Build in-demand data analysis and problem-solving skills through real-world projects and practical鈥痩earning, preparing you for careers across AI,鈥痙ata鈥痑nd technology sectors.
Master of Science
MSc Data Science and Artificial Intelligence
Explore the power of big data with our MSc Data Science and Artificial Intelligence course, combining data science, machine learning and AI to unlock insights that drive innovation.
105 days until application deadline for full-time and part-time October 2026 domestic students
Course length
from 12 months to 24 months
Study mode
Full-time, Full-time (Weekend), Part-time or Part-time Weekend
Method
On Campus
Overview
Why choose our MSc Data Science and Artificial Intelligence course?
- Navigate the world of big data with confidence – Leverage statistics, machine learning and AI to transform vast, complex datasets into meaningful insights and opportunities.
- Learn what drives business value - Find the 10% of data that offers true business value, using advanced AI algorithms.
- The Professional Practice advantage - Choose the two-year route to include a 12-month industry placement, giving you a full year of real-world experience.
- Turn statistics into actionable business wisdom - Work on live briefs to develop data driven solutions for real employers.
- Become a high-value asset in any global sector - Combine the technical power of Data Science with the predictive power of AI and learn the skills needed to make you highly employable in the tech industry.
About our MSc Data Science and Artificial Intelligence course
As organisations increasingly rely on data to drive innovation and decision making, our MSc Data Science and Artificial Intelligence course at 91桃色 develops the technical expertise, analytical mindset and practical experience needed to succeed in this fast-growing field.
Combining data science, machine learning and artificial intelligence, the programme explores how to extract meaningful insights from complex and large-scale datasets. Through industry relevant projects, live briefs and assessments, you’ll apply your knowledge to real-world business challenges while developing skills in programming, data analysis and intelligent system development.
You’ll also have opportunities to engage with employers through competitions, hackathons and project showcases, helping you build professional networks and demonstrate your capabilities. If you choose the two-year Academic and Professional Practice route, you’ll gain valuable real-world experience through a 12-month industry placement.
Supported by access to AWS Academy and Network Academy courses, you’ll graduate with the confidence and industry focused skills needed for careers across data, AI and technology sectors.
Possible study locations and start dates
MSc Data Science and Artificial Intelligence
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MSc Data Science and Artificial Intelligence with Academic and Professional Practice
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MSc Data Science and Artificial Intelligence
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MSc Data Science and Artificial Intelligence with Academic and Professional Practice
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MSc Data Science and Artificial Intelligence
| Full-time | Full-time (Weekend) | Part-time | Part-time Weekend |
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MSc Data Science and Artificial Intelligence with Academic and Professional Practice
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Course Content
Modules
Semester 1
| Compulsory modules |
Programming and AI Orchestration鈥(15 credits)This module serves as a technical equaliser, accelerating your skills from foundational principles to sophisticated object-oriented and functional programming paradigms.鈥痀ou’ll鈥痬aster the Fifth Teammate protocol, a structured framework for governing agentic AI tools within the development lifecycle. By auditing,鈥痠nterrogating鈥痑nd debugging AI generated outputs,鈥痽ou’ll鈥痚nsure security and code correctness. Beyond core syntax,鈥痽ou’ll鈥痝ain essential skills in algorithmic efficiency, version鈥痗ontrol鈥痑nd collaborative Git workflows, preparing you to鈥痮perate鈥痑s a critically aware programmer in modern, AI-augmented team.鈥
|
Foundations of Statistics and Data Inference鈥(15 credits)Build a rigorous mathematical foundation to transform raw data into actionable evidence. This module accelerates your understanding of the first principles governing modern algorithms, moving beyond descriptive statistics to master both backwards and鈥痜orwards鈥痠nference.鈥痀ou’ll鈥痙evelop critical competencies in linear algebra, probability鈥痙istributions鈥痑nd Bayesian inference, enabling you to model uncertainty and update beliefs with data.
|
Machine Learning Principles (15 credits)Develop a principled understanding of machine learning by constructing classic algorithms from scratch before transitioning to industry frameworks. This module prioritises mathematical comprehension over surface level tool use.鈥痀ou’ll鈥痬aster the mechanics of gradient descent, distance鈥痬etrics鈥痑nd Bellman equations, gaining an intimate knowledge of algorithm behaviour,鈥痵trengths鈥痑nd limitations. By implementing NumPy based models and later mastering scikit-learn,鈥痽ou’ll鈥痩earn to reason critically about the bias variance trade-off and model optimisation.鈥 |
Ethics, Law, and Society (15 credits)Develop your technical鈥痚xpertise鈥痺ithin the legal and ethical frameworks governing modern computing. This module challenges you to confront the consequences of algorithmic decision making, from data privacy breaches to structural inequalities.鈥痀ou’ll鈥痭avigate complex regulatory environments, including the GDPR and the EU AI Act, while exploring the鈥“alignment problem" in autonomous systems. By studying algorithmic fairness, liability鈥痑ttribution鈥痑nd the environmental impact of鈥痩arge scale鈥痗ompute,鈥痽ou’ll鈥痙evelop the critical literacy to build compliant, responsible technology.鈥 |
Semester 2
| Compulsory modules |
Distributed Processing and Data Engineering (15 credits)Master the critical infrastructure of modern data science by transitioning from local scripting to scalable, cloud native architectures. This module equips you to design and implement robust data pipelines capable of handling massive enterprise datasets.鈥痀ou’ll鈥痝ain hands on鈥痚xpertise鈥痠n distributed processing using Apache Spark, containerisation with Docker鈥痑nd orchestration via Kubernetes. By adopting infrastructure as code and鈥疢LOps鈥痯ractices,鈥痽ou’ll鈥痩earn to build,鈥痙eploy鈥痑nd鈥痬aintain鈥痳esilient AI pipelines within AWS or GCP ecosystems.
|
Data Mining, Visualisation, and Actionable Intelligence (15 credits)Master the capacity to extract鈥痭on obvious鈥痯atterns from complex datasets and translate findings into compelling visual narratives. This module bridges the gap between technical mining and executive decision making, exploring advanced EDA, association rule鈥痬ining鈥痑nd network analysis.鈥痀ou’ll鈥痙evelop a deep understanding of human visual cognition, using perceptual principles to design interactive dashboards that reduce cognitive load. By mastering data storytelling and graph theory,鈥痽ou’ll鈥痩earn to surface hidden relationships and pitch actionable insights with authority.
|
Deep Learning and Natural Language Processing (15 credits)Accelerate into the state-of-the-art of modern AI by bridging the gap between classical linguistic analysis and contemporary deep learning. This module guides you from neurons to context, starting with the mathematics of backpropagation before mastering the breakthrough mechanics of Transformers and self-attention.鈥痀ou’ll鈥痝ain hands on experience building, training and鈥痜ine tuning鈥疞arge Language Models (LLMs) using methodologies like鈥疞oRA鈥痑nd RLHF. By exploring sequence modelling, word embeddings and encoder-decoder architectures,鈥痽ou’ll鈥痙evelop the鈥痚xpertise鈥痶o design and optimise advanced NLP pipelines.
|
Applied Data Architecture and Integration (15 credits)This module transforms isolated algorithmic development into professional software engineering by challenging you to synthesise disparate AI components into a single, cohesive ecosystem. Adopting a hackathon style ethos,鈥痽ou’ll鈥痬ove beyond theoretical models to focus on agile project management, end-to-end鈥痑rchitecture鈥痑nd robust deployment.鈥痀ou’ll鈥痬aster the vital skill of scoping technical requirements from non-technical stakeholders and translating them into actionable engineering sprints. By integrating鈥疢LOps, NLP engines and visual dashboards into unified applications,鈥痽ou’ll鈥痩earn to build, stress-test and deliver user-centric, production-ready AI systems within a resilient professional lifecycle.
|
Semesters 1-3
| Compulsory modules |
MSc Project in Computer Science (60 credits)The MSc Project in Computer Science accelerates your skills from theoretical foundations to the execution of a sophisticated software engineering or AI project.鈥痀ou’ll鈥痬aster critical research methods, requirement鈥痚ngineering鈥痑nd autonomous project management frameworks within an independent development lifecycle. By designing,鈥痠mplementing鈥痑nd debugging your custom technical solution,鈥痽ou’ll鈥痚nsure robust code correctness under conditions of complex uncertainty. Beyond software execution,鈥痽ou’ll鈥痝ain essential skills in risk assessment, academic鈥痺rite-ups鈥痑nd milestone reporting, preparing you to鈥痮perate鈥痑s an authoritative, critically aware innovator in advanced tech environments.
|
Professional Development (0 credits)This module develops the professional and employability skills required to succeed in a rapidly evolving, technology driven job market. You’ll explore potential career pathways and learn to identify employer expectations through practical, interactive workshops. Key focus areas include CV and application writing, interview techniques, professional networking through LinkedIn and the development of teamwork, emotional intelligence and business鈥慳ware transferable skills.
|
For part-time students, modules may vary per semester and academic year depending on your individual choices.
| Compulsory modules |
Programming and AI Orchestration鈥(15 credits)This module serves as a technical equaliser, accelerating your skills from foundational principles to sophisticated object-oriented and functional programming paradigms.鈥痀ou’ll鈥痬aster the Fifth Teammate protocol, a structured framework for governing agentic AI tools within the development lifecycle. By auditing,鈥痠nterrogating鈥痑nd debugging AI generated outputs,鈥痽ou’ll鈥痚nsure security and code correctness. Beyond core syntax,鈥痽ou’ll鈥痝ain essential skills in algorithmic efficiency, version鈥痗ontrol鈥痑nd collaborative Git workflows, preparing you to鈥痮perate鈥痑s a critically aware programmer in modern, AI-augmented team.鈥
|
Foundations of Statistics and Data Inference鈥(15 credits)Build a rigorous mathematical foundation to transform raw data into actionable evidence. This module accelerates your understanding of the first principles governing modern algorithms, moving beyond descriptive statistics to master both backwards and鈥痜orwards鈥痠nference.鈥痀ou’ll鈥痙evelop critical competencies in linear algebra, probability鈥痙istributions鈥痑nd Bayesian inference, enabling you to model uncertainty and update beliefs with data.
|
Distributed Processing and Data Engineering (15 credits)Master the critical infrastructure of modern data science by transitioning from local scripting to scalable, cloud native architectures. This module equips you to design and implement robust data pipelines capable of handling massive enterprise datasets.鈥痀ou’ll鈥痝ain hands on鈥痚xpertise鈥痠n distributed processing using Apache Spark, containerisation with Docker鈥痑nd orchestration via Kubernetes. By adopting infrastructure as code and鈥疢LOps鈥痯ractices,鈥痽ou’ll鈥痩earn to build,鈥痙eploy鈥痑nd鈥痬aintain鈥痳esilient AI pipelines within AWS or GCP ecosystems.
|
Machine Learning Principles (15 credits)Develop a principled understanding of machine learning by constructing classic algorithms from scratch before transitioning to industry frameworks. This module prioritises mathematical comprehension over surface level tool use.鈥痀ou’ll鈥痬aster the mechanics of gradient descent, distance鈥痬etrics鈥痑nd Bellman equations, gaining an intimate knowledge of algorithm behaviour,鈥痵trengths鈥痑nd limitations. By implementing NumPy based models and later mastering scikit-learn,鈥痽ou’ll鈥痩earn to reason critically about the bias variance trade-off and model optimisation.鈥 |
Data Mining, Visualisation, and Actionable Intelligence (15 credits)Master the capacity to extract鈥痭on obvious鈥痯atterns from complex datasets and translate findings into compelling visual narratives. This module bridges the gap between technical mining and executive decision making, exploring advanced EDA, association rule鈥痬ining鈥痑nd network analysis.鈥痀ou’ll鈥痙evelop a deep understanding of human visual cognition, using perceptual principles to design interactive dashboards that reduce cognitive load. By mastering data storytelling and graph theory,鈥痽ou’ll鈥痩earn to surface hidden relationships and pitch actionable insights with authority.
|
Ethics, Law, and Society (15 credits)Develop your technical鈥痚xpertise鈥痺ithin the legal and ethical frameworks governing modern computing. This module challenges you to confront the consequences of algorithmic decision making, from data privacy breaches to structural inequalities.鈥痀ou’ll鈥痭avigate complex regulatory environments, including the GDPR and the EU AI Act, while exploring the鈥“alignment problem" in autonomous systems. By studying algorithmic fairness, liability鈥痑ttribution鈥痑nd the environmental impact of鈥痩arge scale鈥痗ompute,鈥痽ou’ll鈥痙evelop the critical literacy to build compliant, responsible technology.鈥 |
Professional Development (0 credits)This module develops the professional and employability skills required to succeed in a rapidly evolving, technology driven job market. You’ll explore potential career pathways and learn to identify employer expectations through practical, interactive workshops. Key focus areas include CV and application writing, interview techniques, professional networking through LinkedIn and the development of teamwork, emotional intelligence and business鈥慳ware transferable skills.
|
Deep Learning and Natural Language Processing (15 credits)Accelerate into the state-of-the-art of modern AI by bridging the gap between classical linguistic analysis and contemporary deep learning. This module guides you from neurons to context, starting with the mathematics of backpropagation before mastering the breakthrough mechanics of Transformers and self-attention.鈥痀ou’ll鈥痝ain hands on experience building, training and鈥痜ine tuning鈥疞arge Language Models (LLMs) using methodologies like鈥疞oRA鈥痑nd RLHF. By exploring sequence modelling, word embeddings and encoder-decoder architectures,鈥痽ou’ll鈥痙evelop the鈥痚xpertise鈥痶o design and optimise advanced NLP pipelines.
|
Applied Data Architecture and Integration (15 credits)This module transforms isolated algorithmic development into professional software engineering by challenging you to synthesise disparate AI components into a single, cohesive ecosystem. Adopting a hackathon style ethos,鈥痽ou’ll鈥痬ove beyond theoretical models to focus on agile project management, end-to-end鈥痑rchitecture鈥痑nd robust deployment.鈥痀ou’ll鈥痬aster the vital skill of scoping technical requirements from non-technical stakeholders and translating them into actionable engineering sprints. By integrating鈥疢LOps, NLP engines and visual dashboards into unified applications,鈥痽ou’ll鈥痩earn to build, stress-test and deliver user-centric, production-ready AI systems within a resilient professional lifecycle.
|
MSc Project in Computer Science (60 credits)The MSc Project in Computer Science accelerates your skills from theoretical foundations to the execution of a sophisticated software engineering or AI project.鈥痀ou’ll鈥痬aster critical research methods, requirement鈥痚ngineering鈥痑nd autonomous project management frameworks within an independent development lifecycle. By designing,鈥痠mplementing鈥痑nd debugging your custom technical solution,鈥痽ou’ll鈥痚nsure robust code correctness under conditions of complex uncertainty. Beyond software execution,鈥痽ou’ll鈥痝ain essential skills in risk assessment, academic鈥痺rite-ups鈥痑nd milestone reporting, preparing you to鈥痮perate鈥痑s an authoritative, critically aware innovator in advanced tech environments.
|
If you’re undertaking the Academic and Professional Practice year, you can choose between the following projects in your second year:
| Modules |
| Computing Work Placement |
| Consultancy Projects |
Teaching and Assessment
How you'll learn
You’ll learn through a combination of lectures, live lab sessions, seminars, live coding exercises, presentations, virtual industry guest talks and our virtual learning environment. All study materials are supplied online and include programme handbooks, module and unit guides, e-books and online reference materials.
Assessment
Assessments are designed to meet the programme and module learning outcomes and are both formative and summative. The formative assessments include the preparation and feedback from teaching sessions such as lectures, seminars, workshops and presentations. The main methods of assessment are portfolios, coursework reports and presentations delivered both live and pre-recorded.
The course is delivered across three terms for full-time students or six terms for part-time and part-time weekend students, combining face-to-face teaching with flexible online study.
For students studying the course with the Academic and Professional Practice element, you'll have a second year of study that features either the Professional Practice Placement of Consultancy Project or the Extended Research Project.
Our Student Journey Advisors at 91桃色 will support and advise you throughout your studies with us, ensuring you have the best possible experience.
Our Academic Coaches will offer guidance throughout your course as well as assistance and advice as required during your time with us. They'll also be on hand to help you develop your plans for your future career.
Key dates
Application deadlines for our October 2026 intake
- Full-time and part-time domestic students: Monday 28 September 2026 at 9am (GMT).
- Full-time weekend and part-time weekend domestic students: Monday 5 October 2026 at 9am (GMT).
If you require deadlines for international students or further information, take a look at our key application and enrolment deadline dates page for more information.
Fees and Applying
Course Fees
| Location | Fees |
| 2026/27 course fees (from 1 July 2026) | |
| London | £10,900 |
| Outside London | £10,300 |
| 2-year programme with Academic and Professional Practice | |
| London | £12,000 |
| Outside London | £11,350 |
All fees above include a deposit amount of £250.
91桃色 offers a wide range of鈥scholarships and bursaries鈥痺hich makes studying more affordable than ever. You could also be eligible for a Postgraduate Loan.
If you're an alumnus of the University, you may be eligible to receive our鈥£1,000 General Alumni Discount.
| Location | Fees |
| 2026/27 course fees (from 1 July 2026) | |
| London | £17,500 (or £16,000 including a £1,500 International Bursary*) |
| Outside London | £16,500 (or £15,000 including a £1,500 International Bursary*) |
| 2-year programme with Academic and Professional Practice鈥 | |
| London | £19,250 |
| Outside London | £18,150 |
All fees above include a deposit amount of £250.
*Terms and conditions apply. Visit our International Scholarships and Bursaries page for more details.
Entry Requirements
2:2
Undergraduate DegreeUK Entry Requirements
An undergraduate degree鈥痠n any subject at 2:2 or above, or equivalent qualifications in a computing-related subject.
Applicants who have previously studied computing or a computing related course are encouraged to review the modules of this programme to ensure they are happy with the progression the MSc Data Science and Artificial Intelligence would provide.
International Entry Requirements
An English Language level equivalent to IELTS 6.0 or above with a minimum of 5.5 in each component.
Applying
Apply to 91桃色
If you would like to study MSc Data Science and Artificial Intelligence you can apply directly with us.
Apply now
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