Computer Science MSc PG Certificate PG Diploma

Students on this module will learn how to write software programs in python and develop an understanding of programming techniques such as conditions, loops, functions, and object oriented programming.

This module introduces students to the design and analysis of efficient algorithms and data structures. Students learn how to quantify the efficiency of an algorithm and what algorithmic solutions are efficient. Techniques for designing efficient algorithms are taught, including efficient data structures for storing and retrieving data. This is done using illustrative and fundamental problems: searching, sorting, graph algorithms, and combinatorial problems such as finding the shortest paths in networks.

This module introduces software engineering, covering the appropriate choice of software lifecycle model, the interactions between developer and customer, the management of a software engineering team, the conceptual management of project information throughout the project lifecycle, and other aspects of software engineering. It is an essential pre-requisite for the team development project.Weekly formative coursework is an essential part of the learning for this module; the final examination will be based on this work.

This module aims to enable students to recognise the legal, social, ethical and professional issues involved in the exploitation of computer technology and be guided by the adoption of appropriate professional, ethical and legal practices. It describes the relationship between technological change, society and the law, including the powerful role that computers and computer professionals play in a technological society. It introduces key legal areas which are specific and relevant to the discipline of computing (e.g., intellectual property, liability for defective software, computer misuse, etc) and aims to provide an understanding of ethical and societal concepts that are important to computer professionals, and experience of considering ethical dilemmas.

The Team Development Project is a software engineering project which runs over the Spring semester. The philosophy underlying the project is that the skills needed for team working in the software engineering field can most effectively be learned by experience. The project is based around a client who has a real software development problem to be solved. Students are organised into small teams who work cooperatively on the analysis, design, implementation and testing of the client's software.

This module will introduce students to the cybersecurity principles and core fundamentals for an increased security posture in today's rapidly changing threat landscape.

Students completing this module will be exposed to theoretical and practical aspects of cybersecurity, including risk management and the significant legal implications of insecure systems in our increasingly digital world.

The module emphasises developing a keen awareness of the far-reaching consequences of cybersecurity actions on individuals, organisations, and society at large. Students will get to understand how vulnerabilities can be exploited and how robust security measures can mitigate these pervasive risks. Hands-on exercises and case studies will provide practical experience in identifying, assessing, and responding to cyber threats.

The goal is to develop not only technically proficient but also ethically responsible professionals, who are aware of the consequences of their actions on individuals, organisations, and society at large, thereby preparing them to be conscientious and ethical leaders in the cybersecurity field.

This module provides students with practical skills to integrate modern AI technologies into real-world software applications. Rather than focusing on mathematical foundations, the course emphasises hands-on experience with contemporary AI frameworks, APIs, and cloud services.

Students will explore the current AI landscape, including large language models, reinforcement learning systems, and generative AI tools. The curriculum covers prompt-engineering, model selection, performance evaluation, and the critical decision-making process of when and how to incorporate AI capabilities into software projects.

Through working with pre-trained models and various AI service providers, students will learn to implement AI-powered features and manage data pipelines for AI systems. Practical experience with popular frameworks and cloud-based services will enable students to build applications that incorporate diverse AI capabilities.

The module places strong emphasis on the ethical and practical considerations of AI deployment, including bias detection and mitigation, privacy concerns, explainability requirements, and the social implications of AI-powered systems. Students will explore the environmental impact of AI systems, examining energy footprints and how training-inference trade-offs are reshaping approaches to AI scaling and deployment. Students will develop critical thinking skills to distinguish between marketing hype and genuine AI potential, enabling them to make informed technical decisions in this rapidly evolving field and understand the real limitations versus the potential for impact of future AI solutions.

This module aims to provide a solid foundation in research methodology, both for the dissertation project and preparing for future research into new technologies and products. Students receive instruction on topics such as: advice on research methods and technical writing style; risk analysis and contingency planning; peer-review processes; and the details of working within a professional, legal and ethical framework.

For your individual project, you can choose from a wide range of possibilities in many different environments both within and outside the University. The project is completed during the summer, and you will have a personal academic supervisor to guide you during this period.