MSc Business Analytics

Data mining is the process of identifying anomalies, patterns, and correlations within large datasets to predict outcomes. It involves exploring databases to extract potentially useful information such as rules, structures, regularities, and hidden trends. A wide range of analytical techniques is used to transform this information into actionable insights that support decision-making, improve customer relationships, reduce risks, increase revenues, and optimise operational efficiency. In today's data-driven business environment, data mining and business analytics play a critical role in maintaining a competitive advantage. This module introduces key analytical approaches and case studies of well-known applications, including shopping basket analysis (such as loyalty card systems), fraud detection in financial transactions, stock market prediction, risk analysis in banking, web analytics, and social network analysis. In addition, the module introduces AI-driven analytics methods to enhance traditional data mining approaches. These include automated pattern discovery, predictive modelling, and the analysis of unstructured data such as text. Emphasis is placed on how these methods can support more accurate forecasting, deeper insight generation, and improved decision-making in real-world business contexts.

This module introduces methods for building, estimating, and interpreting statistical and econometric models focusing on the area of business analytics, and analyzing quantitative data for making better decisions. The module provides the theoretical foundation and intuitive knowledge, applied to business data by making use of econometric/statistical software.

The module provides the theoretical underpinnings of our MSc Accounting and Finance programme. It introduces the pivotal concepts which form the basis of theoretical finance under three broad headings; Portfolio Theory and Practice, Equilibrium in Capital Markets and Introductory Analysis of Asset Classes. Core concepts include the relationship between risk and return, the Capital Asset Pricing Model (CAPM) and the Efficient Market Hypothesis (EMH) but the module also extends this analysis into new theoretical areas such as Behavioural Finance.

The module creates an understanding for the students about the role and importance of supply chain management and logistics for industries. It expands the perspective of students about the application of business analytics in different areas of supply chain management and provides knowledge to them about the challenges which supply chains are facing, e.g., environmental issues. The objectives of the module are: Providing theoretical and practical knowledge about: The principles, elements, and performance dimensions of logistics and supply chain management. The strategies used by companies when managing their supply chains by considering the types of products and the specifications of their market. The role of digitalisation and Information and Communication Technology (ICT) in managing logistics and supply chain operations. The role of sustainability practices across supply chains. The importance of logistics and supply chain management in eCommerce.

The module examines the various approaches to equity investment analysis, providing a systematic understanding of the challenges faced and the decisions to be taken when analyzing and valuing corporate equity. It encompasses the principles and practice of valuation of companies’ shares. This is examined from several aspects; industry analysis, company analysis, valuation methods and the link between valuation and investment style. Real corporate analysis will be undertaken in order to highlight and evaluate the different approaches to investment analysis.

This module provides an introduction to data analytics within a business context, focusing on the foundations of data-driven decision-making and the analytics lifecycle. Students will develop practical skills in data management, business intelligence, visualisation, and introductory machine learning techniques.The module introduces key concepts and frameworks used in business analytics, including the Cross-Industry Standard Process for Data Mining (CRISP-DM), relational databases, structured query language (SQL), exploratory data analysis, and dashboard development. Students will work with real-world datasets to develop analytical solutions that support managerial and operational decision-making.A strong emphasis is placed on transforming data into actionable insights through effective visualisation, storytelling, and business intelligence techniques. The module also introduces fundamental concepts in statistical learning and predictive analytics, providing a foundation for more advanced modules in machine learning and artificial intelligence.

Operational Analytics is a core module for Business Analytics. Students will learn how to apply Operational Research techniques - the cornerstone of Management Science for the past 70 years - in a digital world rich with data. Students will learn various quantitative techniques (linear programming, Risk Analysis, Simulation Modelling) that are commonly used within OA. Importantly, these techniques will be studied in the context of the overall decision-making process, so that they are aware of why and how we turn data into actionable insights. Thus the module also covers more qualitative approaches such as problem-structuring and data visualisation.

This module provides an applied introduction to machine learning and artificial intelligence (AI) within a business analytics context. It focuses on the design, implementation, evaluation, and interpretation of analytical models for data-driven decision-making.Students will develop knowledge of statistical and algorithmic machine learning methods, including supervised, unsupervised, and potentially reinforcement learning approaches. The module emphasises practical implementation using real-world datasets and modern data science workflows.Key topics include data preparation, model development, validation techniques, and ensemble methods. Neural networks, optimisation, and explainable artificial intelligence (XAI) may be considered. Students will critically evaluate analytical approaches and interpret model behaviour using appropriate evaluation and explainability techniques.The module also discusses emerging developments in AI (e.g. generative AI and large language models) and considers their opportunities and limitations in business applications.

The module equips students with the knowledge and tools to implement financial models using Python. The course introduces students to the general principles of building financial models, as well as a number of specific financial modelling tools, including matrix calculations, optimization, regression analysis (both time-series modelling and panel data modelling), out-of-sample forecasting and simulation. These methods are applied to a range of practical problems in finance, including passive and active portfolio management, risk management and currency valuation. The emphasis of the course is on practical application of the theory, with lectures on each topic followed by in-depth practical classes, in which students work through real world problems using Python.

This module introduces students to the principles of data and business process management and examines the opportunities, benefits and challenges that business process modelling and Enterprise Resource Planning (ERP) systems create for organisations and their management.The module has two complementary strands.First, through lectures, students develop a critical understanding of data and business process management and its organisational and managerial implications.Second, through practical laboratory sessions, students model and operationalise business processes using the SAP platform. These sessions provide hands-on experience of how information systems enable, integrate and transform organisational processes.Across all topics, students critically evaluate the advantages and limitations of technology-enabled business processes.

This module is designed to introduce students to how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience in implementing an efficiency and productivity assessment with real case studies using appropriate software.

This module introduces students to the concept and current practices in marketing analytics. Technology advances of the past decade have dramatically enhanced marketers' means of collecting and analysing data to measure the effectiveness of their marketing strategies. This module is designed to provide students with an overview of state-of-the-art marketing analytics practices that guide marketing executives in their strategic decisions. The module focuses on introducing students to key analytical techniques with an emphasis on interpreting results and generating strategic insights for marketing decision-making. The course will be of particular value to students planning careers in marketing and management consulting. The course is designed for students with a basic understanding of univariate and bivariate statistics. Addressing different learning styles, the following teaching methods are applied in this course: Pre-readings, Lectures, Class Exercises, Class Discussions, and Real World Cases/Industry Insights.

The module is compulsory for all MSc students and is the final element of the programme, providing an opportunity for a sustained period of independent study and research. It allows students to concentrate on topics that are of particular interest to them and it draws upon a range of different aspects of the taught programme particularly the analytical and quantitative methods they learn throughout the course. It also gives an opportunity for students to work independently with individual supervision. The module can take one of two different formats: a) Dissertation - An academic piece of work. This form of dissertation follows the standard academic pattern of identifying a topic arising from a gap in the literature and developing a methodology to explore this area in depth. b) Project - A business or applied piece of work. This form of project starts with an emerging business problem, either provided from an industrial partner or with their co-operation in the process, and seeks to provide a research based solution to or exploration of the problem. Any engagement with external party needs approval. Both formats of the written piece of work seeks to develop the same learning outcomes and follow the same assessment criteria.