MSc Data Science

This module develops the statistical reasoning and practical analytical skills required for advanced study and professional data science practice in AI and Data Science. You will learn how to explore, model, test, and interpret data using statistical methods that underpin modern intelligent systems. Across the module, you will study core topics including descriptive statistics, probability distributions, correlation and regression, hypothesis testing, and statistical classification. Weekly lectures are complemented by hands-on R workshops, enabling you to apply each concept to realistic datasets and data science problems. The module is centred on moving from raw data to robust, evidence-based conclusions. You will develop confidence in selecting appropriate statistical methods, interpreting outputs, evaluating assumptions, and communicating findings clearly within applied AI and Data Science contexts. Emphasis is placed on reproducible analysis, critical evaluation of results, and the appropriate use of statistics to support trustworthy decision-making. By the end of the module, you should be able to use statistical reasoning and R-based methods to support data exploration, predictive modelling, and rigorous analytical reporting.

This module introduces you to the study of complex networks and the methods used to analyse and optimise them. Complex networks appear in many real-world settings, including social media, transport systems, biological systems, communication infrastructures, and organisational networks. By examining how entities are connected and how information, influence, or resources move through these connections, network analysis helps us understand the structure, behaviour, and performance of complex systems. Throughout the module, you will explore key concepts and techniques for analysing network structure, identifying important nodes and relationships, and using network-based evidence to support decision making and optimisation. You will also consider how methods from data science and artificial intelligence can be used to improve information exchange and performance within complex networks. A practical focus is maintained throughout the module through the use of SAS Viya, which you will use to visualise, analyse, and interpret network data from a range of application areas. By the end of the module, you will have developed both conceptual understanding and practical skills in working with complex networks and communicating network-based insights effectively.

This module introduces you to the core principles and practices of Data Science using the R programming language. The module focuses on the practical skills needed to work effectively with data, from importing and tidying data through to transforming, visualising, modelling, and communicating results. You will learn how to use a modern data science workflow to explore datasets, identify patterns, generate insights, and present findings clearly. The module emphasises hands-on work with data and develops your confidence in using R and its supporting tools to solve realistic data problems. Alongside technical skills, you will also consider issues such as reproducibility, clarity of communication, and the importance of well-structured analytical workflows. By the end of the module, you will have developed a practical understanding of key data science techniques and the ability to apply them in a structured and professional way. The module provides an important foundation for further study in analytics, visualisation, machine learning, and applied data science projects.

This module introduces you to the principles and practice of data visualisation as a powerful way of exploring, understanding, and communicating data. As datasets become larger and more complex, effective visualisation plays an increasingly important role in helping users identify patterns, trends, relationships, and insights that may not be obvious from raw data alone. You will explore how data can be presented in clear, meaningful, and interactive ways to support analysis, learning, communication, problem solving, and decision making. The module will help you develop both theoretical understanding and practical skills in data visualisation, including the design of effective visual representations, the principles of good visual communication, and the use of interactive techniques to support data exploration. Throughout the module, you will use specialist software, SAS, to apply visualisation methods in practice. Weekly activities will give you opportunities to work with data, experiment with visual techniques, and build confidence in designing and evaluating visual outputs. By the end of the module, you will have developed an understanding of key concepts in interactive data visualisation and presentation, as well as practical experience of using SAS to create visualisations for analysis and communication, including in your final assessment.

This module helps you develop as a responsible, reflective and employment ready computing professional. It has been designed to support your transition from academic study into professional practice by combining digital citizenship, professional skills and authentic work related experience. Through this module, you will explore what it means to act as a good digital citizen in contemporary computing and data driven environments. You will consider the ethical, legal and professional issues that shape practice in areas such as artificial intelligence, data use, privacy, GDPR, digital regulation, responsible innovation and professional conduct. You will examine how computing professionals make decisions that are not only technically effective, but also ethical, inclusive and socially responsible. Alongside this, the module develops the practical skills needed to succeed in the workplace. You will strengthen your communication, teamwork, reflective practice, professional presentation and career planning skills, while learning how to present yourself effectively in professional settings. A key feature of the module is the opportunity to engage in authentic work experience through flexible placements, micro-internships, live briefs or related professional activities. These experiences are designed to help you apply your learning in real-world contexts, build confidence and develop insight into the expectations of employers and the wider profession. By the end of the module, you will have a stronger understanding of your professional identity, your responsibilities as a computing practitioner, and the standards expected in modern digital workplaces. The module will help you connect your academic learning with career development and prepare you to contribute positively, ethically and professionally in a wide range of computing related roles.

This module provides you with the opportunity to undertake a substantial independent project in an area of Data Science that reflects your academic interests, career goals, and specialist knowledge developed during your course. It is designed as a major integrative exercise in which you will apply and extend the knowledge, skills, and techniques gained from earlier modules to investigate a complex problem in depth. Working under the guidance of an academic supervisor, you will identify, plan, and carry out a project that may involve research, analysis, design, development, implementation, and evaluation within a Data Science context. Your project may focus on areas such as data analysis, machine learning, visualisation, predictive modelling, data engineering, responsible data use, or the application of data science methods to a real-world problem. The module requires you to demonstrate independence, critical thinking, and professional judgement as you define a project, select appropriate methods, manage your progress, and evaluate your outcomes. You will be expected to engage with relevant literature, justify your methodological choices, and produce a substantial artefact supported by rigorous analysis and reflection. Through a combination of workshops, independent learning and supervisory meetings, the module supports you in developing your project from initial idea to final submission. By the end of the module, you will have completed a significant piece of work that demonstrates your ability to undertake advanced study or professional practice in Data Science.

This module introduces you to the core concepts, methods, and applications that underpin Artificial Intelligence. It explores how intelligent systems are designed to perceive information, represent knowledge, reason about problems, learn from data, and make decisions in complex environments. The module provides a foundation for understanding both classical and contemporary approaches to AI, including search, optimisation, knowledge representation, reasoning, uncertainty, and machine learning. You will examine the principles that allow AI systems to solve problems, support decision making, and operate in real-world contexts. Alongside the technical foundations, you will also consider the strengths, limitations, and wider implications of Artificial Intelligence, including issues of ethics, responsibility, and professional practice. By the end of the module, you will have developed a broad understanding of the key principles of AI and how they are applied across a range of domains. This module provides an important foundation for further study in specialist areas such as machine learning, data science, robotics, intelligent systems, and generative AI.

In order to understand and make predictions researchers need to be able to not only assess, examine and interpret data, but also be able to communicate their findings in ways that conveys empirical rigor and trustworthiness. This module will set learners on the path to scientific success by supporting the development of competencies such as the formulation of research ideas, the design of research studies that enable the collection and analysis of valid data whilst operating within the parameters of ethical principles. Students will develop their analytical literacy, learn how to perform systematic and objective literature searches and reviews, to design robust methodologies and apply appropriate data analyses to test research questions. At the end of the module, students will submit a research proposal which will evidence their competency in designing a research study, and selecting an appropriate method. Students will benefit from knowledge of a number of transferable skills such as decision making, problem solving, data management, critical appraisal of written content and report writing skills.

This module prepares you to design, plan, and justify a substantial Masters level project in a computing related discipline. It introduces the principles of research design, academic and professional enquiry, and project planning in areas such as Computer Science, Data Science, Artificial Intelligence, and Cyber Security. You will learn how to identify and refine a suitable research or development problem, review and evaluate relevant literature, formulate research questions or project aims, and select appropriate methods for investigation. The module will also help you develop the professional skills needed to manage a project effectively, including planning, risk awareness, ethical consideration, communication, and reflective practice. Alongside research methods, the module explores professionalism in computing, including ethical and responsible practice, legal and regulatory awareness, data and AI ethics, cyber responsibility, and professional standards expected in industry and research environments. You will consider how computing professionals design and conduct work in ways that are rigorous, responsible, and aligned with the needs of stakeholders. The assessment for the module is a research proposal in which you will define and justify a project idea, demonstrate awareness of relevant literature and methods, and present a realistic project plan. This will help prepare you for your final MSc project and for professional practice in technical and research led roles.