Master of Computer Science

The learner will cover all the basic concepts of a programming language and is taught through real world coding examples, the learner will have regular coding assignments for better understanding through practice. Students engage with current research literature and professional sources on modern programming practices, including software maintainability, performance optimisation, and secure coding. Emerging topics such as concurrent programming, memory management in managed runtimes, and evolving Java frameworks are explored through reference to peer-reviewed articles and industry research.

Learner will also develop the programming skills required to communicate with database systems for accessing, transforming, and persisting the data using Structured Query Language. The module incorporates engagement with contemporary research on data modelling, transaction management, database optimisation, and data governance. Students examine emerging research topics such as scalable database architectures, data consistency in distributed systems, and ethical data management through academic journals and professional publications.

Topics include processor design, memory hierarchy, input/output systems, and instruction set architectures. Students will learn how computer systems are structured and how components interact to execute programs efficiently. Practical applications and performance optimization techniques will also be explored. Students engage with current research literature in computer architecture, including advances in multi-core processing, energy-efficient architectures, parallel computing, and hardware–software co-design. Research findings from academic journals and conferences are used to contextualise architectural trade-offs and performance considerations.

It covers topics such as data transmission, network topologies, routing algorithms, and security mechanisms. Students will gain practical skills in designing, managing, and securing network infrastructures through hands-on projects and case studies. The module integrates contemporary research on network architectures, protocols, and security, including topics such as software-defined networking, network automation, and resilient network design. Students engage with peer-reviewed research and industry white papers to analyse evolving networking challenges.

Learners will develop a critical understanding of the philosophical, practical, and ethical concepts of research within the context of computing discipline. This module is explicitly research-focused and requires sustained engagement with contemporary computing research literature. Students analyse peer- reviewed journal articles and conference papers to develop research questions, methodological approaches, and ethically grounded research designs.

The aim of this module is to introduce a range of data science concepts, data administration and governance and big data sources. The module introduces learners to contemporary research in data science, including data governance, scalable analytics, and ethical data use. Students engage with peer-reviewed research on data-driven decision-making and emerging analytical techniques relevant to organisational and societal contexts.

Students will be guided to appreciate AI types and applications and develop a better understanding of aspects related to intelligent agents. In this module students will master key concepts and gain the practical knowledge to apply machine learning principles to challenging real-world problems. Students engage with contemporary research topics including explainable artificial intelligence, ethical machine learning, model interpretability, and large-scale data analytics. The module draws on peer-reviewed journal articles and conference proceedings to contextualise theoretical and applied developments in AI and machine learning.

The course covers various data analysis techniques, including descriptive and inferential statistics, as well as data cleaning and transformation methods. Students will learn to use software tools such as Excel, Python (with libraries like Pandas, NumPy, and Matplotlib), and Tableau to manipulate, analyse, and visualise data effectively. By the end of the module, students will be able to identify patterns, trends, and anomalies in data, and communicate their findings through impactful visual representations, supporting decision-making processes in business and research contexts. The module integrates engagement with current research on data interpretation, visual analytics, and evidence-based communication. Emerging research topics such as visual bias, transparency in data storytelling, and interactive visualisation are explored through academic literature and professional research.

Topics include probability distributions, hypothesis testing, regression analysis, and statistical inference techniques. The module emphasises the practical use of statistical tools to analyse and interpret data, helping students develop the skills necessary for making data-driven decisions. Real- world case studies and hands-on exercises using software such as R or Python will be incorporated to enhance students' ability to perform statistical analysis and draw meaningful conclusions from data. Students engage with contemporary research in statistical modelling, uncertainty quantification, and data-driven inference. The module draws on peer-reviewed studies to examine the application of statistical reasoning in complex and uncertain analytical environments.

The learner can critically evaluate various security tools and standards for security engineering in a range of data security measures, social engineering attacks, to explore and implement system hardening techniques and acquire the knowledge and skills to be able to design a secure system. Students engage with current research literature in security engineering, including vulnerability management, secure system design, threat modelling, and cryptographic applications. Emerging research topics such as zero-trust architectures and secure cloud infrastructures are examined through academic and professional sources.

Students will explore the intricate relationship between the IoT and cryptography, gaining a profound understanding of how cryptography plays a pivotal role in securing the expansive network of interconnected devices that constitute the IoT ecosystem. This module will provide you with a comprehensive theoretical foundation on how cryptographic techniques are applied to IoT environments, ensuring their security and privacy. The module incorporates engagement with current research on IoT architectures and applied cryptography, including secure communication protocols, lightweight cryptographic schemes, and privacy-preserving IoT systems. Students analyse emerging research challenges through academic journals and standards-based publications.

Students will learn about network protocols, IP addressing, subnetting, routing, and switching, as well as how to configure and secure networks. The module will also cover the use of Kali Linux tools for ethical hacking, including information gathering, vulnerability scanning, and exploitation techniques. Through practical exercises, students will develop the skills necessary to assess and secure networks in real-world environments. Students engage with contemporary research in network security and penetration testing, including emerging attack vectors, defensive strategies, and ethical hacking methodologies. Research literature and professional security reports are used to contextualise practical activities and tools.

Students will learn how to identify, exploit, and defend against vulnerabilities in various systems, including networks, web applications, and devices. The module emphasises hands-on practice with tools like Kali Linux to perform real-world hacking simulations and understand cybersecurity concepts. By the end, students will be able to apply ethical hacking principles to assess and improve system security. The module draws on current research in ethical hacking and offensive security, including studies on vulnerability discovery, exploit development, and responsible disclosure. Students engage with peer-reviewed research and professional security publications to inform analytical and practical work.

It covers topics such as data acquisition, evidence preservation, cybercrime investigation, and legal considerations in digital forensics. The module focuses on the application of forensic tools and techniques to solve real- world cybercrime cases, ensuring students understand both technical and ethical aspects of forensic computing. Students engage with contemporary research in digital forensics, including evidence acquisition, forensic analysis techniques, and legal admissibility of digital evidence. Academic research and professional guidelines are used to analyse evolving forensic challenges

Students will learn how to assess security, compliance, and performance of various applications and devices, identifying vulnerabilities and potential risks. The module emphasises practical tools and real-world scenarios for conducting effective audits, ensuring the integrity and security of IT systems. The module integrates current research on application security, device auditing, and compliance frameworks. Students engage with academic and professional research on audit methodologies, risk assessment, and emerging regulatory and technological challenges.

Common objectives include applying knowledge, conducting in-depth research, showcasing problem-solving skills, demonstrating effective project management, communicating findings clearly, exhibiting technical proficiency, applying critical thinking, considering ethical considerations, and promoting collaboration. The specific requirements may vary, so it's essential to refer to your institution's guidelines for precise information. The Capstone Project requires sustained engagement with current research literature relevant to the chosen project domain. Students critically analyse academic research, industry reports, and professional standards to inform problem formulation, solution design, and evaluation. The Dissertation / Capstone Project constitutes the culminating academic component of the Master of Computer Science and enables learners to demonstrate advanced theoretical understanding, applied technical competence, and independent scholarly judgement consistent with MQF Level 7 descriptors. Through an individual, research-informed project, learners design, implement, and critically evaluate an advanced computing system or research study addressing complex and unpredictable technical challenges. The module emphasises autonomy, responsibility, and critical decision-making in both technical and research contexts.