Module: Data Analytics for Accounting & Business

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Data Analytics for Accounting & Business
Module Code	AC579 (ITS) / ACC1026 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Vincent Tawiah
Module Teachers	Andreas Robotis, Damien Dupré, James Byrne
NFQ level	9	Credit Rating	5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

None

Description

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 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. Apply various 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.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	Weekly Lectures
Directed learning	40	Assessment Preparation
Independent Study	46	Reading before and after lectures
Online activity	15	Online 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 Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
In Class Test	Students will be required to complete in-class tests on various aspects of the module.	30%	n/a
Project	Individual Report and Presentation on a Tool or Statistical Technique	35%	n/a
Project	Each 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: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
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

None