Information Systems Management MSc

The ever-increasing advancements in sensing technologies, network infrastructure, storage and social media have enabled us to acquire an unprecedented volume of data at an explosive rate. As a result, the ability to efficiently and accurately derive human-understandable knowledge from these datasets has become increasingly critical to our digitally driven society and economy. Under this Big Data phenomenon, tremendous endeavours have been devoted to tackle its underlying challenges through both novel solutions and the evolution of existing methodology. The module aims to provide you with the knowledge and critical understanding of contemporary challenges posed by the big data. The topics covered here include the fundamental characteristics and operations associated with big data; existing and emerging architectures and processing techniques; domain applications of big data in practice. Through this module, you will develop an informed understanding of the principles and practice of big data analytics in both general and application specific contexts.

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where is often integrated to other reasoning systems.

This module aims to cover planning for different types of change – discontinuous, radical, incremental or continuous, focusing on both the human and organisational impacts of these changes. As a manager it’s important for you to be able to incorporate management theory and concepts within your working practice. This module aims to help you understand how planning and project management provide opportunities for you to manage change more effectively and efficiently. You’ll have the opportunity to study project management methods, tools and techniques as well as developing an understanding of risk.

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. In this module you will look at different data mining techniques and use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. You will study approaches to preparing data for exploration, supervised and un-supervised approaches to data mining, exploring unstructured data and the social impact of data mining. You will be expected to develop your knowledge such that you are able to contribute to discussions around current application areas and research topics and to increase your background knowledge and understanding of issues and developments associated with data mining.

This module brings together database, object-oriented semantics and web authoring skills using an appropriate set of development tools to enable the student to construct distinct software artefacts. You will be introduced to the programming and design techniques used to produce information systems that meet their required specifications. This will involve the modelling of business activity, the information that supports decision making and instances of significant events and actions. You will acquire skills in programming languages capable of implementing object-oriented and web script software and will also be able to select and apply design techniques to enable an appropriate choice of semantic components and implemented software components to meet the requirements of a given software system.

The data needs of modern enterprises and organisations require a more flexible approach to data management than that offered by traditional relational database management systems. With organizations increasingly looking to Big Data to provide valuable business insights, it has become clear that new approaches are required to handle these new data requirements. Primarily focusing on non-relational data models, this module introduces you to alternative approaches to modelling the data needs of an organization. It also provides you with an opportunity to use non-relational databases and database technologies to build robust and effective organizational information systems.

How do you combine information from Web sources that are using different terminology? This is the fundamental question underlying the idea of the Semantic Web. The answer is the use of graph-based structures that describe a kind of common vocabulary for a particular domain, and annotation that describes information sources in terms of this vocabulary. Then, information can be queried similar to having a (distributed) database. This module will cover basic languages for describing ontologies, as well as languages and tools for processing and querying them. Working both individually and in teams, you will get a hands-on experience in using semantic web technologies. In addition, you will be introduced to industry practice around concepts of so called Linked Data and Knowledge Graphs.

This module enables you to work independently on a project related to a self-selected problem. A key feature in this final stage of the course is that you will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on you to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.