Teaching and assessment for Mathematics MMath (Hons)

Algebra involves manipulating expressions and solving equations.

Calculus focuses on change and motion, with methods to analyse rates of change and calculate areas under curves.

This module provides a refresh of the maths taught at high school. This will give you the best chance of success with university mathematics.

Mathematics 1B builds on the foundational concepts introduced in Mathematics 1A. You will be introduced to matrices. You will learn about the use of vectors in 3D geometry, integral calculus, and basic differential equations. It ensures you are well-prepared for future studies in a maths or other numerate degree courses.

This module provides an introduction to logic, proof, group theory, and number theory. Proofs are arguments that show that a mathematical statement is true. Number theory is an area of maths where we restrict the numbers used to whole numbers only. Logic underpins proof, and group theory studies algebraic structures.

This module introduces you to statistics and probability. Statistics is the science of collecting, analysing, and interpreting data, and is used to draw conclusions from data. Probability is the study of chance and randomness which can be used to predict the likelihood of an event occurring.

Solve problems using logic, probability, and strategic thinking. Learn the maths behind algorithms, decision-making, and Markov chains.

Develop analytical approaches to problem-solving and communication skills that employers value. Work on problem-solving challenges, explore scientific careers, and learn how to present with impact

Starting university is a big step and we’re here to help you make the most of it.

No matter what subject you’re studying, there are important life and study skills that will help you succeed now and in the future.

Discrete mathematics involves the study of structures that are either finite or have a form like the set of whole numbers.

This module introduces several areas of discrete mathematics. including difference equations, game theory, and combinatorics.

Previously seen concepts such as linear independence, basis and null spaces are explored. This will be in the context of matrix algebra.

Concepts such as determinants and eigenvalues will be introduced.

Learn about partial derivatives and their properties.

Study integration of multi-variable functions including techniques such as change of variables.

You will learn to use data from a sample of a population to deduce information about the average properties of the whole population.

You will also learn how to use the software package RStudio to do calculations with data.

Learn how to solve real-world planning and transport problems using maths. Build practical skills in optimisation, modelling, and decision-making.

Explore vector spaces, linear mappings, and diagonalisation. Learn the essential mathematics that underpins data science, physics, and engineering.

Master programming in Python and MATLAB through hands-on labs. Gain real skills used by scientists and engineers in industry every day. 

Now that you’ve settled into university life, it’s time to build on the foundations you set in your first year.

This second-year module is all about helping you take your development to the next level. This will cover this from a personal, academic, and professional perspective.

This module explores vector calculus, which is an area of mathematics that combines 3D geometry with calculus. Vector calculus is concerned with differentiation and integration of functions in 3D space. It can apply to either scalar-valued functions or vector-valued functions.

This module examines analysis, which is an area of pure mathematics forming the foundation for other areas of maths. The concept of convergence is also covered, which forms part of the framework used by computers to solve complicated equations for mathematical models in physics, biology, and economics.

Operational Research (OR) is a branch of applied mathematics. It looks for optimal solutions to complex real-life problems.

Business, industry, government, military, health and other sectors rely on OR for decision making . These decisions can have enormous financial implications.

Model real-world systems using numerical methods. Learn to solve equations, simulate dynamics, and code like a computational scientist.

Complex numbers are an extension of the real numbers that we use every day. Complex analysis is a branch of maths that studies functions of complex numbers. It extends the concepts of calculus to the complex plane. This module explores properties of complex functions, including differentiation, integration, and series expansions.

In finance and energy you often want to maximise profit. There are however constraints from the operational side of the business that must be adhered to. This gives rise to what are known in mathematics as constrained optimisation problems. In this module, you'll study optimisation methods to solve these problems.

Study how systems evolve over time. Learn how to model and analyse physical and biological systems using differential equations and qualitative methods.

This module explores calculus, specifically to find derivatives of functions. Ordinary Differential Equations (ODEs) are equations involving derivative terms.

Very few ODEs can be solved exactly, so methods have been developed to find approximate solutions. In this module, you will learn the basic numerical methods for calculating these approximate solutions.

Learn how maths models the motion of fluids and plasmas. Explore flow, waves, and magnetic fields using equations that explain natural and engineered systems. 

Derivatives give the rate of change of a function. If functions have more than one input variable then this gives partial derivatives. Partial differential equations (PDEs) are equations involving partial derivative terms. In this module, you will be given a broad introduction to PDEs.

Mathematical Statistics is the application of probability theory to statistics. This is an important area, providing useful connections to other areas of Mathematics. Mathematical Statistics has applications across the sciences, business, government, and more. It is a core topic in the growing area of Data Science.

In finance and energy you often want to maximise profit.

There are however constraints from the operational side of the business that must be adhered to. This gives rise to what is known in mathematics as constrained optimisation problems.

In this module, you'll study optimisation methods, to solve these problems.

Study a new topic not covered elsewhere in your degree, develop deep technical understanding, and present your findings to peers and non-experts.

Learn how to build and test machine learning models in Python using tools like TensorFlow. Develop the practical coding skills industry needs.