Overview:
- Credits:
- 7.5
- Level:
- 4
- Semester:
- Summer
- Subject:
- Finance
- School:
- Business
- Coordinator:
- Dr Kevin (Yong Kyu) Gam
FIN41910 Green Data Science
Academic Year 2023/2024
This module provides students with a comprehensive understanding of data science concepts and applications to the field of sustainable finance and business. The module introduces students to the data value chains underpinning sustainable finance and business decision making, to the statistical techniques for modelling the impacts and progress towards the Sustainable Development Goals (SDGs) and to data driven approaches to identifying green- washing in environmental reporting. The module will also critically review whether and how new financial technologies (such as AI or blockchain) may be used to solving sustainable development issues. The module features seminars from data science professionals working on sustainable development issues, as well as a practical data science group project.Show/hide contentOpenClose All
Curricular information is subject to change
Learning Outcomes:
On successful completion of this module students should be able to:
1. Explain how different statistical techniques can be applied to the modelling and monitoring of SDGs.
2. Demonstrate a comprehensive understanding of the practical implementation of green
data science projects.
3. Critically evaluate data completeness and coverage on SDGs.
4. Implement data processes and robustness checks for Anti-Green-Washing.
5. Critically assess whether and how new financial technologies may be applied to the field of sustainable development.
| Student Effort Type | Hours |
|---|---|
| Autonomous Student Learning | 136 |
| Lectures | 24 |
| Total | 160 |
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Group Project: Students will work on a small group research project and presentation on assessing the accuracy and completeness of GHG emission claims of companies. | Week 4 | n/a | Graded | No | 20 |
| Assignment: Online python introductory course | Throughout the Trimester | n/a | Pass/Fail Grade Scale | No | 10 |
| Group Project: Group data project | Week 11 | n/a | Graded | No | 40 |
| Essay: 3,000 word essay on a current topic in green data science | Week 7 | n/a | Graded | No | 30 |
Not yet recorded |
Not yet recorded
| Name | Role |
|---|---|
| Dr Theodor Cojoianu | Lecturer / Co-Lecturer |
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