COURSE CURRICULUM

This Data Science BSc (Hons) course is structured over three years to gradually build a comprehensive understanding of data science, blending core mathematical, statistical, and computing skills with practical, real-world applications. In the first year, students are introduced to foundational topics such as calculus, algebra, programming, data handling, and statistics. Key modules include Programming: Concepts and Algorithms, Working with Data, and Probability and Statistics. These modules aim to develop core technical and analytical skills, while also covering essential professional practices like teamwork, ethics, and data governance.In the second year, students delve deeper into areas such as artificial intelligence, advanced algorithms, linear algebra, and differential equations. Modules like Artificial Intelligence and Advanced Algorithms broaden the student's problem-solving capabilities and expose them to the legal, ethical, and computational considerations in AI and software development. The Data Science module introduces tools and techniques used in big data environments, and the Linear Statistical Models module equips students with the knowledge of regression and ANOVA techniques used widely across industries. A group project further reinforces collaborative skills and practical application of concepts.In the final year, the focus shifts to advanced techniques and independent research. Students explore data visualisation methods, advanced statistical techniques such as Generalised Linear Models and Bayesian networks, and a deeper dive into machine learning, including supervised and unsupervised learning, as well as artificial neural networks. The Project Discovery module allows students to define their research focus, either individually or as part of a group, with support from academic supervisors. The course concludes with a Dissertation and Project Artefact module, where students carry out in-depth research and produce a deliverable artefact along with a comprehensive written report. This structured approach ensures graduates leave with both theoretical knowledge and applied experience, ready for careers in data science or further study.