COURSE CURRICULUM

The MSc Data Science program offers a comprehensive and rigorous curriculum that equips students with both the theoretical foundations and practical skills needed to tackle complex data-driven challenges across various sectors. Core modules begin with Data Mining, which introduces essential concepts in data representation, manipulation, and advanced processing, including dynamic partitioning and clustering. Students learn to develop software scripts to implement data processing and understand performance trade-offs in real-world systems. Data Science Fundamentals builds a holistic understanding of the data scientist’s role within organisations, including research design, data integration, and effective communication of findings. Programming for Data Scientists, using R and Python, ensures that both beginners and experienced coders can manage complex data science tasks, covering topics such as statistical analysis and visualisation. Statistical Foundations lays the groundwork in statistical inference, model fitting, and regression, which are critical for analysing uncertainty and making predictions. This is deepened in Statistical Learning, which explores methods like Lasso, Elastic Net, random forests, neural networks, and unsupervised learning techniques. Optional modules allow students to specialise further. Applied Data Mining expands core knowledge into domains like social networks and recommendation systems. Building Big Data Systems focuses on infrastructure and architecture to support enterprise-scale data projects. Intelligent Data Analysis and Visualisation covers end-to-end analytics using statistical modelling and machine learning in R. Optimisation and Heuristics introduces mathematical programming for decision-making in business contexts. The program culminates in a dissertation, often based on an industry placement, where students apply their accumulated skills to solve a real-world problem or conduct research, leading to a substantial written thesis. Throughout the course, students engage in practical work, case studies, and seminars with industry experts, preparing them for successful careers as data scientists in industry, research, or further academic study.