Unit description 

Core Units

Artificial Intelligence 

This unit gives an overview of the application and associated theories of artificial intelligence (AI), machine learning and the techniques used in developing intelligent systems. The emphasis is on business, data science and engineering problems. Upon completion, students should be able to evaluate machine learning techniques and manage the application of various tools available for developing such systems to solve problems in the real world. Topics include artificial neural networks (including deep learning), data mining, fuzzy systems, genetic algorithms, intelligent agents, optimisation techniques and rule-based systems. 

 

Business Analytics 

Business analytics is a broad term that encompasses a number of approaches, skills and technologies to the analysis of business data for the purpose of supporting management decision-making. This unit will explore business analytics using a number of case studies, real-life datasets and publicly available analytics tools. Students will apply the theories addressed in the unit to case studies in order to develop the technical skills required to gain a deeper understanding of business analytics. 

 

Data Analytics 

This unit examines topics relevant to data science. It includes data cleaning, summarising data using descriptive statistics and graphical displays, using linear models for both inference and prediction, and applying methods for dimension reduction and classification. Throughout the unit, advanced statistical software will play an important role in data visualisation and analysis, and topics will be motivated through the presentation of a range of real research problems. A project will be used to simulate statistical problems commonly encountered by data scientists in the workplace. 

 

Data Resources Management 

This unit sees the information available in corporate databases as a significant strategic resource. It focuses on the issues involved in managing and strategically using data, including the discovery of trends and patterns, and new knowledge creation. Topics include the nature of the data resource and the intelligent enterprise, data distribution and architectures, data administration, planning, design and exploitation of the data resource, knowledge management, and issues of data ownership and security. 

 

Foundations of Data Science 

The objectives of this unit are to introduce students to an understanding of data, uncover patterns within it and develop models whose prediction accuracy on future, unknown data can be quantified. 

Major topics include assessment of model performance, classification models, concepts of statistical learning, extracting meaning from data, machine learning algorithms, over-fitting and model tuning, recommendation engines and social networks, and regression models. The R programming language and software environment will be introduced to students and used to demonstrate implementations. 

 

IT Professional Practice  

This unit focuses on current expected professional practice in the information technology (IT) industry, and the legal and ethical challenges raised by new technologies. Topics include copyright and intellectual property issues, employment and principles of human resource management within the organisation and across a global context, legal requirements and responsibilities including privacy issues, modes of communication including professional presentations and technical writing, and professional standards and codes of practice. 

 

Information Technology Project Management 

This unit covers the principles and practices of managing the development of information technology systems. Upon completion, students should be able to use appropriate tools and techniques based on the Project Management Body of Knowledge (PMBoK). 

 

Information Technology Research Methods 

This unit is designed to provide an introduction to research for students in information technology areas. It will focus on the kinds of research questions addressed in information and communications technology (ICT) research and introduce students to the broad range of research approaches used in it, including action, case study research, design, experimental and survey. Students will gain the knowledge and skills needed to critically evaluate studies in the ICT literature. 

 

Machine Learning  

In this unit, students will develop an in-depth understanding of machine learning and some of the main algorithms in this field. Topics include classification and clustering techniques, concepts of machine learning, data pre-processing, ensemble learning, model selection and performance evaluation, neural networks and deep learning, and transfer learning and multitask learning. Application examples are taken from areas such as health data analysis, sentiment analysis and computer vision. Students will learn how to design and implement machine learning and deep learning models for data analysis using Python. 

Elective Units

Applied Information Security Management 

Information security managers design, build and manage enterprise information security. This unit focuses on enabling students to use the tools of threat and vulnerability assessment, security policy and procedure, and strategic decision-making for information security management. They will work in teams within a realistic enterprise environment to develop enterprise security policies to deal with challenges to information security. 

Business Analysis and Systems Development Approaches  

This unit aims to further develop the knowledge and skills that are critical to effective business analysis and design, building on knowledge from undergraduate systems analysis and design units. It emphasises business process modelling, and the techniques and tools that can be used to analyse, model and design business processes. Agile system development methodologies will also be contrasted with more formal approaches, and user experience design, and software evaluation and selection will be covered as well. 

 

Human Factors in Information Technology 

This unit covers the human factors (HCI) aspects of information technology at 4 levels: individual, group, national and international. It also discusses methods of design and evaluation used to produce efficient, effective and satisfying user interfaces, multimedia products and websites. The unit further considers the various forms of augmented reality, computer-mediated communication, groupware, mobile technologies, ubiquitous computing and virtual reality. Students will be able to relate theoretical concepts to practical techniques within a fair socio-technical context. 

Information Technology Strategy 

This unit recognises the highly dynamic nature of information technology (IT) development and its impact on strategic management. Techniques allowing changes to be understood, modelled and tracked in strategic IT management contexts are introduced. Topics include IT governance and IT human resource issues, including outsourcing, change management and contract management, IT organisational structure, IT strategy, strategic planning, strategic alignment maturity and the role of the Chief Information Officer.  

IT Group Project 

The approach to learning in this unit is focused on providing an experience that is based on the kind of professional practice that graduates will encounter, allowing students to apply the whole range of knowledge and skills acquired throughout the programme to a challenging real problem. In addition to regular interactive workshops, each team will meet with their supervisor weekly, their clients regularly and their team frequently. Each team will produce a requirements document, a project management plan and project deliverables that will be negotiable depending on the nature and scope of the project. The project culminates with a public presentation of the project deliverables. 

Knowledge Management 

This unit examines the role of knowledge management (KM) in organisations, and its principles and practices. It includes a consideration of theories of knowledge and approaches to KM, drawing on a range of perspectives such as artificial intelligence, cognitive science, library science, management and psychology. Topics include cultural aspects of knowledge, document-centric practices and information management, knowledge representation including ontologies, organisational memory and learning organisations, situating KM practices within an organisational context, and social approaches including communities of practice. 

 

Quantitative Research for Business 

Statistics are a vital part of most decision-making processes. Both descriptive and inferential statistics are utilised to explain and interpret information. This unit introduces data analysis and decision-making tools for managers. Students will identify situations in which quantitative analysis can support problem-solving and decision-making. They will gain experience in applying decision-analysis techniques and statistical packages in management contexts. These include the modelling of business research problems, presenting descriptive measures of quantitative data and reporting statistical inferences.