MSc Business Analytics and AI

This module introduces students to the fundamentals of Python programming for business analytics. It assumes no prior coding experience and progressively builds programming capability through practical business data problems. Students learn to handle, explore, and visualise data, applying descriptive analytics to generate insights that inform strategic decision-making. The module establishes a foundation for advanced study in predictive analytics, machine learning, and AI.

This module provides students with an understanding of how artificial intelligence drives innovation and market creation across industries. Students will explore how AI technologies transform business models, customer engagement, and strategic decision-making, while considering the ethical and governance implications of responsible AI adoption.

This module provides the statistical foundation for Business Analytics, guiding students from basic data description to credible causal inference. Through practical Python-based analysis, students learn how to explore data, build and assess models, and make evidence-based decisions that separate correlation from causation.

This module introduces students to advanced decision analysis principles and their integration with AI-driven business analytics. It explores how organisations can leverage data and AI technologies to make informed, strategic decisions under conditions of uncertainty and complexity. Students will learn to frame decision problems, apply quantitative and qualitative techniques, and incorporate  prescriptive analytics into decision-making processes. The inclusion of prescriptive analytics for this particular module caters for the other modules that explain data that informs what has happened or what is forecast to happen. With this post-data and forecast of knowledge, this module explains through prescriptive analytics what the data states should be done in data-orientated decision making.

The module emphasises practical application through hands-on work in IT labs, using real-world datasets and case studies. Students will develop skills in designing AI-enabled solutions, evaluating trade-offs, and communicating insights effectively to diverse stakeholders through visualisation and storytelling. Ethical, legal, and governance considerations in AI adoption are embedded throughout, ensuring graduates are prepared to implement responsible and impactful decision strategies in business contexts.

This module provides students with insights into how analytics is used in practice across business and scientific industries. There will be a large focus on employability skills and preparing students to the world of work in analytics.

The module is an introduction to Predictive Analytics and Machine Learning (ML) for business. It aims to equip students with knowledge, understanding, and hands-on skills required for employing predictive analytics and ML in business.

The project is the final stage of the Master’s programme in Business Analytics and Artificial Intelligence. Building on the analytical, technical, and reflective skills developed in the core modules, this capstone module requires students to identify and undertake research or applied work appropriate to master’s level study in the field of business analytics and AI. With academic supervision, students will work towards the completion of a dissertation or applied project. Supervisor hours will be allocated with their supervisor, outside of the dedicated learning hours, on a regular basis through email, telephone, and/or video call.

Working in collaboration with the supervising tutor, students will define the precise area to be researched or applied. They will develop and apply critical research skills, including methodological justification and subject-specific considerations. This module will enable students to prepare a project that applies advanced analytics and AI concepts, tools, and techniques to address a significant organisational or sectoral issue or opportunity.

Students will be expected to work independently to identify and manage their own project to completion, reflecting on their learning journey in this final capstone module. Those in relevant employment will have the opportunity to use their own workplace as a source for the project focus, ensuring practical relevance and impact.