This module offers a comprehensive introduction to programming, focusing on Python for students with varying backgrounds. Beginning with fundamental programming concepts, the course progresses to explore data visualisation techniques and key libraries. Emphasis is placed on hands-on learning, enabling students to develop practical skills in data exploration and analysis. By the end of the course, students gain proficiency in programming, data visualisation, and problem-solving, preparing them to apply these skills across diverse fields and disciplines. Assessment method: 100% coursework.
This module will cover the fundamental concepts and techniques from linear algebra, differential calculus and probability. The acquired tools will help the student to understand how AI is built and enable them to tailor standard methods to specific problems they will be facing and to interpret the results obtained through data analysis.
This module provides an introductory exploration into the foundational concepts of computer science, computer architecture, and operating systems. Students will develop a comprehensive understanding of the historical developments of computing, principles of computer hardware and software, and the fundamental components and functions of operating systems. Assessment method: 100% coursework.
This is an integrated module presenting 10 credits of Software Engineering (SE) and 10 credits of data structure and algorithms material. For Software Engineering, the emphasis is on the knowledge needed to be able to model, design, implement and evaluate larger software systems effectively. The module starts with development lifecycle models, such as agile development, and then continues to cover requirements specification, requirements analysis, object-oriented concepts, the Unified Modelling Language (UML), and object-oriented design. Software engineering is an inherently practical subject, and applying the concepts being taught is a vital component of developing expertise in this area. Consequently, students undertake a substantial group project, working through a number of stages of the development of a larger software application. Students will be expected to largely organise themselves and their work, learning key transferable skills in team management and organisation. Assessment method: 100% coursework.
The module requires students to undertake an independent piece of research/development work, investigating in depth a subject, in which they have a particular interest and of their own selection. The dissertation assesses students’ ability to integrate information from various sources, to conduct an in-depth investigation, where necessary, specify, appropriately develop bespoke software/technology-based solutions, to critically analyse results and information obtained and to propose improvements/further work. Each student will submit a dissertation of between 12,000 and 15,000 words. Assessment method: 100% coursework.
Data Management and Data Quality are increasingly at the heart of every commercial and research enterprise, as the value of data increases along with the need for adaptive security. This module will give you a critical and evaluative knowledge of the theory, practice and research of software engineering techniques for Data Management amid today’s complex and changing business/commercial/research environments. Assessment method: 100% coursework.
The placement gives you the opportunity to spend a year in the workplace, honing your transferable skills and proving your academic learning in the development of real-world systems. The assessment of the placement is designed to support and accredit the experience by formalising personal development outcomes and by contextualising prior learning. Regular on-line contact with tutors, peer contact and placement support will be maintained throughout the year. Assessment method: 100% coursework.
In the information era, more companies and organisations rely on data-driven technologies to provide better service quality. However, with the broader collection and deeper digging of data, social participants raise more concerns about information privacy and ethics in data processing. This module targets on delivering the knowledge and skills revolving around this topic. The contents include but are not limited to database hardware and software structure, data security in storage, information transferring safety, ethics in processing sensitive data, safety in cloud service, security frameworks and privacy regulations. The module will provide both theoretical knowledge and practical exposure to the students.
The module will provide students with the critical skills, knowledge and analytical abilities needed to identify and address ethical challenges as they arise in practice from the application of AI. The module will engage with the ethical and societal challenges of AI and is thoroughly informed by the knowledge, theories and methods of established academic disciplines from philosophy to computer science. Industrial seminars on AI applications will complement the content of the module, exposing the students to the challenges of developing an AI application, considering the short- and long-ranging societal fallout.
