Latest Module Specifications
Current Academic Year 2025 - 2026
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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 on how data analytics is used and impacts across various areas of business and accounting will complement core lectures. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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 |
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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) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books:
Articles:
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Other Resources None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||