Develop a thorough understanding of the in-demand artificial intelligence (AI) and natural language processing (NLP) technologies used to translate complex data into actionable business strategies with Lancaster University’s online MSc Data Science.Develop a thorough understanding of the in-demand artificial intelligence (AI) and natural language processing (NLP) technologies used to translate complex data into actionable business strategies with Lancaster University’s online MSc Data Science.Develop a thorough understanding of the in-demand artificial intelligence (AI) and natural language processing (NLP) technologies used to translate complex data into actionable business strategies with Lancaster University’s online MSc Data Science.
Learn how to decode data that drives innovation and impact with a programme that’s designed specifically to meet the needs of aspiring data scientists. The 28-month, part-time online master’s in data science emphasises career readiness through hands-on coursework, empowering you to tackle real-world data challenges that accurately reflect day-to-day workplace responsibilities.
As a data science graduate student, you’ll study within Lancaster University’s Data Science Institute, a leading research institution at the forefront of data science and AI innovations across the UK and abroad. The programme’s modern-day curriculum is continually informed and refined by the Institute’s cutting-edge research. Through project work and individual assignments, you will become fluent working with Python, Hadoop and other applications that are vital for implementing data science solutions across various professional settings.
Whether you are looking to upskill existing computer science skills or pivot into a new career, you will emerge ready to unlock the power of data and confidently ascend in your role as a data science professional.
Lancaster University consistently ranks among the top academic institutions in the UK: number 10 in the Complete University Guide 2025, number 12 in the Times and Sunday Times Good University Guide 2025, and number 11 in the The Guardian University Guide 2025.
Academics from the Data Science Institute, School of Computing and Communications, School of Mathematical Sciences, and the Security Lancaster institute collaborate to deliver a curriculum that reflects the interconnected and ever-evolving nature of data science.
A blend of live and asynchronous learning affords you the ability to complete coursework when it’s convenient for you, while connecting with teaching staff and peers in real time to discuss coursework and develop lasting relationships.
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Lancaster University seeks applicants who are looking to build a career in data science. The online data science programme was built specifically for working professionals, so you can accomplish your career goals on your time. Our forward-thinking curriculum covers the theoretical and practical applications you need to become a knowledgeable, confident and prepared data science leader.
To meet the entry requirements, you must have a 2:2 Honours degree (UK or equivalent) in any discipline, provided that you have had exposure to quantitative methods such as statistics or mathematical modelling.
The final deadline for the September 2025 cohort is 11 August 2025.
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Develop the technical, analytical and communications skills necessary to become a subject matter expert in data science, AI, NLP and machine learning. Our online data science programme seamlessly integrates theory and applications from statistics, computing, mathematics and data security – reflecting the interconnected nature of data science. You will master data modelling to extract and analyse various types of data, and be able to distill complex data sets into digestible, actionable insights for non-technical audiences. Modules emphasise the importance of devising ethically sound AI algorithms and processes and how to account for potential bias in data outputs.
The MSc Data Science dissertation final project affords students the opportunity to work alongside one of our industry partners to complete hands-on projects, solving real-world problems and demonstrating learning from the programme in a corporate setting.
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.
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.
On successful completion of this module, you’ll learn to understand cross-validation of sample splitting into calibration training and validation samples, as well as be able to move to handling regression problems for large data sets via variable reduction methods such as the Lasso and Elastic Net. You’ll also gain understanding on a variety of classification methods including logistic and multinomial logistic models, regression trees, random forests, and bagging and boosting. Additionally, you’ll have opportunities to examine classification methods that will culminate in neural networks presented as generalised linear modelling extensions and learn to analyse big data using K-means, PAM and CLARA, followed by mixture models and latent class analysis.
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.
With this module, you’ll be exposed to 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.
The main goal of this module is to equip you 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 of the module, you’ll be able to architect and engineer data-driven systems, critically evaluate enterprise-scale IT solutions and implement distributed machine learning models effectively.
A large part of the programme involves completing an industry- or research-related project. This starts with the students selecting an industry or research partner, undertaking the project work itself and then submitting a written dissertation of up to 20,000 words. This is primarily a self-study activity offering students the opportunity to apply their technical skills and knowledge on current world class research problems and to develop expert knowledge on a specific area. Divided into two distinct parts, the Foundations element will cover requirements capture, basic specification, literature review, exploratory data analysis, facilitating the formulation of a detailed project plan and discussions on anticipated findings. The Implementation element will cover the execution of methods, production of results, the writing of a detailed discussion of the results and synthesis into an overall dissertation, followed by the presentation of the results to the academic supervisor and industry or research partner.
Prerequisites
There are no prerequisites for Introduction to AI and Data Science, Fundamentals of AI and DS and Statistical Learning, but Introduction to AI and Data Science must be taken first to introduce the programme.
Natural Language Processing and Language Models and Intelligent Agents and Autonomous Systems require understanding of analytical techniques and, in some cases, machine learning, therefore they would need to be taken after Fundamentals of AI and DS and Statistical Learning.
Data science is the spark that ignites new insights, opportunities and business growth in the twenty-first century. Gain the technical and communication skills today’s data scientists need to drive innovation and sound decision-making within organisations.
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We believe in supporting the whole you – academically, socially and personally – while you are pursuing your degree. You’ll find our team is here to support you from day one at the University – and even after you’ve graduated.
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Our close-knit academic community is powered by a dedicated teaching staff who go beyond lecturing – they work closely with students throughout the programme and beyond. They actively participate in cutting-edge research through the University’s Data Science Institute, bringing fresh insights directly to your learning experience. Professional data scientists regularly join as guest lecturers, sharing how they’ve navigated complex projects, overcome challenges and built distinguished careers.
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Go from big data to big career opportunities with the online MSc Data Science from Lancaster University. Capitalise on high earning potential: professional data scientists can earn an average annual salary ranging from £32,000 to £82,500 for experienced professionals, according to the National Careers Service.
Our online data science programme emphasises real-world projects so you can build a strong portfolio that demonstrates your expertise to potential employers. Whether you are targeting technology, health care, finance, government or another industry, you’ll gain a broad range of skills and the expertise to pursue a data science career that aligns with your personal and professional interests.
Some careers you may pursue with a master’s in data science include:
Connect foundational data science principles to emerging research and real-world applications with Lancaster University’s career-focused MSc Data Science.
