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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Data Analytics for Accounting and Business
Module Code AC584
School DCUBS
Module Co-ordinatorSemester 1: Damien Dupré
Semester 2: James Byrne
Autumn: James Byrne
Module TeachersJames Byrne
Damien Dupré
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

The rise of big data and data analytics is having a significant impact on business decision making and the accounting function. The main objective of this module is to introduce the student to the fundamental tools and techniques of using data analytics to support business decisions. The student will develop practical skills in preparing, visualising and analysing structured and unstructured data. Students will be exposed to the principles of data science and analytics and develop an ability to make better evidence-based decisions through leveraging insights gained from data analysis. Case studies, guest lectures, and collaborations with DCU's IC4 and Institute of Ethics on how data analytics is used and impacts across various areas of business and accounting will complement core lectures.

Learning Outcomes

1. Explain the key concepts of big data and data analytics, the business case for data analytics, how it interacts with the accounting function, and the ethical issues of using big data in business.
2. Evaluate the different sources of data for a business and the various preparation, visualisation and analytical techniques that can be used on those data sources.
3. Critically evaluate different data analytics methodologies and approaches, and demonstrate skills for integrative reasoning, problem-solving and critical thinking applied to practical accounting and business related scenarios.
4. Describe the types of analytical tools and statistical modelling techniques available to analyse business data and assess their suitability for different types of business problems.
5. Evaluate, select and apply appropriate tools, techniques and frameworks to analyse a complex business-related issue.

Workload Full-time hours per semester
Type Hours Description
Lecture24Weekly Lectures
Directed learning40Assessment Preparation
Independent Study46Reading before and after lectures
Online activity15Online tutorials applying data analytics tools and techniques
Total Workload: 125

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 Big Data and Data Analytics
What is Big Data and Data Analytics? Growth of Big Data; Business Case for Big Data; Barriers to using Big Data, including Ethical Concerns; Big Data and the Accounting Function.

Data Types, Data Structure and Accounting Information Systems
What are the different types of data types? What is the difference between structured and unstructured data? Which types of data sources are businesses using to make decisions? How can accounting information systems be integrated with data analytics?

Review of Basic Probability and Statistics
Review of probability concepts, probability distributions and statistics. Application to business and accounting contexts.

Data Visualisation
Basic and advanced visualisation techniques. Data Quality/Data Capture, Functions of Visualisations, Tables & Graphs, Multiple Datasets, Interactive Graphs

Data Analytics using Excel
Excel and the Accounting Function; Basic Statistics using Excel, Excel Data Analysis Toolpak, Visualisation in Excel;

Data Analytics Tools
On overview of the variety of Statistical Software, Data Programmes, Databases & languages, Business Intelligence Tools, and Visualisation Tools available to analyse business data.

Advanced Analytics and Statistical Modelling
An introduction to various advanced analytical and statistical techniques such as Linear and Logistic Regression, Clustering Techniques, Decision Trees, Time Series Analysis, and Text Analysis.

Data Analytics in Context
A critical examination of the role and use of data analytics in business information systems, accounting (financial and management), assurance (including audit) and finance; including aspects such as analytical review, continuous audit, performance review, customer relationship management, investment decisions and artificial intelligence and machine learning systems.

Case Studies and Guest Lectures
A variety of case studies and guest lectures focused on how data analytics is applied in practice in the world of business and accounting (e.g. fraud detection, customer profitability analysis, cost driver analysis)

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
In Class TestStudents will be required to complete in-class tests on various aspects of the module.30%n/a
ProjectIndividual Report and Presentation on a Tool or Statistical Technique35%n/a
ProjectEach student is required to submit a project demonstrating their mastery of one or more data analytics tools/techniques to address a particular business issue.35%n/a
Reassessment Requirement Type
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
This module is category 1
Indicative Reading List

  • Provost F. and Fawcett, T.: 2015, Data Science for Business : What You Need to Know About Data Mining and Data-Analytic Thinking, 1st, O'Reilly Media, Inc, USA, 1449361323
  • Powell, S. and Baker, K.: 2016, Business Analytics: The Art of Modeling With Spreadsheets, 1119386497
  • Knaflic, C.: 2015, Storytelling with Data: A Data Visualization Guide for Business Professionals, 1st, Wiley, 1119002257
  • Davenport, T., and Harris, J.: 2017, Competing on Analytics: The New Science of Winning, 1422103323
  • Romney, M. and Steinbart, P.: 2017, Accounting Information Systems, 14th, Pearson, 0134474023
Other Resources

Programme or List of Programmes
MBSMSc in Accounting

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