The final year focuses on individual and group project modules offering a chance for you to study a topic of your choice in depth. Also you may opt to study a school-based placement module. You’ll additionally have a wide choice of modules which cover a varied range of topics from pure and applied mathematics and operational research, to theoretical physics and statistics.
Mathematical Sciences in Context (MATH3601)
This module is designed to be an alternative to the individual project. In the module students perform structured investigations on a variety of advanced topics in mathematics and statistics. Students give oral presentations and write up a journal style article on their work. Some of these articles have been published in the University of Plymouth’s Student Scientist journal.
Professional Experience in Mathematics Education (MATH3603)
This module provides an opportunity for final year students to gain experience in teaching and to develop their key educational skills by working in a school environment for one morning a week over two semesters.
Geometry and Algebra (MATH3604)
A review of group theory leads into an exploration of plane affine, hyperbolic, and projective geometries, all from the Kleinian point of view. Then an introduction to rings and fields is given with applications in geometry emphasised. These topics are key ideas in the study of pure mathematics.
Partial Differential Equations (MATH3605)
This module introduces partial differential equations using real-life problems. It provides a variety of analytic and numerical methods for their solution. It includes a wide range of applications including heat diffusion and the Tsunami wave.
Classical and Quantum Mechanics (MATH3606)
All of physics and a large part of applied mathematics is based on classical mechanics and its extension to quantum theory. This module introduces key ideas of these topics to students with a mathematics background. An overarching theme is the key role of symmetry, both for classical motion and quantum behaviour.
Optimisation, Networks and Graphs (MATH3609)
This module introduces the mathematics of continuous and discrete optimisation. It provides the theoretical background and practical algorithmic techniques required to model and solve a diverse range of problems.
Electrodynamics and Relativity (MATH3611)
This module introduces Maxwell's theory of electromagnetism and Einstein's theory of special relativity. It includes a wide range of applications of electromagnetism, the Lorentz transformations and some of the apparent paradoxes of relativity together with their resolution. It also explains why E = mc^2.
Data Modelling (MATH3613)
This module provides an employment relevant tool box of statistical modelling techniques and a rigorous treatment of the underlying mathematics. The Bayesian framework for statistical inference is developed and compared with the classical approach. Important computational algorithms, including Markov Chain Monte Carlo, are described. Application-rich modelling problems are considered.
Medical Statistics (MATH3614)
The content includes the design and analysis of clinical trials, including crossover and sequential designs and an introduction to meta-analysis. Epidemiology is studied, including case-control and cohort studies. Survival analysis is covered in detail. Computer packages are used throughout.
Professional Experience in Industry (MATH3616)
This module provides an opportunity for students to gain experience in applying mathematics in a commercial setting by undertaking a summer placement. Students develop their skills in written and verbal communication, listening, problem solving, time management, teamwork and leadership. Recent summer placement providers include Babcock International, BMW Group, Chess Dynamics Ltd and South West Water.
Financial Statistics (MATH3623)
This module introduces students to the concepts and methods of financial time series analysis and modelling and to a variety of financial applications. The module reviews the necessary univariate and multivariate time series models and inferential techniques. Model selection, forecasting and the ‘curse of dimensionality’ problem for high dimensional modelling are treated both analytically and computationally. The R programming language is widely used in this module.
Students who have identified a topic of particular interest have the opportunity to study it in a final year project. Students work individually and independently, with help and advice from a supervisor, on the chosen topic. The project is assessed through presentations and the preparation of a dissertation. This is a major piece of work and the project counts as two modules
Fluid Dynamics (MATH3629)
Fluid flow problems are at the heart of systems ranging from weather forecasting and climate models to hydroelectricity generation and aerodynamics. They are all formulated mathematically as systems of partial differential equations. These are then solved and the results interpreted for a mixture of theoretical and practical examples of both inviscid and viscous fluid flows. Applications studied include: aeronautics, ocean waves and a variety of industrial topics.