Operations management is how organisations create and deliver goods and services. And since the production of goods and/or services is the reason why organisations exist, it is clear that the effective and efficient management of operations is a central concern for all managers, regardless of the size or sector location of their organisation. Managers should be able to create and maintain the necessary conditions for productive work, often in a climate of resource constraints and increasingly in a global context. This module will also introduce supply chain management – that is, the management of the relationships and flows between different operations processes, both 'locally' and at regional and global scales. The Operations and Supply Chain Management module aims to engage the students in a critical evaluation of the various models of operations and supply chain management and an analysis of the range of processes, skills and competencies required for the successful management of both.

In the modern world, it’s supply chains that compete for and deliver business opportunities, not individual organisations. This module considers how supply chains are formed, managed and made sustainable. It looks at the details of logistics, considering the options for lean and reactive supply chains that meet fluctuating customer demand. Through a variety of lectures projects and simulations, you understand why supply chains are so crucial to delivering responsive, flexible business solutions.

Data and information are critical to the success of organisations. Organisations that manage information effectively can improve efficiency, be more responsive to market opportunities, achieve competitive advantage and operate more sustainably. As organisations seek to develop innovations and grow in a sustainable way, they are looking for better information to guide decisions. This module will explore the role of information and analytics in supporting the development of innovative, sustainable growth strategies and the practical techniques managers can use to design effective information flows.

Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology.

Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others.

This module will give you skills in artificial intelligence and data science developed through themes presented by relevant experts across a range of academic departments. It is aimed at strengthening the quantitative skill set of those faced with data challenges in their future professional life.

The aim of this module is to provide students with an understanding of the role of artificial neural networks (ANNs) in computer science and artificial intelligence. This will allow the student to build computers and intelligent machines which are able to have an artificial brain which will allow them to learn and adapt in a human like fashion.

How do you formulate financial decision problems mathematically? And how do you identify an appropriate method of solution? Understand the basic models and mathematical methods underlying modern portfolio management. Assess the limitations of these models and learn to correctly interpret your results from calculations.

This module will enable you to expand your knowledge on multiple statistical methods. You will learn the concepts of decision theory and how to apply them, have the chance to explore “Monte Carlo” simulation, and develop an understanding of Bayesian inference, and the basic concepts of a generalised linear model.

Ever considered becoming an Actuary? This module covers the required material for the Institute and Faculty of Actuaries CT4 and CT6 syllabus. It explores the stochastic process and principles of actuarial modelling alongside time series models and analysis.