COURSE CURRICULUM

In Year 1, the focus is on building a strong foundation in mathematics, with modules that cover a range of essential topics. Calculus I introduces students to fundamental calculus concepts such as limits, derivatives, and integrals, and emphasizes problem-solving skills. Computational Mathematics equips students with practical skills in Python and MATLAB, enabling them to apply these programming tools to solve mathematical problems, implement algorithms, and visualize data. Linear Algebra offers an in-depth understanding of matrices, vectors, and linear transformations, focusing on both visual and algebraic perspectives. The Mathematical Foundations module is centered around proof techniques, helping students develop logical reasoning and mathematical language skills. In Mathematical Models, students apply mathematics to real-world scenarios, from biology to physics, learning to model problems and interpret solutions. Finally, Statistical Theory and Methods I introduces probability, statistical inference, and data analysis, with a focus on regression modeling using Minitab software.Year 2 builds on this knowledge with more specialized topics. In Applications of Mathematics and Graduate Skills, students tackle real-world mathematical problems, developing career-oriented skills such as problem-solving, report writing, and job application strategies. Calculus II expands on the foundations laid in Year 1, introducing advanced topics like multi-variable calculus, Fourier series, and the solution of differential equations. Statistical Theory and Methods II delves deeper into advanced data analysis techniques, including estimation, sampling, and the application of complex statistical models.In Year 3, students apply their learning to more specialized fields. Statistics Methods in Health Research and Social Science focuses on using statistical methods in health and social science research, covering topics such as population sampling, life tables, and clinical trials. The Statistical Learning module introduces students to modern statistical learning techniques, using tools like R or Python to analyze large datasets and apply advanced regression, clustering, and forecasting methods. These modules prepare students for careers in applied mathematics, statistics, and data science, with a particular focus on real-world applications in various industries.