8356 modules
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MANG3093 2027-28
Analytics Implementation III: Knowledge Management, Methods and Ethics
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. -
MANG3093 2028-29
Analytics Implementation III: Knowledge Management, Methods and Ethics
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. -
MANG3093 2029-30
Analytics Implementation III: Knowledge Management, Methods and Ethics
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. -
MANG3073 2028-29
Analytics in Action
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. -
MANG3073 2027-28
Analytics in Action
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. -
MANG3108 2027-28
Analytics in Action I
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. -
MANG3108 2028-29
Analytics in Action I
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. -
MANG3108 2029-30
Analytics in Action I
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. -
MANG3098 2029-30
Analytics in Action II
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. -
MANG3098 2027-28
Analytics in Action II
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.