Module manager: Stephen Stackhouse
Email: s.stackhouse@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
A-level Mathematics or equivalent (as for entry onto programme).
SOEE1160 (Computers and Programming in Geosciences)
This module is not approved as a discovery module
Programming and Data Science introduces you to how computers work, basic computer programming, and geospatial science, with the aim that by the end you will have the foundations to create new computer programs to solve quantitative geoscience problems and visualise geoscience data. Over the module, you will learn the fundamentals of the programming language Python, geographical information systems (GIS), and UNIX-type computer systems via a series of lectures, which will provide sufficient background, coupled with extensive hands-on practical sessions where you learn by solving real geophysical problems.
The module provides students with the skills necessary to design and implement short computer programs for the purposes of analysing geophysical data and reporting the results of this analysis. It introduces geographical information systems and the representation of geospatial data. Overall, it builds the key computational skills needed for further study of geophysical and atmospheric science.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
SSLO1: Understand the principles behind the visualisation and analysis of geospatial data.
SSLO2: Apply UNIX tools to navigate the filesystem, interrogate files on disk and pass data between programs.
SSLO3: Design, implement, document and test Python code to process, analyse and visualise data.
SSLO4: Use notebooks, scripts and libraries to efficiently re-use existing and new code to solve geophysical problems.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SKLO1: Communicate with others using written documentation and use existing documentation to understand a technical process (work ready, digital skills, academic skills)
SKLO2: Devising technical solutions to solve problems creatively within the constraints of time and computing power (work ready skills, digital skills, enterprise skills)
SKLO3: Understand complex systems of inter-related pieces of software (work ready skills, digital skills, academic skills)
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 6 | 1 | 6 |
| Lecture | 9 | 2 | 18 |
| Practical | 2 | 4 | 8 |
| Practical | 6 | 2 | 12 |
| Practical | 9 | 2 | 18 |
| Private study hours | 138 | ||
| Total Contact hours | 62 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Students receive instant feedback during practical classes on their solutions to the programming problems and can interact directly with demonstrators to fill in any gaps of understanding or knowledge.
Feedback on the formative Python assessment will help students improve for the assessed exercise and identify if any students need extra help, or if there are any gaps amongst the cohort as a whole.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 30 |
| Total percentage (Assessment Coursework) | 30 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Unseen Practical exam (S2) | 2.0 Hrs 0 Mins | 70 |
| Total percentage (Assessment Exams) | 70 | |
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: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team