Business Analytics MSc — 2027 entry

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 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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 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.

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.

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 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.