This module teaches learners how to validate and reconcile conversion data across analytics platforms, ad platforms, and systems of record. Learners examine why discrepancies occur between GA4, CRM, order -management systems, and ad platforms, including attribution windows, cookie -consent limitations, client-side pixels, server-side tracking, and modeled conversions. The module emphasizes establishing a source of truth based on reporting objectives and business context. Learners use validation scripts to compare records, flag variance thresholds, standardize dates, calculate variance percentages, identify outliers, and document discrepancies. AI-assisted workflows support script generation while reinforcing review of join logic, variance calculations, and validation steps. In the hands-on lab, learners build comparison tables, calculate variances, flag inconsistencies, and recommend a source of truth.

Learn how to bridge the gap between "clicks" and "customers." This module teaches you how to write SQL joins that link website session data to CRM revenue records.

Big data requires smart queries. You will learn to refine complex SQL to run faster and ensure your aggregation logic correctly counts marketing events without double-counting.

Learn the math behind data health. You will use profiling techniques to quantify how much of your marketing data is missing or duplicated.

Move from finding problems to solving them. Learn how to interpret profiling reports to decide which data issues need immediate fixing and which can wait.

Accelerate your data cleaning and querying workflow using Generative AI. You will learn how to use LLMs to generate complex SQL joins, debug cleaning scripts, and automate the normalization of messy marketing data.

Put your data infrastructure skills to the test. In this project, you will perform a full data audit and cleaning protocol on a multi-channel campaign dataset, using SQL and profiling techniques to transform raw exports into a high-quality analysis-ready dataset.