MSc — 2025 entry Business Analytics

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.

The primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.

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.

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.

Machine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.

The primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.

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.

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.

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 finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.

The primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.

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.

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.

Machine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.

The primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.

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.

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.

Machine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.

The primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.

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.

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.

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.