Build in-demand data analysis and problem-solving skills through real-world projects and practical learning, preparing you for careers across AI, data and 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.
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 The University of Law 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
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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 (15 credits)This module serves as a technical equaliser, accelerating your skills from foundational principles to sophisticated object-oriented and functional programming paradigms. You’ll master the Fifth Teammate protocol, a structured framework for governing agentic AI tools within the development lifecycle. By auditing, interrogating and debugging AI generated outputs, you’ll ensure security and code correctness. Beyond core syntax, you’ll gain essential skills in algorithmic efficiency, version control and collaborative Git workflows, preparing you to operate as a critically aware programmer in modern, AI-augmented team.
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Foundations of Statistics and Data Inference (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 forwards inference. You’ll develop critical competencies in linear algebra, probability distributions and 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. You’ll master the mechanics of gradient descent, distance metrics and Bellman equations, gaining an intimate knowledge of algorithm behaviour, strengths and limitations. By implementing NumPy based models and later mastering scikit-learn, you’ll learn to reason critically about the bias variance trade-off and model optimisation. |
Ethics, Law, and Society (15 credits)Develop your technical expertise within 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. You’ll navigate complex regulatory environments, including the GDPR and the EU AI Act, while exploring the “alignment problem" in autonomous systems. By studying algorithmic fairness, liability attribution and the environmental impact of large scale compute, you’ll develop the critical literacy to build compliant, responsible technology. |
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. You’ll gain hands on expertise in distributed processing using Apache Spark, containerisation with Docker and orchestration via Kubernetes. By adopting infrastructure as code and MLOps practices, you’ll learn to build, deploy and maintain resilient AI pipelines within AWS or GCP ecosystems.
|
Data Mining, Visualisation, and Actionable Intelligence (15 credits)Master the capacity to extract non obvious patterns 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 mining and network analysis. You’ll develop 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, you’ll learn 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. You’ll gain hands on experience building, training and fine tuning Large Language Models (LLMs) using methodologies like LoRA and RLHF. By exploring sequence modelling, word embeddings and encoder-decoder architectures, you’ll develop the expertise to 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, you’ll move beyond theoretical models to focus on agile project management, end-to-end architecture and robust deployment. You’ll master the vital skill of scoping technical requirements from non-technical stakeholders and translating them into actionable engineering sprints. By integrating MLOps, NLP engines and visual dashboards into unified applications, you’ll learn 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. You’ll master critical research methods, requirement engineering and autonomous project management frameworks within an independent development lifecycle. By designing, implementing and debugging your custom technical solution, you’ll ensure robust code correctness under conditions of complex uncertainty. Beyond software execution, you’ll gain essential skills in risk assessment, academic write-ups and milestone reporting, preparing you to operate as 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‑aware transferable skills.
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For part-time students, modules may vary per semester and academic year depending on your individual choices.
| Compulsory modules |
Programming and AI Orchestration (15 credits)This module serves as a technical equaliser, accelerating your skills from foundational principles to sophisticated object-oriented and functional programming paradigms. You’ll master the Fifth Teammate protocol, a structured framework for governing agentic AI tools within the development lifecycle. By auditing, interrogating and debugging AI generated outputs, you’ll ensure security and code correctness. Beyond core syntax, you’ll gain essential skills in algorithmic efficiency, version control and collaborative Git workflows, preparing you to operate as a critically aware programmer in modern, AI-augmented team.
|
Foundations of Statistics and Data Inference (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 forwards inference. You’ll develop critical competencies in linear algebra, probability distributions and 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. You’ll gain hands on expertise in distributed processing using Apache Spark, containerisation with Docker and orchestration via Kubernetes. By adopting infrastructure as code and MLOps practices, you’ll learn to build, deploy and maintain resilient 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. You’ll master the mechanics of gradient descent, distance metrics and Bellman equations, gaining an intimate knowledge of algorithm behaviour, strengths and limitations. By implementing NumPy based models and later mastering scikit-learn, you’ll learn to reason critically about the bias variance trade-off and model optimisation. |
Data Mining, Visualisation, and Actionable Intelligence (15 credits)Master the capacity to extract non obvious patterns 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 mining and network analysis. You’ll develop 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, you’ll learn to surface hidden relationships and pitch actionable insights with authority.
|
Ethics, Law, and Society (15 credits)Develop your technical expertise within 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. You’ll navigate complex regulatory environments, including the GDPR and the EU AI Act, while exploring the “alignment problem" in autonomous systems. By studying algorithmic fairness, liability attribution and the environmental impact of large scale compute, you’ll develop the critical literacy to build compliant, responsible technology. |
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‑aware 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. You’ll gain hands on experience building, training and fine tuning Large Language Models (LLMs) using methodologies like LoRA and RLHF. By exploring sequence modelling, word embeddings and encoder-decoder architectures, you’ll develop the expertise to 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, you’ll move beyond theoretical models to focus on agile project management, end-to-end architecture and robust deployment. You’ll master the vital skill of scoping technical requirements from non-technical stakeholders and translating them into actionable engineering sprints. By integrating MLOps, NLP engines and visual dashboards into unified applications, you’ll learn 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. You’ll master critical research methods, requirement engineering and autonomous project management frameworks within an independent development lifecycle. By designing, implementing and debugging your custom technical solution, you’ll ensure robust code correctness under conditions of complex uncertainty. Beyond software execution, you’ll gain essential skills in risk assessment, academic write-ups and milestone reporting, preparing you to operate as an authoritative, critically aware innovator in advanced tech environments.
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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 The University of Law 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.
Course dates
Application and booking deadlines vary by intake - take a look at our key application and enrolment deadline dates 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 Professional Practice | |
| London | £12,000 |
| Outside London | £11,350 |
All fees above include a deposit amount of £250.
The University of Law offers a wide range of scholarships and bursaries which 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 £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 Professional Practice | |
| 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 in 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 The University of Law
If you would like to study MSc Data Science and Artificial Intelligence you can apply directly with us.
Apply now
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