Module manager: Guy Ziv
Email: g.ziv@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
This module is not approved as an Elective
The Environmental Data Science Project enables students to apply data science techniques to contemporary environmental challenges. Students may explore topics from their programme or investigate emerging issues. The project comprises two core elements: a Programmatic Analysis (Jupyter Notebook with supporting files) and a scientific written report with executive summary. This submission requires dissertation-level quality and effort. The module provides hands-on experience in environmental data science, preparing students for professional practice in this field.
Upon completion of this module, students will have demonstrated their ability to tackle contemporary environmental challenges through applied data science. They will have shown mastery in project management skills whilst critically engaging with relevant literature, conducting rigorous analysis, evaluating findings, and communicating results effectively to diverse audiences. This capstone experience draws together the technical and analytical skills developed throughout the programme, allowing students to showcase their proficiency in addressing real-world environmental problems through data-driven approaches.
Subject Specific learning Outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Critically Evaluate relevant literature and data sources to contextualise an environmental data science problem.
2. Apply appropriate data science techniques to analyse complex environmental datasets, demonstrating both theoretical understanding and practical proficiency.
3. Design, plan and execute an independent environmental data science project from inception to completion.
4. Synthesise and communicate analytical findings effectively through different formats tailored to diverse audiences.
5. Evaluate the limitations and implications of data analysis methods and results in addressing real-world environmental challenges.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
6. Work ready skills: Demonstrate effective project management through independent working, efficient time management, and stakeholder engagement; communicate complex findings clearly to diverse audiences using appropriate formats and visualisations.
7. Academic skills: Critically evaluate information from multiple sources, synthesise scientific literature, and apply ethical frameworks to research decisions; demonstrate reflective practice in problem-solving approaches.
8. Digital and technical skills: Apply advanced data manipulation, analysis and visualisation techniques using appropriate programming tools; demonstrate adaptability in selecting and implementing digital solutions to environmental problems.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 5 | 0.5 | 2.5 |
| Lectures | 1 | 2 | 2 |
| seminars | 3 | 1 | 3 |
| Practicals | 1 | 2 | 2 |
| Private study hours | 590.5 | ||
| Total Contact hours | 9.5 | ||
| Total hours (100hr per 10 credits) | 600 | ||
1. Dissertation supervisors will provide formative feedback during supervision meetings, and through contact via email if the student require additional help.
2. The formative poster submission has a dual purpose, serving as an interim check in, allowing supervisors to check student progress and any potential problems, as well as allowing students to get formative feedback from supervisors, and peers. The online format will allow fixed windows of time where students can view and comment on their peers' work. The online format of the poster presentation allows for a greater number of staff and students to view the posters.
3. Students will have taken training in the form of the research and data analysis process, both in the module's lectures and seminars, the latter affording opportunities for student to raise questions.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 5 |
| Coursework | Coursework | 50 |
| Coursework | Coursework | 45 |
| 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: 23/04/2025
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