Data Analytics Crash Course

Describe and summarize data with descriptive analytics by aggregating and comparing data from multiple sources to understand what happened, not predict outcomes, and support continuous monitoring, data discovery, and consolidation.

Diagnose past performance by interpreting data to uncover trend analysis, root causes, correlations, and suspicious patterns; drill down with data mining to reveal fraud incidents.

Explore predictive data analytics to forecast future outcomes by extrapolating trends, adjusting for known changes, and identifying correlations with statistical models and machine learning.

Prescriptive data analytics builds on predictive analytics to recommend decision options and show the impact of each option, helping to mitigate risks and seize future opportunities.

Determine the objectives of the data analytics program and what value the data will provide before collecting data. Cleanse and normalize data, then analyze it to deliver useful insights.

Explore how data analytics supports internal audit across compliance, error and fraud detection, and operational performance, including export restriction checks, text analysis, and network and critical path insights.

Identify how operational performance use of data analytics helps internal auditors improve key performance indicators for functional units, differentiating it from compliance, fraud detection, and internal control analysis.

Identify specialty applications in data analytics, with text analysis as the sole true specialty; prescriptive analysis is a main type, while anomaly detection and internal control analysis are non-specialty.

Compare statistical and non-statistical sampling, explaining how samples infer population properties, with risk-based selection, sample size considerations, and examples like gifts and holidays to uncover fraud.

Examine attribute sampling for estimating characteristics in a population, and apply ratio estimation with representative samples. Understand stratified random sampling and cluster sampling, plus real-world pitfalls.

Explore sampling methods and stratified sampling using audit software to evaluate loan collateralization and aging, ensuring loans are accurately categorized as current or non-current.

Explore the normal distribution and bell curve, and define the mean, median, and mode. Assess variance and standard deviation to reveal trends, outliers, and black swan possibilities in data.

Learn statistical process control with upper and lower control limits and natural variation in production data, and apply trend analysis to identify unreasonable budgeted versus actual costs.

Evaluate whether 5% minority representation and no hires in the last year suffice to conclude a policy violation, noting insufficient information and lack of context about workforce roles.

Explore analytical tests for internal auditing, including Benford's law, regression analysis, data mining, and continuous monitoring, plus proportional and trend analyses to spot anomalies.

Explore continuous auditing with automated tests and exception reports, and use embedded audit modules to tally transactions against thresholds via a data repository for real-time insights.

Identify analytical tests in continuous auditing and embedded audit modules by examining how comparing the balance on the schedule with prior-year balances reveals a simple trend analysis.