Operational Research (OR) is a field that develops and applies analytical methods to improve decision-making about managerial problems. In this module, we focus on:

• translating real-world business problems into quantitative models
• applying quantitative methods and software tools to analyse them.

The module introduces three approaches: optimisation, decision analysis and simulation. You will apply these approaches to examples and case studies, illustrating how you can use the approaches in practice. You will also learn how to:

• implement models in MS Excel and Python
• generate insights and formulate clear recommendations and conclusions based on quantitative models.

This module introduces the topic of forecasting in business organisations. You will engage with issues concerned with forecasting model building in regression and its extensions, building on material covered earlier in your degree programme. You will then consider extrapolative forecasting methods, particularly Exponential Smoothing, and Machine Learning or Artificial Intelligence methods, particularly Neural Networks.

You will engage with a case study about forecasting in organisations, which embeds all the methods covered in this module. The module ends by analysing how forecasting is applied to operations and how forecasting can be improved in an organisational context.

Python is a simple yet very powerful high-level computer programming language that is extremely popular today. It is widely used in many scientific areas for data exploration and is the preferred programming language in a range of modern organisations.

This module will cover a range of the most commonly used algorithms and techniques, including:

• sorting and searching algorithms
• data manipulation
• visualisation
• the use of package extensions such as pandas and numpty.

The module will also cover applications in Business Analytics, such as simulation and optimisation.

At the heart of many management problems lies data that needs to be described, analysed and interpreted. This module will equip you with essential skills to analyse and interpret data effectively, which are core capabilities for any business analyst. You will explore key topics including:

• probability
• sampling
• hypothesis testing
• regression analysis
• model building
• model selection.

You will learn to make effective use of computer software, R. Each topic is motivated by real-world business problems, with a strong emphasis on practical application. The skills learned on this module form the foundation for more advanced modules across the Business Analytics MSc programme.

You will conduct an in-depth research project on a topic linked to your MSc programme, applying your knowledge to a real business problem. This project is your opportunity to:

• explore an area that interests you
• develop a structured research approach
• contribute original insights to a business.

You will employ your knowledge of research methods, problem-scoping techniques, and academic literature to define and investigate your chosen topic. Whether you focus on solving a practical issue or advancing understanding in a specific field, you will apply what you have learned to deliver a professional research project.

Throughout the process, you will work closely with a supervisor, developing your ability to:

• engage professionally in academic discourse
• manage feedback
• communicate findings.

You will consolidate your learning by developing academic research, writing and communication skills and demonstrate your ability to work independently to deliver value in a real organisation.

You will independently investigate a scholarly question that you design linked to real business problems. Typically, the process begins with a thorough literature review to identify a clear research gap. You then craft a relevant research question and select an appropriate methodology—qualitative, quantitative, or mixed methods—to generate new evidence.

Many students conduct primary fieldwork, gathering data through interviews, surveys, experiments or archival analysis. Other students integrate secondary datasets with advanced analytical techniques. We expect you to demonstrate critical engagement with theory, methodological soundness and ethical research practice.

You will develop your knowledge and understanding of professional academic engagement in your interactions with your supervisor and your reporting of your outputs and research discussion. The resulting dissertation defends your findings, situates them in existing scholarship and articulates their practical and theoretical implications. By producing this research, you showcase your capacity for original, discipline-relevant inquiry.

This module introduces the fundamental methods and approaches from the interrelated areas of data mining, statistical/machine learning, and intelligent data analysis. It covers the entire data analysis process, including:

• formulating a project objective
• developing an understanding of the available data and other resources
• statistical modelling
• performance assessment.

The module focuses on classification, and you will use the R programming language.

Many firms possess abundant information about existing and potential customers. Although data acquisition technologies are becoming increasingly widespread, it is not trivial to transform this wealth of accumulated data into valuable and actionable insights.

• How can a marketing manager track and use consumer data to create better value for their firms and customers?
• How can they leverage data analytics to gain customer insights and make informed marketing mix decisions?
• How can they use various types of data (numerical, text, image) to shape their marketing strategies?

In this module, you will address these questions with the help of applied quantitative models. Topics covered include:

• customer segmentation
• text mining
• image analytics
• marketing mix modelling.

Your learning is supported by workshops, which will offer opportunities to build your quantitative modelling skills.

You will explore how organisations achieve excellence in both their internal operations and across their supply chains. This module introduces tools, techniques and strategic approaches that help drive performance, innovation and long-term value. You will learn about:

• lean principles
• Six Sigma
• the theory of constraints
• statistical process control.

You will explore supplier development and sustainability performance in supply chains and consider how these apply across industries and sectors. Through case studies, group work and analysis, you will examine how firms can continuously improve and work more effectively with their supply chain partners. You will consider how external standards, stakeholder expectations and sustainability goals shape operational excellence in today’s organisations. This module equips you with practical methods and a critical mindset to identify improvement opportunities, evaluate supply chain capabilities and support responsible, high-performing operations.

Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software can now solve many industrial-scale problems to proven optimality.

This module enables you to apply optimisation techniques to business problems. It introduces you to different problem formulations and algorithmic methods that guide decision-making in businesses and other organisations.

Many systems in business and industry operate under uncertainty, with apparent randomness. Ignoring uncertainty when making decisions about how these systems operate can lead to poor system performance. This module introduces important methods for modelling and optimising systems under uncertainty.

Simulation is a powerful technique for gaining insight into complex stochastic models in which analytical tools become intractable. Reinforcement learning is a state-of-the-art method which uses reward signals and feedback loops to search for near-optimal decision-making policies in dynamic decision-making problems.

The possible applications of these methods include:

• healthcare services
• manufacturing
• inventory management
• dynamic pricing.

You will develop the analytical and practical skills needed to analyse and improve system performance in a wide variety of contexts.