Coursera

Apply Data Cleaning Basics

Coursera

Apply Data Cleaning Basics

Access provided by Alliance University

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Normalize UTM fields and standardize marketing channel naming conventions

  • Remove duplicate records and clean inconsistent campaign datasets

  • Reconcile conversion counts across GA4, ad platforms, and CRM systems

  • Build validation workflows that identify discrepancies and reporting gaps

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments¹

AI Graded see disclaimer
Taught in English
Recently updated!

June 2026

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Build your subject-matter expertise

This course is part of the Marketing Data & Reporting Essentials Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 2 modules in this course

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.

What's included

3 videos3 readings2 assignments

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.

What's included

1 video2 readings3 assignments

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Instructor

ansrsource instructors
277 Courses18,009 learners

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.