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.
This advanced module equips students with critical mathematical and statistical foundations necessary for data science, machine learning, and data mining. Key areas of focus include Probability and Stochastic Processes, Statistics, Linear and Matrix Algebra, Multivariable Calculus, Optimisation, and Bayesian Statistics. Additionally, the module addresses algorithm complexity to assess computational efficiency in data-driven solutions. Students will apply these concepts through practical problem-solving using industry-standard tools such as R Studio, Excel, and Matlab, gaining hands-on experience in statistical modelling and analytical techniques essential for modern data science.
The module introduces you to the basic theory, concepts, and techniques of machine learning using Python. It will cover the main topics and essential theory in the area. The module also focuses on developing practical skills in designing and developing machine learning systems using suitable software and algorithms in order to solve real-world problems. Assessment method: 100% coursework.
Future Internet technologies comprise set of enablers to deal with the limitation of existing Internet. This includes but not limited All-IP Networking Architectures, evolution towards 4G+/5G networking architectures, open-based networking technologies, Cloud Computing challenges and IoT technologies and its interworking with 4G+/5G networks. The module will provide both theoretical knowledge and practical exposure to the students. Assessment method: 100% coursework.
The module introduces you to the basic theory, concepts, and techniques of data mining, and its role in data science and business intelligence. It will cover the main topics in the area. The module also focuses on developing practical skills in solving real-world data mining problems by using appropriate software suites. Base SAS®, SAS® Enterprise Miner, SAS® Enterprise Guide and Tableau® may be taught and used for this purpose.
To develop an in-depth, critically evaluative knowledge of concepts of security in networks and systems and the acquisition of knowledge about the processes, techniques, and security technologies to achieve an end-to-end security system. The course will analyse the security requirements and the vulnerabilities that threaten the smooth functioning of a computer system / network and will inform about ways of prevention, protection, recognition, and treatment of malicious attacks using appropriate technologies and security tools. Assessment method: 100% coursework.
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.
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 12,000-15,000 words. Assessment: 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: 100% coursework.
