Module Catalogue 2025/26

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

To reinforce the computing in Python studied as part of physics laboratories at Stage 1, and to provide the foundations for candidates to undertake independent programming activities.

To introduce a range of mathematical techniques relevant to physical problems using Python, including methods that will be useful towards project work elsewhere in the programme.

Module Summary
Computing methods are of great use in a wide range of applications in physics. This module builds on the methods introduced at Stage 1, introducing additional techniques, some of increasing computational sophistication, with broad applications in a physics context. In implementing these methods, students will attain an increased competence with Python programming, and an increasing ability to use such methods independently, towards project-orientated goals. The module supports the development of a range of transferable skills including problem solving, computer literacy, data analysis and presentation.

Outline Of Syllabus

The module covers a range of computational strategies including:
- Reading, querying and manipulating data, such as working with formats regularly used for physics research.
- Data visualisation and plotting, such as plots in 2D and 3D and production of high-quality academic plots.
- Arrays and matrices in Python, including linear algebra techniques such as required for solving eigenvalue problems.
- Curve fitting, building on the foundations established at stage 1.
- Numerical approaches to root finding.
- Numerical derivatives through finite difference, and related techniques of numerical integration.
- Numerical solution of ordinary differential equations and applications to physical systems.

Intended Knowledge Outcomes

Students will consolidate and expand their knowledge of numerical methods and the application of these methods to physics problems. Including specific knowledge of Python and an algorithmic approach to computing.

Intended Skill Outcomes

Students will consolidate and expand their practical skills in using Python to solve problems in a widening range of applications in physics. They will also develop increasing ability to program independently, less constrained by specific set problems

Students will develop skills across the cognitive domain (Bloom's taxonomy,2001 revised edition): remember, understand, apply, analyse, evaluate and create.

Students will develop a range of transferable skills:
• Critical thinking skills to consider the role of AI in the programming workflow.
• Presentation of a range of data types.
• Logical problem solving strategies, such as expressed in both mathematics and coding.
• Critical evaluation of data (e.g. numerical significance and uncertainties).
• Computer literacy.

Teaching Rationale And Relationship

Lectures and problem classes are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practicals are used to help develop the students’ abilities at applying the theory to solving problems.

The teaching methods are appropriate to allow students to develop a wide range of skills, from understanding basic concepts and facts to higher-order thinking.

Assessment Rationale And Relationship

A substantial examination is appropriate for the assessment of the material in this module. The format of the examination will enable students to reliably demonstrate their own knowledge, understanding and application of learning outcomes. The assurance of academic integrity forms a necessary part of the programme accreditation.

The coursework assignment allows the students to develop their problem-solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; this assessment has a secondary formative purpose as well as a primary summative purpose.

General Notes

N/A