MSc Computer Science with Data Science (Full-Time)

Develop a critical understanding of the principles and methodologies underpinning modern data science, exploring the full data science lifecycle from collecting and cleaning data through to analysing, sharing and acting on insights. You will learn how to prepare, transform and integrate diverse datasets for analysis, applying statistical, machine learning and analytical techniques using tools and programming environments such as R to extract meaningful findings from complex data. The module introduces big data environments and explores how data science is applied within organisational and research contexts to support strategic decision-making. Throughout, you will engage with the professional, ethical, legal and societal implications of data-driven practice, including how responsible data use and sustainable approaches contribute to trustworthy digital systems.

Critically explore how organisations generate, manage and use Big Data to support strategic decision-making and drive innovation, examining data ecosystems from structured and unstructured data sources through to governance frameworks, trust and responsible data practice. You will evaluate both the opportunities and the challenges that large-scale data use presents, including data quality, privacy, ethics and the environmental and societal implications of data-driven technologies. Drawing on contemporary research and real-world case studies, you will assess how organisations design and implement effective data strategies while navigating their legal, ethical and sustainability responsibilities. By the end of the module, you will have the critical analytical skills to contribute meaningfully to Big Data initiatives across a range of organisational and sector-specific settings.

Develop a critical understanding of machine learning and data mining and their central role in building intelligent, data-driven systems, exploring how models are designed, trained, optimised and evaluated across a range of analytical and predictive applications. You will examine a broad range of machine learning approaches, from neural networks and hyperparameter tuning to data augmentation and model deployment, developing the practical skills to tackle real-world computational challenges. The module addresses the complexities of generalisation, performance and real-world integration, preparing you to evaluate model suitability and navigate the challenges of deploying machine learning solutions in live environments. Alongside the technical content, you will engage critically with the professional, ethical, societal and sustainability considerations that come with developing and deploying AI-driven systems.

Explore the principles of computer architectures and networks, examining how endpoint devices are built, connected and secured within modern distributed computing environments. Combining conceptual understanding with practical investigation, you will use network simulation tools to model, analyse and troubleshoot the behaviour of routers, switches and security appliances in realistic network scenarios. Security is woven throughout the module: you will examine network vulnerabilities, evaluate attack vectors and apply technical defences and secure design principles to build resilient network infrastructures that can withstand real-world threats. The module also considers emerging networking trends, including the growing impact of the Internet of Things (IoT), ensuring your knowledge reflects the rapidly evolving landscape of modern networks.

Develop a critical understanding of the principles and practices used to design and deliver complex software systems, tracing how software moves from requirements through to working, reliable code. You will examine software development lifecycle models including traditional, agile and DevOps approaches, applying techniques for requirements engineering, system modelling and architectural design using UML to real-world software scenarios. Testing, verification and quality assurance are central to the module, equipping you with the skills to evaluate and improve software reliability and maintainability throughout the development lifecycle. You will also engage with contemporary tools and practices including continuous integration, collaboration platforms and the implications of generative AI, alongside the ethical, security and sustainability responsibilities of professional software engineers.

Develop an advanced understanding of database engineering and learn to design, implement and manage secure database systems using both relational and emerging technologies, including NoSQL platforms. You will critically examine key concepts such as data modelling, database design, transaction management, query optimisation and distributed architectures, gaining practical programming skills through database implementation tasks. Security is treated as a fundamental design consideration: you will evaluate access control models, integrity mechanisms and protection strategies against threats such as SQL injection, and explore how databases contribute to resilient, trustworthy digital infrastructure. Professional, ethical and legal responsibilities are addressed throughout, including GDPR compliance and the role of responsible data governance in building sustainable organisational systems.

Develop a critical understanding of Object-Oriented Programming (OOP) and apply it to build robust, reusable and maintainable software systems. You will model real-world problems using classes and objects, putting core principles such as encapsulation, inheritance and polymorphism into practice through structured programming activities. Moving beyond the fundamentals, you will explore advanced design patterns, exception handling, concurrency and multi-threaded programming, strengthening the reliability and performance of your software solutions. You will also critically evaluate the growing role of generative AI within programming workflows, forming a well-informed view of its benefits, limitations and implications for professional software engineering.

Gain a critical understanding of cloud computing and how it powers modern digital infrastructure, exploring the full spectrum from bare-metal servers through to Software-as-a-Service (SaaS) platforms. You will develop practical Linux administration skills as the foundation of cloud infrastructure, gaining hands-on experience with virtualisation, containerisation and DevOps deployment approaches alongside infrastructure-as-code practices. Designing cloud solutions that are secure, resilient and effectively governed is a core focus, and you will evaluate how cloud systems are deployed and managed in real organisational contexts. The module also encourages critical reflection on the sustainability and ethical responsibilities of cloud technologies, including energy-efficient system design aligned with the United Nations Sustainable Development Goals.

Develop the knowledge and skills to understand, critically evaluate and design rigorous research across Computer Science, Data Science and Cybersecurity. You will explore key research paradigms and methodologies including experimental design, statistical analysis, simulation, and user and security studies, applying these to critique published research and plan investigations of your own. A central task is designing a hypothetical research study, where you will address questions of validity, reproducibility and ethics while justifying your methodological choices through academic writing and scholarly discussion. By the end of the module, you will have the research foundations to contribute meaningfully to computing innovation, whether in academic or professional contexts.

Undertake an in-depth, independent critical review of a selected area of emerging technologies relevant to your programme pathway and research interests, developing advanced skills in systematic literature searching, critical evaluation and scholarly synthesis. Guided by taught sessions on literature review methodology and academic conventions, you will design and implement a rigorous search strategy, critically appraise scholarly sources and identify key themes, debates and knowledge gaps at the forefront of your chosen field. You will communicate your findings through both a written literature review and a presentation, demonstrating the analytical and scholarly communication skills that mark out a confident, research-informed practitioner. The module prepares you to engage with rapid technological change and contribute to emerging conversations in your discipline.