COURSE CURRICULUM

The MSc in Data Analytics is structured over four terms, offering a comprehensive progression from foundational concepts to advanced applications in the field. In Term 1, students begin with Fundamentals of Data Analytics, which lays the groundwork in statistical methods, data exploration, and the use of key tools and techniques. This is complemented by Predictive Analytics and Machine Learning using Python, where students learn to build and evaluate models that forecast trends and patterns based on historical data.In Term 2, the focus shifts to managing large volumes of data and understanding data infrastructure. Enterprise Data Warehouse and Database Management Systems introduces students to data storage, retrieval, and management techniques critical to any analytics environment. Alongside this, Big Data Analytics explores methods for processing massive datasets using frameworks like Hadoop or Spark, enabling students to gain insights from unstructured or semi-structured data sources.Term 3 introduces more specialised tools and techniques. Visualisation and Storytelling using Tableau equips students with the ability to communicate complex data clearly and persuasively through visual formats. This module is particularly valuable for roles that require translating analytical findings into actionable business insights. The term also includes Computer Vision and Artificial Intelligence, where students explore the application of AI techniques in image processing, pattern recognition, and automated systems—skills increasingly relevant in sectors such as healthcare, retail, and autonomous technology.In Term 4, students complete a dissertation, which serves as the capstone of the program. This project allows them to independently investigate a topic of their choice, applying their technical skills and research capabilities to solve a real-world problem or explore a theoretical concept in depth. The dissertation reflects each student’s ability to conduct rigorous analysis, manage a research project, and communicate findings effectively, positioning them for advanced roles in the growing field of data analytics.