BSc (Hons)Applied Artificial Intelligence

Core modules

Programming for AI and Data Science

This module introduces you to the fundamental principles of programming within the context of artificial intelligence (AI) and data science. You'll focus on developing core programming skills using Python and will learn how to write, test, and debug code, work with data structures, and use key Python libraries to process, analyse, and visualise data. The module also introduces the role of cloud computing platforms in supporting AI and data science workflows, particularly for handling computationally intensive tasks and large datasets.

Fundamentals of Computing and Programming

This module provides the essential foundations of computing and programming for students entering software engineering and computer science. Through practice‑led activities such as live coding, pair programming, debugging labs, and guided exercises, you'll be introduced to how computers are structured and operate, how data is represented and processed, and how programs are designed, implemented and tested using a modern programming language.

AI Fundamentals and Professional Practice

This module provides an introduction to artificial intelligence alongside the core professional practices required for success in modern computing disciplines. You'll develop foundational AI literacy by exploring classical AI methods, intelligent agents, search strategies, data fundamentals and introductory machine-learning concepts. Through hands-on exploration of datasets, notebook-based activities and conceptual modelling tasks, you'll gain an applied understanding of how AI systems operate, where they are effective and what limitations or risks they present.

Data, Tools and Applications

This module introduces you to the fundamental concepts of data, tools, and data handling practices within the context of artificial intelligence and digital systems. You'll focus on how data is represented, stored, organised, and managed using a range of commonly used tools and platforms, including spreadsheets, notebooks, and introductory database systems. You'll also explore the data lifecycle, from data acquisition and storage to preparation and structured use, gaining practical experience in working with real-world datasets.

Core modules

Applied AI and Machine Learning

This module introduces the core principles and practical applications of applied artificial intelligence and machine learning. You'll begin by exploring descriptive data analysis and exploratory data analysis (EDA) techniques to examine datasets, identify patterns, and prepare data for machine learning tasks. You'll then examine key machine learning approaches, including supervised learning methods such as regression and classification, and unsupervised learning techniques including clustering and dimensionality reduction.

Data Analytics and Visualisation

This module will equip you with the technical and analytical skills required to transform raw data into actionable business intelligence. You'll address the growing industry demand for professionals who can analyse complex datasets using both statistical frameworks and computational tools and will move beyond static data viewing to a practice-led exploration of the analytics lifecycle, learning how to interpret results and justify analytical decisions within professional enterprise contexts.

Data Engineering and Big Data

This module provides a comprehensive exploration of modern data engineering infrastructures, balancing the theoretical principles of distributed systems with the practical demands of scalable data pipelines. Designed to build on knowledge gained in your first year, you'll shift focus from basic data handling to the professional implementation and troubleshooting of complex high-volume storage architectures and automated workflows. Following a 'pipeline-first' approach, you will master the technical agility required to transform raw, unstructured data into high-quality, machine-learning-ready datasets.

AI Ethics, Governance and Business Strategies

At the end of this module, you'll be able to demonstrate a thorough understanding of the foundations of AI and ethics. Throughout the module, you'll develop a rationale for the application of the economics of AI, generative AI in the enterprise, competitive advantage, and sustainability within the context of an organisation scenario, recognising the implications of the modern global ethical, legal and social frameworks.

Core modules

Advanced Data Science and Deep Learning

This module provides a comprehensive exploration of modern data-driven intelligence, balancing the rigorous statistical foundations of advanced data science with the transformative power of deep learning. Designed for the final year of study, it shifts focus from basic implementation to the critical evaluation and synthesis of complex data pipelines and neural architectures. You will engage in intensive, practice-led learning to solve non-linear, real-world problems, ensuring you graduate as a 'creator of knowledge and critical solutions'.

Final Major Project

In this module you will manage your own project, from choosing a topic to producing the final report. You set aims and objectives, keep a project plan, and maintain documentation that records your research and development work. You'll be assigned a supervisor to guide the process, confirm the project scope, and approve ethical requirements.

Industrial Consulting and Entrepreneurship

In this module you'll gain practical experience working with employers, industry speakers, entrepreneurs, and external organisations. You will collaborate in development teams on an industry‑set brief, applying digital and technology skills while developing project management, communication, and professional behaviours. Alongside consulting practice, you will explore entrepreneurial thinking by identifying opportunities, generating innovative solutions, and understanding how value is created within technical and digital contexts.

Optional modules

Please note: Not all optional modules are guaranteed to run each year.

Generative AI and NLP

This module introduces the core principles and practical applications of natural language processing and generative artificial intelligence. You'll begin by exploring text preprocessing techniques to clean and prepare language data for analysis before examining core NLP techniques, including tokenisation and vectorisation, alongside classical models for text analysis and pattern extraction. Building on this foundation, you'll then explore generative AI concepts, working with large language models to produce and analyse text outputs.

AI and Autonomous Systems

This module provides an advanced exploration of artificial intelligence and autonomous systems, focusing on the design, evaluation and responsible deployment of intelligent agents operating in dynamic environments. You will develop the ability to engineer AI‑driven behaviours, integrate perception and decision‑making pipelines, and evaluate the safety, reliability and ethical implications of autonomous technologies.

Agentic AI and MLOps

This module explores the shift from static AI models to autonomous agentic systems capable of perception, reasoning, and action. You will investigate the transition from traditional AI to agentic architectures, including reactive and deliberative systems. The curriculum covers the integration of Large Language Models (LLMs) with external tools, memory systems, and multi-agent orchestration frameworks. You'll also master the operationalisation of these systems through industry-standard ML Ops practices, such as building automated CI/CD pipelines, experiment tracking, model versioning, and real-world monitoring for model drift and safety.