Module manager: Dr Francesca Pontin
Email: f.l.pontin@leeds.ac.uk
Taught: Semester 1 Feb to 31 May (4mth)(adv yr), 1 May to 31 July View Timetable
Year running 2024/25
Students should normally have completed the PGCert year of the programme before attempting this module, or be able to evidence equivalent prior learning through other educational programmes or work experience.
| GEOG5990M | Programming for Geographical Information Analysis: Core Skil |
This module is not approved as an Elective
This module provides foundation level skills in computer programming. It introduces programming and reproducible data science practice in a general and in a geographical context. It encourages reproducible software development through: the application of software licences; the production of well documented source code; software testing; version control; and the production of user documentation. It is based on the development of software for geographical data processing and visualisation in a series of supported practical exercises.
This module seeks to:
- Enable students to feel confident in programming, including addressing errors and the tools to address common issues in computer programming and in developing software.
- Provide an opportunity for students to learn about and apply steps of ‘the data science process’ in different spatial and non-spatial contexts
- Develop a clear understanding of sustainable software development practice.
- Develop awareness of useful resources for developing software.
- Provide an opportunity to practise developing well-tested, well-documented source code and delivering a package of software.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Demonstrate an understanding of foundational level computer programming
2. Apply practical skills in sustainable software development
3. Explain ‘the data science process’ and how to apply it to a research question to ensure robust, reproducible research.
Skills learning outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
4. Independently code in a programming language
5. Write code to understand, interpret, analyse and manipulate numerical & spatial data.
6. Experience in critically applying spatial data science methods to ensure robust academic/industry research.
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 8 | 2 | 16 |
| Individual Support | 8 | 1 | 8 |
| Independent online learning hours | 48 | ||
| Private study hours | 78 | ||
| Total Contact hours | 24 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Formative feedback will be provided during practical activities where students will be encouraged to post outputs to devoted unit-by-unit discussion boards. This will allow for peer critique in addition to staff comments. Note that the outputs requested here will differ from those required as part of the summative assessments.
The module leader will also be on hand to provide support (email / Teams / discussion board / etc) during the teaching weeks, in advance of assessment. Whilst not directly formative assessment, this will ensure that the students receive feedback / support on matters of need.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Coursework | 70 |
| Assignment | Coursework | 30 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
There is no reading list for this module
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team