Auditing with Data Analytics (AUDT8100)

Auditing with Data Analytics

Auditing with Data Analytics


Transform your audits and analytical skills to high gear. This course concentrates on the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling, and visualization while planning or performing audits. The course is also applicable to financial managers and program evaluators looking for patterns and correlation, cause and effect relationships, impact analysis and possible fraud assessment.

Learn the use of descriptive, predictive and prescriptive audit data analytics techniques within the auditing process for performing:

  • Risk Assessment and Planning
  • Auditing Financial Assertions
  • Assessing Internal Controls and Operational Effectiveness
  • Continuous/Concurrent Auditing and Monitoring
  • Fraud Detection

For the virtual version, students must have Excel-with the Data Analyst ToolPak activated (this is included with Excel software)

Who Should Attend?

Auditors, financial managers and program evaluators with three years of experience and seasoned professionals with limited exposure to the subject matter. Analysis Techniques for Auditors (AUDT7900A) is a recommended prerequisite for this course.




24.0 CPE's

Class Type:

This course is currently being offered in the following training modalities:

Learning Outcomes:

  • Explain the importance of data analytics in auditing
  • Identify patterns and outliers for audit
  • Describe the difference between structured and unstructured data
  • List difference of common data analysis tools
  • Establish "reasonable assurance" that audit data can be used to service the audit objective
  • Describe and comply with federal law and agency regulations
  • Satisfy Government Accountability Office (GAO) standards for data validity, reliability, and integrity
  • Discuss multiple tools, including descriptive statistics, Benford's Law, correlation, etc.

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Course Outline

Module 1:

  • Consideration of Data Validity, Reliability, and Integrity under Generally Accepted Government Auditing Standards

Module 2:

  • Data Integrity

Module 3:

  • Descriptive Statistics

Module 4:

  • Graphs

Module 5:

  • Outliers and Their Description

Module 6:

  • Other Anomalies

Module 7:

  • Correlation

Module 8:

  • Hypothesis Testing

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