Module manager: Stephen Stackhouse
Email: s.stackhouse@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
Students taking this module should have a strong background in mathematics, including differentiation, integration and matrices and linear systems, and be proficient in Python programming.
| GEOG3122 | Coding and Numerical Analysis |
SOEE2250 Numerical Methods and Statistics
This module is not approved as a discovery module
This module teaches students how to analyse data with uncertainties and solve numerical problems. You will be introduced to numerical methods in the lectures and implement and use these in the practical sessions.
The main objective of this module is to provide students with experience using numerical methods to solve mathematical problems, such as finding the roots or optima of functions, solving linear systems of equations, interpolating values, performing numerical integration and differentiation, and solving initial and boundary-value problems. It will also train students handle data with uncertainties appropriately.
On successful completion of the module students will have demonstrated the following learning outcomes:
SSLO1: Handle and report data with uncertainties in an appropriate manner.
SSLO2: Derive expressions for simple numerical methods.
SSLO3: Solve mathematical problems via recall or use of an appropriate numerical method.
SSLO4: State the advantage and disadvantages of different numerical methods and, where appropriate, conditions required for convergence.
SSLO5: Use, debug, and complete code to perform numerical methods and implement code to use them and write out and plot the results.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SKLO1: Select, use and adapt computer programs to solve numerical problems. (Work Ready Skills: Information technology).
SKLO2: Use reasoning and judgement to solve numerical problems. (Work Ready Skills: Critical Thinking).
SKLO3: Learn through practice, learning proactively and adopting effective learning strategies. (Work Ready Skills: Active learning)
SKLO4: Work to deadlines, and to tolerate demands and pressure. (Work Ready Skills: Working Under Pressure).
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 2 | 2 | 4 |
| Lectures | 10 | 1.5 | 15 |
| Practicals | 8 | 2 | 16 |
| Private study hours | 65 | ||
| Total Contact hours | 35 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Students will receive oral feedback on the programming in the practical sessions and written feedback on their solutions to the formative programming assessment. Students will receive oral feedback on their solutions to the problem set questions in the tutorial part of the lecture (the last 30 minutes).
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Unseen Practical exam (Semester 1) | 2.0 Hrs Mins | 30 |
| Standard exam (closed essays, MCQs etc) (S1) | 2.0 Hrs Mins | 70 |
| Total percentage (Assessment Exams) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 14/05/2026
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