2024/25 Taught Postgraduate Module Catalogue

GEOG5990M Programming for Geographical Information Analysis: Core Skills

15 creditsClass Size: 100

Module manager: Dr Francesca Pontin
Email: f.l.pontin@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is mutually exclusive with

GEOG5003M

Programming for Geographical Information Analysis

Module replaces

Replaces GEOG5540M Introduction to Programming and Customisation as the pre-requisite for GEOG5080M Web-based GIS.

This module is not approved as an Elective

Module summary

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.

Objectives

This module seeks to:
- Enable students to develop experience 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 opporutnity to practice developing well tested, well documented source code and delivering a package of software.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:

1. Develop foundational level computer programming in geographic context.
2. Apply skills in sustainable software development
3. Demonstrate an understanding of ‘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.

Syllabus

Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module

Teaching methods

Delivery type	Number	Length hours	Student hours
Lectures	9	0.50	4.50
Practicals	9	1.50	13.50
Individual Support	9	0.50	4.50
Independent online learning hours			42.00
Private study hours			85.50
Total Contact hours			22.50
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Student progress will be monitored through formative practical tasks in workshops and by email and through normal requests for help during open office hours. In the workshops, the students are to follow a detailed set of detailed instructions with exercises and quizzes to learn how to read, test, document and develop program source code and develop their awareness of good practice. Feedback will be provided before the second assignment deadline in order for students to learn and adapt from this.



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.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Coursework	30.00
Assignment	Coursework	70.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:14:37

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