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Course Description:

Why Accounting Analytics Matter: Data analytics is the discovery of patterns/knowledge from data.

However, accounting students are not here just to learn about data analytics, they are here to learn data analytics in order to make better accounting and business decisions. Hence, the intent of this course is to provide an intuitive and practical introduction to data analytics tools/concepts using problems/applications in financial and managerial accounting, auditing, taxation, and accounting information systems. The primary tool used will be R, Excel and Tableau. Applications of data analytics in accounting include topics such as:

  • Financial Accounting: Compare competing strategies (product differentiation and cost leadership) through ROA decomposition, establish a company’s relative position (competitive advantage, parity, disadvantage) versus its peers.
  • Managerial Accounting: Understand how we translate data into the information needed to monitor the performance of a business. For example, work with a retail business to analyze their sales and develop an interactive business dashboard.
  • Auditing: Audit client records to identify fraud and assess inventory valuation.
  • Taxation: Analyze client data for compliance with IRS rules.
  • Accounting Information Systems: Evaluate payoffs from technology investments.

Understand emerging technologies (e.g., cloud computing, blockchain) and predict rate of adoption.

Course Learning Outcomes

Programs delivered by the School of Accounting and Finance (SAF) are designed to provide students with the competencies, professionalism and practical experience that they need to excel in their chosen careers. By the end of the course, the students should be able to achieve the following


  1. Business Understanding: Identify business applications where we can use data analytics concepts and tools to answer questions and solve problems.
  1. Data Understanding and Preparation: Identify sources of data, collect and extract data, get familiar with data structure, identify quality issues, clean and transform data for analysis.3. Modeling: Explain data mining classification and/or prediction models in plain English, using simple examples and tools.
  1. Evaluation: Leverage mathematical (i.e., test statistics) and logical techniques to evaluate how valuable a model is, what it has found, and what you may want to do with the results.
  1. Deployment: Communicate your results and use the new insight to answer questions and solve problems.

This course will pursue these objectives by discussing the basic theory of data analytics and implement data analytics using R in a business context, using real-world data sets (in the measure possible) and with a view of developing professional skills.

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