover why our MSc Data Science programme was the top choice for our students, from its specialised pathways to its wide range of industrial placement opportunities.

Industry placements

Just some of our students who have enjoyed a successful industry placement.

WWL NHS Trust "The whole process was great, everyone at the office welcomed me with open arms and helped me with anything I wanted. I was provided total flexibility to use any methods I desired, it was really MY project. I was gifted with the reward of seeing the predictions implemented and seeing the work I had done presented and discussed in front of executives from trusts around the north-west."

AstraZeneca "This experience was unique and valuable since it allowed me to learn from experts in academia and industry and taught me how to approach a data science project from a business perspective. The placement made me easier to be incorporated into the industry as a future data scientist by providing a clear understanding of a data scientist’s role."

The Behavioural Insights Team "I learned an enormous amount over course of the project as well as putting into practice and developing the technical skills and knowledge learned on the MSc course, and being able to contribute to a substantial project in this way has increased my confidence in my own ability. The experience has proved to be especially valuable in securing work after completion of the course."

Uniper Energy "Personally, the placement provided a transition from the academic environment to the industrial environment and gave me the opportunity to prepare for the job interviews that came afterwards. The fact that there was a short industrial placement after the MSc was highly appreciated by most of the interviewers and offered a great means of describing how my data scientist skills were applied and enhanced."

Placement hosts

Our students have taken their industry placements in a wide range of businesses, from global corporations to local SMEs. These are just some of the places that have provided students with opportunities to gain experience in real-world data science.

Your future employability

Our graduates find careers in a range of data-related positions, including as data scientists, statisticians, and data analysts. Starting salaries are also competitive with high earning potential.

Our programme opens the door to many possible careers, including Data scientist or data science consultant; Financial modeller; Clinical and pharmaceutical analyst; or Data technologies specialist. Our alumni have gone on to data science roles at Amazon, Deloitte, Santander, Bloomberg, The Office of National Statistics, The Environment Agency and more.

Lancaster University is committed to providing its entrepreneurial students with the support they need to launch their enterprises. We understand that you may wish to start your own company as soon as possible. We offer you the opportunity to incorporate an Enterprise Project into your course, instead of a industry placement. You may prefer to complete your studies with a project that will form the basis of a future enterprise, and we help you to develop your ideas.

As a student at Lancaster, you will gain access to our excellent careers service, offering lifetime support, help and friendly advice. We offer lifetime support, help and advice to all of our students. This service includes one-to-one support and advice on work experience, employability skills and careers.

Entry requirements

Academic requirements.

A 2:1 Hons degree (UK or equivalent) in any discipline, provided that the applicant has had exposure to one or more modules of statistics, mathematical modelling, or machine learning.

Applicants with a 2:2 Hons degree in any discipline, with a 2:1 in at least one module in statistics, mathematics, mathematical modelling, or machine learning (e.g. neural networks), are strongly encouraged to apply.

Students from Computer Science, Mathematics, Statistics, Engineering, Physics, Life Sciences, Economics, Finance and Linguistics and many other disciplines have successfully completed this programme.

Students have successfully completed the course with undergraduate degrees in Computer Science, Mathematics, Statistics, Engineering, Physics, Life Sciences, Economics, Finance, Linguistics, and others.

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 [email protected]

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 modules accordion

Fundamentals of data science and artificial intelligence.

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.

Introduction to Artificial Intelligence 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.

MSc Dissertation

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).

Statistical Learning

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.

optional modules accordion

Advanced topics in artificial intelligence.

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.

Applied Data Science for Biodiversity

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.

Forecasting

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.

Geoinformatics

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.

Intelligent Agents and Autonomous Systems

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.

Intelligent Data Analysis and Visualisation

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.

Large scale platforms for AI and Data Analysis

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.

Natural Language Processing and Language Models

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 and Heuristics

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.

Statistical Ecology

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.

Transportation and Logistics Analytics

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).

Fees and funding

General fees and funding information

Additional fees and funding information accordion

Additional costs.

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.

Application fees and tuition fee deposits

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.

What is my fee status?

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 .

Fees in subsequent years

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 .

We also have other, more specialised scholarships and bursaries - such as those for students from specific countries.

Browse Lancaster University's scholarships and bursaries .

Similar courses

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 PgCert
• Data Science PgDip
• Health Data Science MSc
• Health Data Science PhD

Mathematics and Statistics

• Mathematics PhD
• Medical Statistics MSc
• Natural Sciences MSc by Research
• Natural Sciences PhD
• Social Statistics PhD
• Statistics MSc
• Statistics PGDip
• Statistics PhD
• Statistics PhD (Integrated)
• Statistics and Artificial Intelligence MSc
• Statistics and Epidemiology PhD
• Statistics and Operational Research MRes
• Statistics and Operational Research (STOR-i) PhD

Postgraduate Master's Loan

The government offers a number of loans to UK and some EU national students wishing to study for a Master's degree.

Postgraduate digital open event - 6 November

Join us online and let us tell you about postgraduate study at Lancaster and how to apply.

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 .

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