Module manager: Prof Cathryn Birch
Email: c.e.birch@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2025/26
| SOEE2710 | Data Analysis and Visualisation for Environmental Applicatio |
| SOEE2810 | Data Analysis and Visualisation |
| SOEE2931 | Advanced Skills for Geoscientists |
This module is not approved as a discovery module
This module provides students with a core set of transferable computing and analytical skills to make them highly competitive in the pursuit of their subsequent careers and to facilitate their transition from Higher Education study to the workplace. Recognising that proficiency in computer programming is becoming increasingly necessary and valuable for data analysis (in particular for processing and visualising large/complex datasets), this module aims to provide a broad and solid foundation in this skill to act as a springboard for more advanced or specialist computing, both in research and the workplace. In this module, you will learn the basic computer programming skills required to analyse and plot environmental data sets, beyond what could be done using software such as Excel. The course begins with an introduction to the UNIX computer system and the basic principles of computer programming. Programming experience in the aspects of the Python language necessary for data manipulation and visualisation is developed through the course of the module. It is intended that the module will provide the basic skills required to write the custom computer programs necessary that may be necessary for your Masters project.
The main objectives of this modules are to learn to:
1. Navigate a Linux environment (achieved through accessing course material and datasets using the Linux terminal within the Jupyter server)
2. Design and implement medium-complexity computer programs in Python to read in, manipulate, compare and plot a range of environment-related data sets (achieved through live-coding and worksheet classes)
3. Make a critical assessment of a computer program (achieved through worksheet classes and assessment)
4. Locate and fix errors in code (achieved through worksheet classes and assessment)
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Read in, analyse and combine large environmental data sets using Python
2. Visualise environmental data sets through various types of plots using Python
3. Understand, analyse and interpret environmental model data in the context of a research project
4. Code efficiently and with good coding practice
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Information Technology (IT) skills – familiarity with Linux environment and Python programming language (Digital skills)
2. Problem solving and analytical – ability to design and implement code logic that produces effective and efficient computer programmes (Work ready and Digital Skills)
3. Information searching – ability to use the internet to find coding examples and help pages, understand them and implement solutions into own code. (Digital Skills)
4. Research skills – ability to analyse model data and observations and present results in a clear way through the plotting functionality in Python (Work ready, Academic and Digital Skills)
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practical | 11 | 2 | 22 |
| Seminar | 1 | 2 | 2 |
| Seminar | 8 | 1.5 | 12 |
| Private study hours | 114 | ||
| Total Contact hours | 36 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Worksheets are completed in class weekly by students. Staff and demonstrators are present to discuss their coding, help with the tasks and provide feedback on their solutions. This weekly formative feedback will help with performance in the assessed coursework.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 40 |
| Coursework | Coursework | 60 |
| Total percentage (Assessment Coursework) | 100 | |
The resit is a single Jupyter notebook (same format as the two assessments) that assesses all the learning outcomes.
There is no reading list for this module
Last updated: 29/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team