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FIN41910

Academic Year 2024/2025

Green Data Science (FIN41910)

Subject:
Finance
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
8
Module Coordinator:
Mr Ankitkumar Kariya
Trimester:
Summer
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The course will try to think about questions in climate economics and finance that one often has to answer with data. It will cover aspects such as the importance of these questions within the wider debate in climate economics and finance, empirical methods (i.e., data science tools) used to answer these questions, and implementation of these empirical methods and their limitations. We will achieve this by a mix of in-class coding exercises and discussions.

About this Module

Learning Outcomes:

On successful completion of this module students should be able to:
1. Understand the importance of certain questions in climate economics and finance that need to be answered with data.
2. Learn the practical implementation of data science methods used to answer questions in climate economics and finance
3. Understand the theory behind the data science methods used in climate economics and finance research
4. Critically evaluate the limitations of the data science methods used in climate economics and finance research

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

136
Lectures

24
Total

160

Approaches to Teaching and Learning:
In this module, we will have a mix of in-class coding exercises and discussions.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Group portfolio optimization with green constraints project. More details will be shared on Brightspace. Week 14, Week 15 Graded No

50

 No
Individual Project: More details will be shared on Brightspace. Week 14, Week 15 Graded No

50

 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Self-assessment activities

How will my Feedback be Delivered?

We will have in-class (non-graded) coding exercises with pre-specified output. That will help students self-monitor and learn through trial and error.

Name Role
Dr Theodor Cojoianu Lecturer / Co-Lecturer
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