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Referred to as the ‘the new oil’, the importance of data as a resource to modern society cannot be underestimated. Yet many individuals and organisations alike do not use data effectively, if at all. Effective use of ‘big data’, with its vast sizes and numerous formats that arrive at high velocity and vary in multiple dimensions, is especially challenging.

A picture is worth a thousand words. Visualisation is one of the quickest, yet most effective techniques, for understanding and gaining insights from data. Visualisation is particularly effective at uncovering complex relationships among hundreds, or even thousands, of factors to determine their relative importance. Sophisticated analyses can be performed quickly, even immediately, and results presented in ways that showcase patterns so that the data can be used to aid faster, more effective decision-making.

The purpose of this module is to equip you with the knowledge and skills for analysing and interpreting big data using visualisation techniques. The module will explore some of the theories that explain why visualisation is one of the most effective communication tools, the guiding principles of effective visualisation; and the nature of big data, its unique challenges and the way visualisation analytics must adapt to make sense of big data. You will learn, by examining numerous real-world datasets from a wide range of disciplines, how to create engaging, informative, compelling visualisations that reveal the core story hidden within a dataset, no matter how large and complex.

Part art, part science and quite unlike other data analytics techniques, data visualisation is less reliant on subject specific skills, such as statistical computer programming. Thus, this module will suit anyone who has an interest in making the most effective use of data, enjoys being creative, and is reasonably comfortable with using a computer.

As organisations have become more knowledge intensive, the ability to manage and create knowledge has become a matter of competitive survival. This module is intended to develop students a holistic view of business analytical intention and to understand that it is part of organisational knowledge management process through a blend of theory and current practice in knowledge management and business analytics in organisations. This module firstly introduces the contemporary importance of knowledge and knowledge management in which two perspectives will be examined i.e. objectivist and practice-based perspectives. In the objectivist perspective, information and communication technologies that play a prominent role in knowledge management processes are examined. In the practice-based perspective, knowledge management processes and social issues will be explored. The second part of this module explores two particular aspects of knowledge management i.e. knowledge discovery and creation by examining underlying methodology of business analytics, design thinking and methods and ethical issues.

As organisations have become more knowledge intensive, the ability to manage and create knowledge has become a matter of competitive survival. This module is intended to develop students a holistic view of business analytical intention and to understand that it is part of organisational knowledge management process through a blend of theory and current practice in knowledge management and business analytics in organisations. This module firstly introduces the contemporary importance of knowledge and knowledge management in which two perspectives will be examined i.e. objectivist and practice-based perspectives. In the objectivist perspective, information and communication technologies that play a prominent role in knowledge management processes are examined. In the practice-based perspective, knowledge management processes and social issues will be explored. The second part of this module explores two particular aspects of knowledge management i.e. knowledge discovery and creation by examining underlying methodology of business analytics, design thinking and methods and ethical issues.

As organisations have become more knowledge intensive, the ability to manage and create knowledge has become a matter of competitive survival. This module is intended to develop students a holistic view of business analytical intention and to understand that it is part of organisational knowledge management process through a blend of theory and current practice in knowledge management and business analytics in organisations. This module firstly introduces the contemporary importance of knowledge and knowledge management in which two perspectives will be examined i.e. objectivist and practice-based perspectives. In the objectivist perspective, information and communication technologies that play a prominent role in knowledge management processes are examined. In the practice-based perspective, knowledge management processes and social issues will be explored. The second part of this module explores two particular aspects of knowledge management i.e. knowledge discovery and creation by examining underlying methodology of business analytics, design thinking and methods and ethical issues.

This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.

Having learnt the basic techniques and principles of business analytics in previous modules, this module will introduce you to a number of advanced applications of business analytics in practice. These include pricing and revenue management, credit scoring, big data solutions and technologies, and advanced models to extract complex non-linear patterns from large amounts of diverse data. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

This course provides part of the essential knowledge and skills required for conducting the Final Project module in the final year.

Having learnt the basic techniques and principles of business analytics in previous modules, this module will introduce you to a number of advanced applications of business analytics in practice. These include pricing and revenue management, credit scoring, big data solutions and technologies, and advanced models to extract complex non-linear patterns from large amounts of diverse data. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.

Companies nowadays have collected a large volume of data from various sources. This module aims to introduce the key concepts of using ‘Big Data’ to support organisations in making decision. The module will start by defining the concept of data analytics. We will then focuses on the use of predictive (e.g. regression and classification) and descriptive (e.g. clustering, association and sequence rules) techniques. The module will illustrate how data analytics can be successfully used to develop different application areas such as marketing, retail credit risk, healthcare, fraud detection, etc. The theoretical concepts will be illustrated using real-life application cases and world-class commercial software.

Companies nowadays have collected a large volume of data from various sources. This module aims to introduce the key concepts of using ‘Big Data’ to support organisations in making decision. The module will start by defining the concept of data analytics. We will then focuses on the use of predictive (e.g. regression and classification) and descriptive (e.g. clustering, association and sequence rules) techniques. The module will illustrate how data analytics can be successfully used to develop different application areas such as marketing, retail credit risk, healthcare, fraud detection, etc. The theoretical concepts will be illustrated using real-life application cases and world-class commercial software.

Companies nowadays have collected a large volume of data from various sources. This module aims to introduce the key concepts of using ‘Big Data’ to support organisations in making decision. The module will start by defining the concept of data analytics. We will then focuses on the use of predictive (e.g. regression and classification) and descriptive (e.g. clustering, association and sequence rules) techniques. The module will illustrate how data analytics can be successfully used to develop different application areas such as marketing, retail credit risk, healthcare, fraud detection, etc. The theoretical concepts will be illustrated using real-life application cases and world-class commercial software.

Having learnt the basic techniques and principles of business analytics in previous semester 1 modules, this module will introduce you to a number of advanced machine learning methods and their applications in practice. These include machine learning methods, big data solutions and technologies, and advanced models to extract complex non-linear patterns from large amounts of diverse data. The focus will be on the underlying principles, modelling methodologies, and implementation using appropriate software packages.