Auditing with Data Analytics Course (Self-Paced)
IDAUDT8100SP

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.