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Discover courses across every level โ from primary school to professional development.
1,187 courses available
Information Technology
Enterprise Systems Integration (Year 3)
Covers how large organisations integrate disparate information systems (ERP, CRM, and other platforms) to function as a coherent whole.
Cyber Security
Digital Forensics (Year 3)
Covers the principles and process of collecting, preserving, and analysing digital evidence following a security incident, within a legally defensible framework.
Cyber Security
Malware Analysis (Year 3)
Introduces the techniques used to analyse malicious software to understand its behaviour, origin, and impact, at a conceptual and practical level.
Cyber Security
Security Operations and Incident Response (Year 3)
Covers the operation of a security operations centre and the structured process of responding to and recovering from a security incident.
Cyber Security
Cloud Security (Year 3)
Covers the security considerations specific to cloud computing environments, including shared responsibility models and cloud-specific threats.
Cyber Security
Application Security (Year 3)
Covers the principles and practices of building and testing secure software applications, including common vulnerability classes and secure coding practices.
Data Science
Big Data Technologies (Year 3)
Introduces the tools and architectures used to store and process datasets too large for traditional single-machine approaches.
Data Science
Advanced Machine Learning (Year 3)
Builds on introductory machine learning to cover ensemble methods, model tuning, and techniques for handling complex real-world datasets.
Data Science
Data Engineering and Pipelines (Year 3)
Covers the design and construction of data pipelines that reliably move and transform data from source systems into analysis-ready form.
Data Science
Time Series Analysis (Year 3)
Covers the statistical methods used to analyse and forecast data that changes over time, from classical decomposition to modern forecasting techniques.
Data Science
Natural Language Processing for Data Science (Year 3)
Covers practical techniques for extracting insight from text data, from preprocessing through to sentiment analysis and topic modelling.
Artificial Intelligence
Deep Learning (Year 3)
Builds on neural network fundamentals to cover deep architectures including convolutional and recurrent networks and their training challenges.