Module: Data Science for Finance

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Data Science for Finance
Module Code	FBA1025 (ITS: EF5176)
Faculty	DCU Business School	School	DCU Business School
NFQ level	9	Credit Rating	7.5

Description

The rapid rise of new ways of thinking about finance, as well as technological progress, has created a necessity for finance professionals to be able to work with new forms of data. This move echoes the wider rise of data science as an approach to business. This module will cover the nature and approaches of data science and, in specific, will approach finance through an immersion in Python coding. Python is the most popular language of data science and this module will bring you from the very basics of coding in Python through to advanced techniques for understanding financial data.

Learning Outcomes

1. Understand the potential for modern data science approaches to improve the practice of finance
2. Develop Python language techniques that enable the effective analysis of financial data
3. Analyse financial phenomena through project-based approaches to financial data intelligence
4. Demonstrate an understanding of how to analyse, critically evaluate, and communicate, financial data science findings

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	12	Formal classes (lecture / workshop), one hour per week delivered live online, with a recorded option available.
Lecture	12	Formal classes (lecture), one hour per week delivered in pre-recorded online learning
Assignment Completion	76	Two individual assignments and a group presentation.
Independent Study	87.5	Class related reading and activities, guided reading and further learning.
Total Workload: 187.5

Section Breakdown
CRN	20524	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Tianqi Luo	Module Teacher	Michael Dowling

Section Breakdown
CRN	21217	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Tianqi Luo	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Group presentation	Group work: Students will demonstrate a clear business-oriented solution to a finance problem through the application of financial data science techniques.	30%	Week 9
Report(s)	Individual assessment: The student will demonstrate applied knowledge of the full module material through the exploration of a financial problem using Python.	70%	Sem 1 End

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to data science for finance
Introduction to the basic concepts of data science and the tools and techniques of the methods. Immersive learning by doing, alongside practical application exercise and case study-based learning. Discussion of the development of financial data science as a tool of fintech as well as the general new development of finance.

Financial intelligence through data science
Problem-based approach to developing and strengthening knowledge of financial data science. Through the use of Python. Python support is provided through DataCamp.com which allows development of baseline learning and strengthening lecture learning. Sample topics include: asset valuation, risk assessment, stock price prediction, outlier and fraud detection. Use of standard statistical tools through Python: Pandas, Statsmodels, as well as some introduction to the basics of machine learning for finance.

Financial data science project
Development project on a major topic. Guidance through feedback, group structures, and expert advice. Outcome will be a project capable of showcasing data science learning.

Indicative Reading List

Books:

Brooks, Chris: 2019, Python Guide for Introductory Econometrics for Finance, Cambridge University Press,
Brooks, Chris: 2019, Introductory Econometrics for Finance, Cambridge University Press, 978-110842253
Wes McKinney: 2017, Python for Data Analysis, O'Reilly Media, 550, 1491957662

Articles:
None

Other Resources

0: DataCamp training (various courses), www.datacamp.com,