Module manager: Prof Paul Morris
Email: P.J.Morris@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
| SOEE2253 | Numerical Methods for Earth and Atmosphere |
| SOEE2810 | Data Analysis and Visualisation |
GEOG3020
This module is not approved as a discovery module
You will be provided with a solid foundation in computer programming and introduced to the most common numerical methods and their implementation in Python. You will also learn how to handle and report data with uncertainties in an appropriate manner. They are required to think and write about the role of simulation in physical geography.
This module aims to provide you with a key background in statistical, programming, and mathematical skills that are essential for Geographers in the workplace.
On completion of this module, students will:
- Be proficient in the use of computer programming (using Python) for undertaking a flexible range of tasks.
- Know how to analyse the data they collect and how to draw inferences.
- Gain practical experience of how environmental data is analysed, interpreted and reported.
- Solve mathematical problems with a range of numerical methods and models
- Confidently handle and report data, and select appropriate numerical analysis for datasets etion of this module, students should be able to ...
On completion of this module students will have demonstrated the following learning outcomes relevant to the subject::
1. Design and execute efficient, simple computer programs (in Python) for reading, manipulating, analysing, plotting and outputting data.
2. Diagnose and correct errors in code.
3. Design and implement computer programs to solve numerical problems that would be impossible or time-consuming to complete by hand, and to understand the limitations of the programs.
4. Derive expressions for simple numerical methods.
5. Solve mathematical problems via recall or use of the appropriate numerical method for finding the roots or optima of functions, solving linear systems of equations, interpolating values, performing numerical integration and differentiation, and solving initial-value and boundary-value problems.
6. State the advantage and disadvantages of different numerical methods and, where appropriate, conditions required for convergence.
7. Handle and report data with uncertainties in an appropriate manner.
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 2 | 2 | 4 |
| Lecture | 19 | 1.5 | 28.5 |
| Practical | 10 | 2 | 20 |
| Private study hours | 147.5 | ||
| Total Contact hours | 52.5 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team