Programmes / Doctor of Philosophy (PhD) in Computer Science / Module Descriptors
This module covers the underlying theory and forms of qualitative research approaches, methods and ethics as they apply to the context of the programme. This includes acquiring a critical and interpretative understanding of qualitative research approaches, theories and concepts, as well as methods and techniques that constitute the qualitative research realm. The emphasis in this module will be on an understanding of and rationale for adopting qualitative research, as well as on controversies and debates about qualitative forms, the role of the researcher, the rights of the research subject, cultural and social norms, and research practices. The module will also cover the distinctions between qualitative and quantitative research and the role of mixed methods.
This module introduces students to, and familiarises them with, a wide range of methods of data collection, analysis and interpretation. It will consider the strengths and weaknesses of experimental and quasi-experimental approaches, the proposal and testing of hypotheses and the suitability of such methods. It will introduce students to a range of descriptive and inferential statistical techniques used for interpreting numerical data.
This module concentrates on the development and design of student research proposals, consisting of two main parts: Part I, developing the research question, aims and relevant theoretical approaches; Part II, designing the research methodology, including the approach, methods, instruments, data analysis and project management techniques.
This module provides students with an opportunity to gain an in-depth understanding of the theories and issues in analytics and big data. The course will cover how big data is collected, stored, and analysed. Students will also learn about the main challenges faced when dealing with big data. Practical case studies will be used for illustration.
This course covers the use of intelligent agents for supporting distributed decision making. The objective of the course is to provide students with a wide range of theories of relevance to their research and development in distributed decision support systems – from decision theory and naturalistic decision making to models of agent knowledge representation and learning.
This course teaches students how to use computing techniques and artefacts to support, mediate, and understand aspects of social behaviours and social interactions. Wikipedia, Facebook, Twitter, and Flickr are some examples of how social computing has changed our social behaviour. The purpose of this course is to obtain a deeper understanding about how these technologies influence human behaviours, and to figure out how to improve existing designs and devise new models based on an understanding of human behaviours in technological contexts.
This course covers the study of human language from a computational perspective. The objective of the course is to provide students with a thorough understanding of current paradigms in Natural Language processing as well as hands-on experience in developing NLP systems using current paradigms. Students’ projects will involve both statistical and symbolic approaches to NLP.
The objective of the course is to provide students with a broad understanding of current applications in Arabic Natural Language processing such as part-of-speech tagging, chunking, parsing, text summarisation, sentiment analysis, information retrieval and extraction, and machine translation. Students will also have hands-on experience in developing NLP systems using current tools. Students’ projects will involve both statistical and symbolic approaches to Arabic NLP.
The aim of this module is to teach the principles and technologies of knowledge management in the context of IT organisations. A case study approach, as and where appropriate, will be adopted in introducing the course contents. The module covers the fundamental concepts in the study of knowledge and its creation, representation, dissemination, use and re-use, and management. The focus is on methods, techniques, and tools for computer support of knowledge management, knowledge acquisition and knowledge sharing in organisations.
The course aims to present the principles, techniques, and methods for professional and systematic software development. Unified Modelling Language (UML), CASE tools like Rational Rose, and programming languages like Java, will be used in the context of this course. In order for students to deepen their knowledge of software engineering, several software examples will be examined during the course lectures.
The aim of this module is to help students to understand the core concepts of computer systems, rather than particular implementation details; to understand distributed, storage, and operating systems; and understand how to engage in systems research and development.
This research-based module aims to develop students’ understanding of the challenges posed by the emergence of systems of systems engineering and needed research into concepts and approaches that will be required for the engineering of ultra-large-scale complex systems of systems such as Smart Cities, The Fourth Industrial Revolution/Industry 4.0, Energy Smart Grids, Global Financial Systems, Global Health Systems, and Food Supply Chain.
This module provides students with an opportunity to gain an in-depth understanding of the theories and issues in an advanced topic in CS. The course will cover new technologies that are not offered in the current module descriptions (e.g. Energy Aware Computing, Bioinformatics, Health Informatics, Big Data).
EdD - PhD in EducationPhD in Project ManagementPhD in Architecture and Sustainable Built EnvironmentPhD in Computer SciencePhD in Business Management
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