COURSE CURRICULUM

Year 1 focuses on establishing a solid foundation in mathematics. It combines both theoretical and practical elements, helping students strengthen their skills in calculus, probability, mechanics, and algebra. Students are also introduced to mathematical software tools that will be valuable in later stages of their degree. Key modules include Linear Equations and Digital Skills, where students study matrix theory, systems of equations, and digital tools like LaTeX for typesetting and Excel for solving mathematical problems. Proofs and Limits covers the foundations of mathematical logic and the concept of limits, essential for future studies in real analysis and calculus. Real Analysis and Group Theory builds on calculus to introduce formal definitions of derivatives and integrals, and introduces group theory, which is vital for understanding symmetry in mathematics. Computation and Modelling focuses on mathematical modeling and introduces Python programming for solving real-world problems like classical mechanics. Probability and Multivariable Calculus provides an introduction to probability theory, reliability, and Markov chains, while also delving into calculus of multiple variables, including partial derivatives and Green's theorem. The Calculus module revisits key calculus concepts such as complex numbers, differentiation, integration, and solving differential equations.Year 2 moves on to more advanced topics, blending pure mathematics with applied methods. The Complex Functions and Linear Algebra module explores complex functions and the theory of vector spaces and linear transformations. The Differential Equations and Applied Methods module covers techniques for solving both ordinary and partial differential equations, focusing on applications in the physical sciences. In the Complex Integration and Ring Theory module, students learn about integrating functions in the complex plane and the theory of rings, essential for algebraic structures. The Mathematical Statistics module introduces distribution theory, sampling, and estimation methods, while Linear and Generalised Linear Models applies statistical theory using R for model fitting and analysis of real-world data.In Year 3, students focus on practical and specialized research. The Statistics Project allows for independent research, with students working closely with faculty members on a topic of interest. The Stochastic Processes and Time Series module delves into advanced topics in stochastic processes, such as random walks, Markov chains, and time series analysis, with an emphasis on forecasting and using R for practical applications. These modules are designed to provide a deeper understanding of statistics and its application in real-world scenarios, preparing students for advanced roles in mathematics and data science.