Module manager: Benjamin Mills
Email: b.mills@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2025/26
| SOEE2710 | Data Analysis and Visualisation for Environmental Applicatio |
| SOEE2931 | Advanced Skills for Geoscientists |
| SOEE5710M | Adv Data Ana & Vis for Env App |
This module is not approved as a discovery module
This module aims to provide Environmental Science 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 (particularly 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 for projects such as your final year dissertation.
This module provides key statistical and programming skills to equip Environmental Science students for the modern workplace.
On completion of this module, students will:
- Be proficient in the use of computer programming (using Python) for undertaking a flexible range of tasks.
- Know how to analyse the data they collect and how to draw inferences.
- Gain practical experience of how environmental data is analysed, interpreted and reported.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
SSLO1. Practice skills in analysis, synthesis and integration of information, and in the application of related theoretical knowledge, where relevant.
SSLO2. Understand how to apply a range of statistical techniques for hypothesis testing with numerical data and social science survey data.
SSLO3. Perform simple operations on Linux systems (e.g. moving between and managing directories, text editing).
SSLO4. Design and execute efficient, simple computer programs (in Python) for reading, manipulating, analysing (including plotting) and outputting data.
SSLO5. Diagnose and correct errors in code.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SKLO1. computer literacy on different operating systems (work ready skills)
SKLO2. the logic and syntax required for effective computer programming (work ready and academic skills)
SKLO3. programming expertise in Python (work ready, enterprise, academic and digital skills)
SKLO4. how to manipulate and plot environmental data sets (work ready, sustainability and academic skills)
SKLO5. best practise in human-readable and clear computer scripts (work ready and digital skills)
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Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 9 | 1.5 | 13.5 |
| Lecture | 10 | 1 | 10 |
| Practical | 10 | 1 | 10 |
| Practical | 10 | 2 | 20 |
| Private study hours | 146.5 | ||
| Total Contact hours | 53.5 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Type: practical help sessions with demonstrators, including weekly worksheets with solutions
frequency: weekly in S1
length: 2 hours each
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 50 |
| Coursework | Coursework | 50 |
| 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: 29/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team