Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Messy marketing data leads to inaccurate reporting, wasted budget, and poor business decisions. In this course, you’ll learn how to clean and validate marketing datasets so you can trust the numbers behind your campaigns. You’ll begin with the fundamentals of marketing data cleaning, including how to normalize UTM parameters, standardize inconsistent channel names, remove duplicate records, and fix whitespace and formatting issues that distort reporting. Using spreadsheet tools and basic SQL concepts, you’ll apply practical cleaning routines to realistic multi channel marketing datasets. Next, you’ll learn how to validate and reconcile conversion counts across platforms like GA4, Facebook Ads, and Salesforce CRM. You’ll explore why conversion numbers differ between systems and build validation workflows that identify discrepancies, calculate variance percentages, and establish a reliable source of truth for reporting. Through hands on labs and realistic scenarios from a fictional e-commerce brand, you’ll clean campaign datasets, build validation scripts, and investigate conversion discrepancies caused by attribution windows, tracking behavior, and duplicate events. By the end of the course, you’ll be able to prepare cleaner datasets for analysis, identify common marketing data quality issues, and validate reporting accuracy across platforms with confidence. This course is ideal for junior marketing analysts, digital marketers, and business professionals responsible for campaign reporting or marketing analytics.

Syllabus

Data Cleaning: Normalize Marketing Datasets

This module focuses on the cleaning routines required to make marketing datasets reliable for analysis. Learners examine how inconsistent UTM tagging, fragmented channel labels, inconsistent case, whitespace, and naming conventions distort attribution and reporting. The module covers string normalization, duplicate detection, normalization and deduplication, and industry-standard conventions for utm_source, utm_medium, and utm_campaign fields. Learners also explore pipeline duplicates, tracking misfires, and manual-entry duplication. An AI-first workflow demonstrates how analysts can use AI tools to generate cleaning scripts while maintaining responsibility for validation and quality control. In the guided lab, learners apply TRIM and LOWER functions, create cleaned columns, remove duplicate records, and validate outputs against a reference file.

Data Cleaning: Reconcile Conversion Counts

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.