From business and finance to health and medicine, from infrastructure to societal studies, data science plays a vital role in all aspects of the modern world. Our PG Diploma programme will ensure you have an advanced level of skills, knowledge, and experience in this rapidly expanding, highly in-demand field to achieve your career aspirations.
Studying for a PG Diploma in Data Science at Lancaster will provide you with the perfect environment to develop an expertise in the discipline. You will develop and consolidate your fundamental skills in both computing and statistics before progressing onto one of our specialist pathways which allow you to acquire and enhance advanced technical skills, while gaining professional knowledge that will support and advance your future career choices. The specialist pathway modules promote both an enhanced understanding of modern data science technologies and confidence in the application of data science within the business intelligence, health, environmental, or societal sectors. They are taught by researchers from across the University, who have expertise in the topics relevant to each of the pathways.
The fundamentals followed by pathway structure enables you to tailor the programme either to suit your current career development needs or to take the opportunity to upskill and take your career in a new direction.
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.
Career options
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.
Enterprise education
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.
Lifetime support
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
2:1 Hons degree (UK or equivalent) in Statistics, Computer Science or similar.
We may also consider non-standard applicants, please contact us for information.
If you have studied outside of the UK, we would advise you to check our list of international qualifications before submitting your application.
English Language Requirements
We may ask you to provide a recognised English language qualification, dependent upon your nationality and where you have studied previously.
We normally require an IELTS (Academic) Test with an overall score of at least 6.5, and a minimum of 6.0 in each element of the test. We also consider other English language qualifications.
Delivered in partnership with INTO Lancaster University, our one-year tailored pre-master’s pathways are designed to improve your subject knowledge and English language skills to the level required by a range of Lancaster University master’s degrees. Visit the INTO Lancaster University website for more details and a list of eligible degrees you can progress onto.
Course structure
You will study a range of modules as part of your course, some examples of which are listed below.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research. Not all optional modules are available every year.
Core
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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.
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
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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)
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.
After introducing the topic of forecasting in business organisations, issues concerned with forecasting model building in regression and its extensions are presented, building on material covered earlier in the course(s). Extrapolative forecasting methods, in particular Exponential Smoothing, are then considered, as well as Machine Learning / Artificial Intelligence methods, in particular Neural Networks. All methods are embedded in a case study in forecasting in organisations. The course ends by analysing how forecasting is applied to operations and how forecasting can best be improved in an organisational context.
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 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).
There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.
Specific additional costs for studying at Lancaster are listed below.
College fees
Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small College Membership Fee which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.
For students starting in 2025, the fee is £40 for undergraduates and research students and £15 for students on one-year courses.
Computer equipment and internet access
To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.
The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.
For most taught postgraduate applications there is a non-refundable application fee of £40. We cannot consider applications until this fee has been paid, as advised on our online secure payment system. There is no application fee for postgraduate research applications.
For some of our courses you will need to pay a deposit to accept your offer and secure your place. We will let you know in your offer letter if a deposit is required and you will be given a deadline date when this is due to be paid.
The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your fee status.
If you are studying on a programme of more than one year’s duration, tuition fees are reviewed annually and are not fixed for the duration of your studies. Read more about fees in subsequent years.
Scholarships and bursaries
You may be eligible for the following funding opportunities, depending on your fee status and course. You will be automatically considered for our main scholarships and bursaries when you apply, so there's nothing extra that you need to do.
Unfortunately no scholarships and bursaries match your selection, but there are more listed on scholarships and bursaries page.
We offer an excellent range of learning environments, which include traditional lectures, computer laboratories, and workshops. We are also committed to providing timely feedback for all submitted work and projects.
Assessment varies across modules, allowing students to demonstrate their capabilities in a range of ways. Assessment can include laboratory reports, essays, exercises, literature reviews, short tests, poster sessions, oral presentations and formal examinations.
Community
We have an excellent relationship with our students and alumni. We have received praise for our ambition, positivity and supportiveness. By providing a variety of support methods, accessible at all stages of your degree, we strive to give our students the best opportunity to fulfil their potential and attract the very best opportunities for a successful career. Our academics are welcoming and helpful. We will assign an academic advisor to you who can offer advice and recommended reading. Our open-door policy has been a popular feature among our students. We believe in encouraging and inspiring our data scientists of the future.
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.
