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FIN3009S

Academic Year 2024/2025

Data Analytics for Finance (FIN3009S)

Subject:
Finance
College:
Business
School:
Business
Level:
3 (Degree)
Credits:
10
Module Coordinator:
Dr Vassilios Papavassiliou
Trimester:
Autumn and Summer (separate)
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module covers the fundamental mathematical and statistical background necessary for the understanding of financial markets. The aim of this module is to examine why and how Finance professionals use data and quantitative methods. A comprehensive range of quantitative techniques are reviewed, however the main focus is on applications in Finance. The first part of the module will introduce students to the basic techniques of data analysis used by Finance professionals. This will include a discussion of the main databases, a review of the key features of market data, as well as the techniques available for analysing this data. In order to develop a comprehensive understanding of financial analysis we examine a number of key quantitative principles in the second part. Key relationships will be examined including discounting and the time value of money. The module is concluded with an introduction to empirical relationships in Finance using econometric analysis.

About this Module

Learning Outcomes:

Learning Outcomes
1. Demonstrate knowledge and understanding of the key elements of financial analytics.
2. Apply quantitative techniques to practical real world problems in finance and business.
3. Implement a range of analytical metrics in an efficient manner using spreadsheets.
4. Develop research skills including data analysis.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

85
Autonomous Student Learning

102
Lectures

20
Total

207

Approaches to Teaching and Learning:
Students will attend classes for this module and have the opportunity to engage in active learning during these sessions. There will be in-class discussion and group work to analyse module concepts. Where appropriate, the module will incorporate case based learning.

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
Assignment(Including Essay): Continuous Assessment Week 7 Standard conversion grade scale 40% No
40
 No
Exam (Online): Examination Week 15 Standard conversion grade scale 40% No
60
 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
Repeat Within Two Trimesters
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

General feedback is provided to students on all their submitted assessment components.

Name Role
Dr Vassilios Papavassiliou Lecturer / Co-Lecturer
Dr Christina Burke Tutor
Ms Michele Connolly Doran Tutor
Dr Kevin (Yong Kyu) Gam Tutor
Richa Gupta Tutor
Shirley Ho Tutor
Rachel Sim Tutor
Dr Anita Suurlaht Tutor
Chee Shong Tan Tutor
Charlene Tan Puay Koon Tutor
Dr Tiffany Thng Tutor
Tiffany Thng Tutor
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