Date of entry

September 2026

two students studying at a desk

Teaching

In maths you will learn by methods such as:

  • lectures
  • tutorials with debates and discussions
  • workshops
  • computer assisted learning
  • practical classes during which you design, carry out and analyse experiments

We also teach the use of professional mathematical software packages in order to allow you to explore maths beyond the limits of traditional teaching. This also prepares you for the way in which mathematicians work across various industries.

For psychology, throughout Levels 1-4 you will engage in research, moving from assigned and specified activities or studies to more independent practical work.

Most modules at Levels 1 and 2 have two lectures and one tutorial or workshop each week, including online workshops.

At Level 3 you will have lectures each week and tutorials centring around coursework. At Level 4, teaching is in much smaller groups and our module options are based on the research expertise of our staff, and vary slightly from year to year.

In your final year a supervisor will oversee a dissertation project where you will apply your research skills.

Assessment

Assessments are carried out through coursework and exams, and vary in type and weighting from module to module. Methods of assessment include:

  • exams
  • continuous assessment
  • coursework
  • homework
  • computer assignments
  • class tests
  • projects
  • essays
  • posters
  • oral presentations
  • carrying out and reporting psychological experiments
  • scientific practical reports
  • online multiple-choice questions
  • workbooks

Core Modules

These modules are an essential part of your course.

Module code: MA11001 Credits: 20 Semester: Semester 1

Mathematics 1A is an introductory mathematics module.

It covers fundamental concepts such as algebra, calculus, and geometry.

The topics covered provide a solid foundation for more advanced studies. Skills covered include:

  • learning to solve equations
  • understand functions
  • analyse graphs

Core Modules

These modules are an essential part of your course.

Module code: MA21001 Credits: 20 Semester: Semester 1

You will learn about a key structure called a vector space.

Some of the simplest vector spaces are subsets of the 3-dimensional space in which we live. There are many other sets that share this structure.

You will learn more about solving differential equations.

Core Modules

These modules are an essential part of your course.

Module code: MA31002 Credits: 15 Semester: Semester 1

This module explores an area of calculus involving derivatives of functions. Derivatives give the rate of change of a function. For example, speed is the rate of change of distance, while acceleration is the rate of change of speed. Differential equations are equations that involve functions and their derivatives.

Optional Modules

You need to choose one or more of these modules as part of your course.

Module code: MA33001 Credits: 10 Semester: Runs during the summer

You will gain hands-on experience applying your mathematical knowledge in a professional environment. You'll work with an organisation in an industry such as finance, technology, research, or engineering. This will allow you to tackling real-world problems. You will make use of analytical and computational skills that you have previously gained.

Core Modules

These modules are an essential part of your course.

Module code: MA40001 Credits: 30 Semester: Both Semester 1 and 2

You will independently study a specific mathematical topic. This will allow you to become a logical problem solver. These skills are highly sought after in a wide range of industries.

Optional Modules

You need to choose one or more of these modules as part of your course.

Module code: MA42008 Credits: 15 Semester: Semester 1

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.

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