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Module Specifications

Archived Version 2022 - 2023

Module Title
Module Code

Online Module Resources

NFQ level 9 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

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

Workload Full-time hours per semester
Type Hours Description
Lecture12Formal classes (lecture / workshop), one hour per week delivered live online, with a recorded option available.
Lecture12Formal classes (lecture), one hour per week delivered in pre-recorded online learning
Assignment Completion76Two individual assignments and a group presentation.
Independent Study87.5Class related reading and activities, guided reading and further learning.
Total Workload: 187.5

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.

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Indicative Reading List

  • 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, 1491957662
Other Resources

55556, 0, DataCamp training (various courses), www.datacamp.com,
Programme or List of Programmes