COURSE CURRICULUM

The MSc in Data Analytics equips students with advanced technical and analytical skills to address complex problems in both business and technical contexts. Core modules provide a strong foundation in Statistics and Optimisation, Data Mining and Machine Learning, Analytics Programming and Data Visualisation, Deep Learning and Generative AI, as well as Data Governance, Ethics and Sustainability. These modules ensure that graduates develop expertise in key analytical techniques, programming skills, and an understanding of responsible data management practices. Additionally, students undertake a Research Elective and a Research Practicum or Internship, allowing them to gain hands-on experience applying theory to real-world challenges, enhancing their research, problem-solving, and professional skills.The program includes a choice of two electives from Data Intensive Scalable Systems, Modelling and Simulation, or Domain Applications. Full-time students over three semesters select one elective in semester two and another in semester three, while part-time students over four semesters choose two electives in semester three. This flexibility allows learners to tailor their studies to specific interests, whether in scalable system design, simulation techniques, or domain-specific analytics applications. Elective availability depends on student numbers, ensuring meaningful engagement and collaborative learning experiences.Assessment across the MSc is a balanced combination of project work and examinations, typically weighted as 50% continuous assessment and 50% exam, depending on the module. This structure supports the development of practical, applied skills alongside theoretical understanding, ensuring that graduates can confidently interpret data, implement analytical solutions, and communicate insights effectively. Upon completion, students are well-prepared for careers in data analytics, business intelligence, and related fields, with the ability to apply advanced analytical methods, leverage AI and machine learning tools, and make data-informed decisions while adhering to ethical, sustainable, and secure practices in data management.