FIN41910 Green Data Science

Academic Year 2019/2020

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.

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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 Hours: 
Student Effort Type Hours


Autonomous Student Learning




Approaches to Teaching and Learning:
Not yet recorded 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.  
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade

Not yet recorded.

Carry forward of passed components
Not yet recorded

Not yet recorded

Please see Student Jargon Buster for more information about remediation types and timing. 
Not yet recorded
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.  
Lecture Offering 1 Week(s) - Spring: All Weeks Mon 14:00 - 15:50