K2's Data Analytics For Accountants And Auditors

You'll spend significant time with hands-on demonstrations using Excel and Power BI—the tools most accountants and auditors already have. Mac walks through real workflows: extracting the first digit from invoice amounts using LEFT() and MID() functions for Benford's law analysis, building regression models to validate account balances, running Monte Carlo simulations to stress-test forecasts, and filtering transaction data in Power Query. You're not just learning concepts; you're learning exactly where to click and what formulas to build.

No. Mac explicitly states he has "fifty or sixty hours of postgraduate statistics" but doesn't consider himself a statistician—and the course is designed the same way. You'll learn to interpret regression R-squared values, distinguish correlation from causation, and read Benford's law distributions, but the focus is on when to use each tool and how to act on what it tells you, not on the mathematics underneath. For example, you'll learn that an R-squared of 0.97 means 97% of payroll expense variance is explained by hours worked and machine hours, and that's powerful enough to validate an account balance without needing to derive the formula yourself.

Mac dedicates substantial time to auditing applications: using Benford's law to direct sampling toward transactions starting with 8 or 9 (where fraud is statistically more likely), continuous auditing with AI-native ledgers like Digits and Kit.co that flag anomalies in real time instead of sampling, analyzing social media and non-traditional data to validate sales projections, and regression analysis to test whether a credit memo balance is reasonable. He even demonstrates a Benford analysis case where Arizona's state treasurer was convicted partly because check amounts violated the expected distribution—showing how these tools have legal precedent in detecting fraud.

You'll be able to build a regression model in Excel's Data Analysis ToolPak in under a minute to validate an account balance, run a Benford analysis on invoice amounts using either formulas, macros, or the ActiveData add-in to identify which transactions to audit first, create a Power BI dashboard with slicers and KPIs that updates automatically instead of sending static reports via email, generate descriptive statistics with one click instead of building ten formulas, and use forecasting sheets (triple exponential smoothing) built into Excel since 2016 that almost nobody uses. You'll also learn to pass routine data entry and categorization tasks to Claude, transforming your role from manual processor to data analyst.

This course connects tools directly to audit and accounting decisions. You're not learning Excel for Excel's sake—you're learning why to use regression (to predict what payroll expense should be and test if actual is reasonable), when to use Power Query (to see the distribution and data quality of transactions before analysis), and how to interpret what each tool outputs in an accounting context. Mac demonstrates with real order and expense data, shows common mistakes (like misidentifying business intelligence as data analytics), and explains limitations (like why Benford's law won't work on assigned numbers like social security numbers). The examples are specifically built for practitioners making audit and financial analysis decisions.