Gwybodaeth Modiwlau

Module Identifier
Module Title
Numerical Techniques for Physicists (10 Credits)
Academic Year
Semester 1
Exclusive (Any Acad Year)
Exclusive (Any Acad Year)
Exclusive (Any Acad Year)
Exclusive (Any Acad Year)
Exclusive (Any Acad Year)
Reading List
Other Staff

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment 1 Hours   Online task  in-class  40%
Semester Assessment 2 Hours   Online coding test, in-class x 2  60%
Supplementary Assessment As determined by the Departmental Examination Board  100%

Learning Outcomes

On successful completion of this module students should be able to:

Demonstrate an awareness of good practice when developing code.

Solve mathematical problems utilising small programs in Python.

Construct visualisations of data in Python.

Recognise when binomial, Poisson, uniform, or Gaussian distribution describes data, and calculate their mean, standard deviation, and other expectation values.

Show ability to analyse observational data, carry out error analysis, and perform hypothesis testing.

Brief description

There are numerous mathematical problems that are either impractical or impossible to solve analytically and must instead be solved by computers using numerical techniques. Mathematical techniques are essential to Physics and, therefore, so are such numerical techniques. This module introduces Python in the broader context of the Scientific Python Stack (Scientific Libraries/Extensions to the core Python language) and statistics. Application of these techniques will be achieved through practical workshops. This module is for physics students studying abroad in semester 2 of year 2.


• Types and variables in Python

• Data structures: Lists, dictionaries and NumPy arrays

• Control Statements and Blocks: If-statements, for- and while-loop

• Organising Python code:
- Function definition and calling
- Catching and handling exceptions
- Organising code into modules

• File handling and data formats

• Visualising data (plotting) and data manipulation

• Statistics, including:
- Gaussian, Poisson and binomial distributions
- Hypothesis Testing

Module Skills

Skills Type Skills details
Application of Number The application of number is required throughout the module.
Communication Documenting Code.
Improving own Learning and Performance From feedback (automatic, through computer, and in-practical feedback from demonstrators and staff).
Information Technology Application of IT skills are central throughout the module.
Personal Development and Career planning No, though skills taught are in high demand from employers.
Problem solving Problem solving skills are required and developed throughout the module.
Research skills Using a computer. Searching the language and library documentation.
Subject Specific Skills Programming, debugging, statistical, and data analysis skills.


This module is at CQFW Level 5