# Auditing with Data Analytics Course (Self-Paced)

Canonical URL: <https://www.graduateschool.edu/courses/auditing-with-data-analytics-course-self-paced>

## Overview

This course is designed for federal employees who want to strengthen their ability to use data analytics in audit work. You will learn how to assess whether data are valid, reliable, and fit for the audit objective, while working within applicable federal requirements and GAO standards, including the Yellow Book and Gray Book. The course covers core audit data skills such as evaluating data integrity, cleaning and organizing data in Excel, using descriptive statistics and graphs, identifying outliers and other anomalies, and interpreting analytical results in a way that supports sound audit judgment. It is grounded in the practical realities of government auditing, with a strong emphasis on documenting reasonable assurance and making defensible reliability determinations.

Throughout the course, students apply these concepts through hands-on analysis and case-based exercises that reflect common audit scenarios. Topics include duplicate transactions, vendor and purchase card analysis, date comparisons, Benford’s Law, correlation, sampling, and the use of geographic or GPS-related data to identify unusual patterns or relationships. The course also introduces commonly used tools and approaches for audit analytics, with particular emphasis on Excel-based techniques for data testing, filtering, grouping, visualization, and risk-focused analysis. By the end of the course, federal employees will be better prepared to turn raw audit data into meaningful evidence, identify areas of concern more efficiently, and support oversight responsibilities with stronger, more informed analysis.

## What you'll learn

- Establish and document the critical “reasonable assurance” that audit data are what they say they are and can be used to service the audit objective.
- Know and comply with applicable federal law and agency rules and regulations.
- Satisfy Government Accountability Office (GAO) standards for data validity, reliability, and integrity, particularly those in the Yellow Book and the Gray Book.
- Address common data issues and anomalies in an appropriate way.
- Summarize data to create useful information and easily describe data to others.
- Demonstrate how to do a data integrity risk and reliability assessment.
- Design a Data Integrity Audit Program.
- Discuss and interpret Benford Tools.
- Use GPS data to find abnormal geographic relationships.

## Prerequisites

Students must complete Analysis Techniques for Auditors (AUDT7900) before enrolling in this course, or have equivalent knowledge.

## Curriculum

#### Module 1: Consideration of Data Validity, Reliability, and Integrity Under Generally Accepted Government Auditing Standards (GAGAS)

- Describe the importance of data validity, reliability, and integrity when conducting audits and attestation engagements under GAGAS.
- Identify the four types of evidence discussed in the Yellow Book and their order of general credibility.
- Examine the requirements for evidence to be sufficient, competent, relevant, valid, and reliable under GAGAS.
- Define validity and reliability as used by GAGAS.
- Discuss the importance of data integrity in auditing.
- Analyze standards for data assessment and the framework for data reliability assessments.
- Describe the process of assessing data reliability and the possible determinations that can be made.

#### Module 2: Basic Data Integrity Procedures

- Describe the three primary types of data integrity: entity integrity, referential integrity, and domain integrity.
- Perform basic data integrity procedures in Excel, including inserting a counter, qualifying data import, and activating data analysis tools.
- Use Excel functions to clean, trim, and format data.
- Perform data grouping and filtering using Excel tools such as Pivot Tables.
- Examine the limitations and features of Excel, including formulas, operators, and references.

#### Module 3: Descriptive Statistics

- Analyze the concept of descriptive statistics and its importance in auditing.
- Differentiate between population parameters and sample statistics.
- Describe the difference between attributes and variables and their respective values.
- Construct frequency distributions and graphs.
- Calculate measures of central tendency, dispersion, skew, and kurtosis.
- Use Excel’s Data Analysis ToolPak.
- Apply descriptive statistics in auditing and data analysis.

#### Module 4: Graphs

- Analyze the importance of graphs in descriptive statistics and their use in visually summarizing relationships between variables.
- Identify different types of graphs and when to use them.
- Examine the advantages and disadvantages of different graph types.
- Recognize how graphs can be manipulated to create false impressions and how to avoid such deceptions.
- Create and interpret graphs accurately and effectively.

#### Module 5: Outliers and Their Disposition

- Analyze the impact of outliers on data analysis.
- Identify outliers using statistical methods such as Z-score, median, and fences methodology.
- Describe options for dealing with outliers and their impact on data analysis.
- Discuss the use of outliers in fraud detection and the determination of materiality thresholds.
- Apply these concepts to real-world scenarios using Excel-based exercises.

#### Module 6: Other Anomalies

- Describe Benford’s Law and its application in data analysis.
- Identify the conditions required for Benford’s Law to hold true.
- Analyze the objectives of Benford Analysis and how it can identify abnormal recurrences and digit patterns.
- Outline the limitations of Benford Analysis and the importance of using it alongside other data mining techniques.
- Examine geographic outliers and how to identify potentially anomalous or inappropriate spatial relationships.
- Apply Benford Analysis to a data set using the provided Excel tool.

#### Module 7: Correlation

- Examine the general principles of correlation and its measures.
- Differentiate between Pearson and Spearman Rank Correlation Coefficients.
- Interpret the strength and direction of the correlation coefficient.
- Describe the limitations of correlation and its inability to determine causation.
- Use correlation as an audit scoping tool.
- Discuss the impact of sample size on correlation.
- Construct a correlation matrix using Excel.
- Analyze the importance of selecting appropriate data for correlation analysis.
- Apply the principle that correlation does not equal causation.

## Pricing

**Tuition:** $1199