Python for IT Auditors: Controls, Fraud & GRC Analytics

This course contains the use of Artificial Intelligence.

Most IT-audit courses stop at theory. This one puts a Python interpreter in your hands and has you write the actual tests — the same continuous-controls, fraud-detection, and GRC analytics that modern audit teams run over full populations instead of tiny samples.

You start from zero Python. Within a few lectures you are reading CSV extracts, then moving into pandas and NumPy to build real Computer-Assisted Audit Techniques (CAATs). Every technique is grounded in NovaBridge Logistics, a realistic model company whose ERP, HR, and IAM systems generate the datasets you test throughout the course — leaver accounts still active, duplicate payments, split purchases under approval thresholds, weekend journal entries, Benford's-Law anomalies, vendor-employee collusion, GL-to-subledger breaks, expired compliance evidence, and more.

You will learn to detect duplicate invoices and payments, find purchases split below approval limits, spot after-hours journal entries, reconcile terminated employees against active accounts, model segregation-of-duties conflicts, reconcile ledgers, flag missing or expired evidence, calculate exception rates and audit priorities, perform stratified and monetary-unit sampling, and rank remediation by risk. The course finishes with a Continuous Controls Monitoring capstone where you assemble your tests into a single reusable audit suite.

By the end you will have a portfolio of runnable Python audit tests you can adapt to real engagements — and the confidence to bring data analytics into your audit work.