COURSE CURRICULUM

The BSc Mathematics with Data Science and AI degree offers a robust and dynamic blend of theoretical mathematics and practical applications in data science, artificial intelligence, and computational analysis. In the first year, students develop a solid foundation in core mathematical concepts such as calculus, linear algebra, discrete mathematics, probability, and data analysis. These are complemented by modules in mathematical coding and real-world applications of mathematics, equipping students with the quantitative and computational skills needed for higher-level studies and practical problem-solving. In the second year, the curriculum deepens into areas such as linear algebra and differential equations, operational research, and vector calculus, while branching into applied domains through modules like big data analysis, AI applications, and statistical data analysis. These courses help students build the analytical and technical skills necessary to extract, manipulate, and interpret complex datasets. By the third year, students explore more advanced topics in AI and statistics, including machine learning, responsible AI, and both standard and advanced optimisation techniques. These modules focus on real-world applicability, encouraging students to develop solutions to problems using ethical and sustainable AI methods. Students also gain a rigorous understanding of linear models, statistical inference, and can choose from specialist modules in financial time series, graph theory, or Bayesian methods, depending on their interests and career goals. Additionally, students select either a substantial research project or a work placement to gain practical experience and develop professional skills. Overall, the program is designed to produce highly skilled graduates with a strong mathematical core, combined with the technological fluency and ethical awareness to contribute meaningfully in areas such as data science, machine learning, finance, and artificial intelligence. The degree not only prepares students for a wide range of data-driven careers but also provides a solid foundation for further academic research or specialised professional training.