Overview

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Build the technical foundation required for modern marketing analytics workflows. In this course, you’ll learn how to prepare, clean, validate, and structure marketing data so it can be trusted for reporting and decision-making. You’ll work with campaign exports, CRM records, web analytics data, and marketing performance datasets while learning how to identify inconsistencies, normalize UTM conventions, and remove duplicate or incomplete records. You’ll also learn how to use SQL to extract and combine marketing-relevant data from multiple sources, enabling you to create campaign-level metrics and performance summaries. In addition, you’ll evaluate data quality using profiling techniques that help identify gaps, anomalies, and reporting risks before they affect business decisions. By the end of the course, you’ll be able to create repeatable data-cleaning workflows, validate marketing datasets across platforms, and prepare reliable datasets for dashboarding, analysis, and optimization projects.

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.

SQL: Extract and Join Marketing Data

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.

SQL: Analyze and Optimize Queries

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.

Profiling: Compute Quality Metrics

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

Profiling: Evaluate and Remediate

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.

GenAI Module: AI-Assisted Data Preparation

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.

Project Module: Data Audit & Cleaning

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.