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AI and Data Analytics Curriculum

Curriculum details

Designed as a conversion master’s, the online MSc in AI and Data Analytics curriculum emphasises applied learning. Rather than relying heavily on examinations, you’ll complete portfolios, coursework, and project-based assessments that use real-world datasets and address industry-relevant challenges. Gain the skills needed to turn complex data into meaningful insights through hands-on experience with AI, machine learning, and real-world datasets, preparing you for the future of work.

Multiple entry and exit routes available. Take breaks when you need them and choose a qualification that best suits you:

Please note that occasionally we may make changes to our programme curriculum. You may not always study modules in the order they are listed here.

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Courses

Learning activities

Curriculum

The programme blends practical AI and data analytics skills with real-world projects, helping you develop the ability to solve complex problems, analyse diverse forms of data, and apply AI techniques in professional settings.

Core

Focusing on the design and evaluation of empirical research, this module introduces students to the methodologies and analytical approaches used to investigate questions in artificial intelligence, data analytics, and technology-driven environments. Students will explore the scientific method, research design, ethical considerations, and critical academic practice, while developing the ability to formulate research questions, collect and analyse evidence, and evaluate findings. Particular attention is given to both hypothesis-driven research and exploratory data analysis, enabling students to select appropriate methods and justify methodological choices in a range of applied contexts. The module prepares students to undertake independent research and communicate evidence-based findings to academic and professional audiences.
Focusing on the principles of programming and algorithmic problem-solving, this module develops students’ ability to design, implement, and evaluate computational solutions. Students will learn how data structures, control flow mechanisms, functions, and object-oriented programming techniques can be used to address complex problems, including search, sorting, and data processing tasks. The module also introduces the theoretical foundations of algorithm design and analysis, enabling students to assess the efficiency and scalability of different approaches. Through the application of software engineering principles, students will develop robust, maintainable, and effective code suitable for real-world computing environments.
Developing practical programming skills for language and data analysis, this module introduces students to the tools and techniques required to acquire, process, and explore data using Python. Students will learn how to collect, clean, validate, and manipulate datasets through structured and reproducible workflows, applying programming concepts to real analytical challenges. The module emphasises the use of data structures, libraries, and visualisation techniques to support exploratory analysis and evidence-based reasoning. Through hands-on engagement with data, students will develop the technical competence and critical awareness required to conduct responsible data analysis and prepare for advanced study in machine learning, natural language processing, and computational linguistics.
Examining the latest developments in artificial intelligence, this module explores how modern AI systems are designed, trained, and applied to address complex real-world challenges. Students will investigate contemporary approaches including machine learning, deep learning, generative AI, and intelligent agents, evaluating their capabilities, limitations, and practical applications across a range of domains. The module combines technical understanding with critical reflection, encouraging students to assess the ethical, societal, and organisational implications of AI technologies while developing evidence-based solutions to real-world problems.
Developing the foundations of modern data-driven intelligence, this module introduces students to the core concepts, algorithms, and workflows that underpin machine learning systems. Students will explore supervised and unsupervised learning techniques, learning how different algorithms can be applied to tasks such as classification, regression, clustering, and dimensionality reduction. The module examines approaches to model evaluation, validation, and performance assessment, enabling students to critically interpret results and identify the strengths and limitations of different methods. Alongside technical development, students will consider issues of fairness, bias, interpretability, and responsible AI, developing a critical understanding of the opportunities and challenges associated with deploying machine learning systems in real-world contexts.
Exploring the computational analysis of human language, this module develops students’ understanding of the methods, techniques, and tools used to process, analyse, and extract meaning from text. Students will investigate the core stages of the natural language processing pipeline, including tokenisation, text normalisation, parsing, named entity recognition, classification, and information extraction. The module examines both rule-based and statistical approaches to language processing, enabling students to evaluate their suitability for different linguistic tasks and applications. Through practical engagement with widely used NLP tools and libraries, students will develop the skills required to analyse language data and implement solutions to real-world language problems. The module also encourages critical reflection on the ethical and societal implications of NLP technologies, including issues of bias, privacy, intellectual property, and responsible innovation.
Focusing on the extraction of knowledge from complex and large-scale datasets, this module examines advanced data mining techniques for identifying patterns, anomalies, relationships, and trends in contemporary data environments. Students will explore methods for analysing streaming, graph-based, and spatio-temporal data, developing an understanding of the theoretical, computational, and practical challenges associated with these data types. The module critically evaluates state-of-the-art approaches to knowledge discovery and considers their application in domains characterised by rapidly evolving and highly interconnected data. Through the study of advanced mining algorithms and analytical frameworks, students will develop the expertise required to interpret and model complex data structures.
Focusing on the design and operation of scalable data systems, this module examines the architectures, tools, and technologies used to support big data processing in enterprise and research environments. Students will explore the characteristics of big data and the challenges they present for conventional data management systems, before investigating distributed computing paradigms such as MapReduce, the Hadoop ecosystem, cloud-based platforms, and NoSQL databases. The module develops practical skills in deploying and managing distributed data environments while critically evaluating alternative technologies for data storage, access, and processing. Through the study of real-world big data solutions, students will develop the knowledge required to support large-scale analytics and data-driven decision-making.
Exploring the principles and practices of effective visual communication, this module develops students’ ability to transform complex data into clear, engaging, and actionable insights. Students will examine a range of static and interactive visualisation techniques, evaluating their suitability for different data types, analytical objectives, and audience needs. The module introduces design frameworks, perception theory, and accessibility principles, enabling students to create inclusive and ethically responsible visualisations using industry-relevant tools. Through the integration of visual design and narrative techniques, students will learn how to construct compelling data stories that communicate evidence-based insights to both specialist and non-specialist audiences.
Focusing on the investigation of real-world business challenges, this module supports students in the design and delivery of an independent research project within the fields of management, marketing, consumer behaviour, entrepreneurship, innovation, or organisational development. Students will engage with the full research process, including question formulation, literature review, research design, ethical approval, data collection, analysis, and interpretation. By synthesising theoretical knowledge with empirical evidence, they will develop actionable recommendations for organisations, markets, or stakeholders. The module culminates in a substantial research report and professional presentation, enabling students to demonstrate advanced analytical, research, and communication skills.

Learn through practical, flexible online study

Study wherever you are while building practical AI and data analytics skills through an engaging online learning experience delivered through Moodle. Across nine eight-week modules, you’ll learn through a blend of videos, readings, practical exercises, independent research, discussion forums, and authentic case studies, with optional live sessions that help you connect with academics and peers. Rather than relying on traditional exams, most modules are assessed through project-based coursework, including business reports, presentations, technical projects, and a final independent research project—allowing you to build a professional portfolio that demonstrates your expertise.

Study online, apply your learning in the real world

Complete your coursework on a schedule that fits your life while working on authentic industry challenges. Through project-based assessments and real-world datasets, you’ll develop practical AI and data analytics skills that you can immediately apply in your career and showcase to future employers.

Online Student Experience

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