COURSE CURRICULUM

The MSc in Big Data offers a structured curriculum that develops both theoretical knowledge and practical expertise in handling large-scale datasets. In the autumn semester, students complete three compulsory 20-credit modules. Representing and Manipulating Data introduces key concepts for managing and processing data efficiently, while Commercial and Scientific Applications explores how big data technologies are applied across industry and research contexts. Mathematical and Statistical Foundations provides the essential quantitative skills required for advanced data analysis and machine learning. In the spring semester, students progress to more advanced areas, studying Machine Learning, which equips them with the ability to build predictive models and apply algorithms to large datasets. Cluster Computing introduces distributed systems and parallel processing, teaching students to manage big data at scale, while Relational and Non-Relational Databases covers SQL and NoSQL systems, ensuring students can design and work with diverse database technologies. The summer semester is dedicated to a substantial 60-credit dissertation project, where students can choose between the Dissertation Project or the Professional Doctorate First Stage. This allows them to apply their knowledge to either academic research or industry-focused challenges. The course ensures graduates are equipped with advanced technical and analytical skills to succeed in the growing field of big data science.