Module manager: Prof. Gavin Burnell
Email: g.burnell@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2025/26
Level 1 physics
| PHAS1000 | First Year Physics Assessment |
| PHAS2000 | 2nd year Physics Assessment |
| PHAS2010 | Quantum Mechanics |
| PHAS2030 | Condensed Matter Physics |
| PHAS2040 | Electromagnetism |
PHYS2300, PHYS2320
This module is not approved as a discovery module
This module explores the concepts and applications of statistical mechanics, which are key to understanding the behaviour of small-particle systems. This module will also enable students to translate descriptions of physical problems and data analysis processes into short programs to read and manipulate data, analyse and present the results for problems relevant to physics using a programming language.
During this module students will learn the theories and concepts of statistical mechanics. Examples and applications will be used to allow students to build their understanding and application of this branch of physics, which is fundamental to explaining the macroscopic behaviour of atoms and other small-particle systems.
This module will also introduce students to statistical analysis and levels of measurement and hypothesis testing.
Computer programming is an important skill for Physics students to learn, preparing them for both higher level academic studies and a wide range of professional careers. This module further develops students’ skills in programming and focuses on applying programming to solve realistic data analysis problems in physics. This module covers tasks such as reading data files, manipulating and fitting data to theoretical models and visualising and presenting the results. For this module we use the Python programming language as it is widely used in scientific environments, freely available and provides a rich set of libraries for carrying out data analysis.
On successful completion of the module students will be able to demonstrate knowledge, understanding and application of the following:
1- Microstates, macrostates, canonical ensembles
2- Boltzmann statistics
3- Partition functions
4- Bose-Einstein statistics
5- Fermi-Dirac statistics
6- Bose-Einstein condenstation
7- Modelling simple physics situations in computer code
8- Reading text-based data files and manipulating data in computer code
9- Fitting data to simple models
10- Suitable visualisation of data and results
11- Data analysis techniques
12- Planning and execution of a piece of work over an extended period
13- Evaluation of progress through regular reporting
Skills Learning Outcomes
On successful completion of the module students will be able to do the following:
a- Manage time and plan work to meet deadlines
b- Problem solving
c- Application of appropriate mathematics
d- Coding skills
1- Macrostates and Microstates
2- Boltzmann statistics for distinguishable particles
3- Partition functions
4- Two-level paramagnet
5- Statistics of indistinguishable particles
6- Fermi-Dirac statistics
7- Bose-Einstein statistics
8- Bose-Einstein condensation
9- Computer modelling of physical systems
10- Programming for data analysis
11- Data visualisation
Methods of assessment
The assessment details for this module will be provided at the start of the academic year
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 10 | 1 | 10 |
| Lecture | 46 | 1 | 46 |
| Practical | 6 | 2 | 12 |
| Practical | 9 | 1 | 9 |
| Independent online learning hours | 23 | ||
| Private study hours | 100 | ||
| Total Contact hours | 77 | ||
| Total hours (100hr per 10 credits) | 200 | ||
The reading list is available from the Library website
Last updated: 23/05/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team