Course Duration:
  3 Credits
Mode of Study:
  Blended Learning
  Upon successful completion of the module, student will receive an e-certificate by London School of Business and Finance.
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Course Provided by: Becker Professional Education

IMA Competency: Data Analytics, Data Visualization, Information Systems

Advanced Preparation Required: None

Course Level: Intermediate

Course Prerequisites: Foundations of data and analytics for the accounting profession

Instructor bio

Dr. Ann C. Dzuranin is the KPMG Endowed Professor of Accountancy at Northern Illinois University. She earned her B.S. from Fairleigh Dickinson University, her MBA from New York University and her Ph.D.from the University of South Florida. Ann is a CPA (NJ) with 15 years of experience in both public and corporate accounting.
Ann conducts behavioral research in management accounting decision-making and the ways in which accounting information systems interact with those decisions. Her publications include Issues in Accounting Education, Journal of Information Systems, Journal of Business Ethics, Management Accounting Quarterly, Journal of Corporate Accounting and Finance and the Journal of Accounting Education.
Ann also received the 2018 American Accounting Association’s Innovation in Accounting Education Award for her work in data analytics curriculum development. Ann’s work in data analytics has resulted in invitations to present on Data Analytics and Accounting curriculum at both academic and professional conferences. Her presentations have reached over 1,800 people and her materials have been shared with more than 50 universities.

This course will be an overview of:

  • How to perform descriptive, predictive, and prescriptive analyses using tools available in Microsoft Excel
  • How to interpret the results of descriptive, predictive and prescriptive analyses
  • How to identify potential outliers and their potential effect on the analysis results
  • How to test and interpret model assumptions.
  • How to perform and interpret correlation
  • How to interpret correlation

This course is ideal for anyone who is interested to learn data analysis tools using Microsoft Excel.

After completing this course, the learner should be able to:

  • Identify common data analysis tools in Microsoft Excel
  • Prepare and interpret descriptive analytics
  • Prepare and interpret diagnostic analytics
  • Prepare and interpret predictive analytics
  • Prepare and interpret prescriptive analytics
  • Prepare and interpret correlation
  • Identify issues related to outliers and model assumptions


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  • Dr. Preethi Kesavan

    Dr. Preethi Kesavan


  • Ann Dzuranin

    Ann Dzuranin

    Faculty for Becker Programme: Performing and interpreting data analytics

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