Module manager: Dr Arjan Gosal
Email: a.gosal@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
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.
This module is not approved as an Elective
Environmental Data for Global Challenges equips students with advanced analytical skills tailored to the unique challenges of environmental data, which often encompasses complex, multi-dimensional, and multi-domain characteristics. In this module, students are introduced to various data sources—spanning atmospheric, aquatic, and terrestrial environments—and learn to interpret these data using advanced analysis techniques applied through visual programming – negating the need for prior programming skills. The course emphasises predictive modelling, real-time data processing, and data visualisation to help students derive actionable insights across a spectrum of environmental domains with issues linked to global environmental challenges. The module is designed to develop critical thinking skills and understanding of how to use large scale environmental data. A variety of current environmental challenges are used to contextualise the critical thinking, knowledge, and technical skills taught in the programme, such as air quality monitoring, forest fire risk analysis, and climate change impacts.
This module seeks to:
1. Equiping students with advanced skills in handling complex environmental data, emphasising multi-source integration, predictive analytics, and real-time data processing.
2. Developing students' critical understanding of unique environmental data characteristics and the ethical implications of data usage and analysis in addressing global environmental issues.
3. Enhancing competency in creating actionable insights from environmental data, using advanced data science methods that align with contemporary environmental challenges.
On successful completion of the module students will have demonstrated the following learning outcomes:
1. Identifying, processing, and combining diverse environmental datasets from various domains such as atmospheric, aquatic, and terrestrial systems.
2. Applying predictive analytics to environmental data though visual programming to to generate models that inform decision-making in environmental contexts.
3. Interpreting and communicating environmental data insights through effective data visualisations, written analysis, and presentation strategies that effectively convey patterns and findings to diverse audiences.
4. Evaluating data quality and ethical considerations in environmental data use through critical assessment of sources, accuracy, and limitations.
Skills Learning Outcomes
5. Technical Skills - Demonstrate proficiency in environmental data processing, statistical analysis, and visual programming techniques for predictive modeling, enhancing core literacies and information technology skills.
6. Work-Ready Skills - Show understanding of data governance, ethical decision-making, and responsible data use in environmental applications, developing problem-solving, ethical reasoning, and information searching capabilities for roles requiring accountability in environmental data handling and analysis.
7. Digital Skills - Apply digital creation tools to develop innovative solutions for environmental challenges, demonstrating problem-solving abilities through data analysis and visualisation.
8. Sustainability Skills - Integrate principles of environmental responsibility with anticipatory and future thinking to evaluate potential scenarios and long-term impacts when addressing global environmental issues.
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 through optional ‘homework tasks’ related to the practicals that the students can submit online. Discussion boards will be used, adding a collaborative learning opportunity for online students, whilst facilitating peer-to-peer knowledge exchange and community building.
| 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
The reading list is available from the Library website
Last updated: 19/03/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team