Data Preparation & Infrastructure

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Data Preparation & Infrastructure

Instructors: John Whitworth

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8 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Clean and normalize campaign and CRM marketing datasets.
Write SQL queries to extract and join marketing data.
Validate data quality across analytics and ad platforms.
Profile datasets to identify reporting inconsistencies and gaps.

Details to know

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Add to your LinkedIn profile

Assessments

18 assignments¹

AI Graded see disclaimer

Taught in English

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There are 8 modules in this course

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.

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 videos2 readings2 assignments

3 videosTotal 16 minutes

The Cost of Dirty Data2 minutes
Marketing Data Cleaning Fundamentals6 minutes
Step-by-Step Cleaning in Action8 minutes

2 readingsTotal 11 minutes

UTM Normalization Standards6 minutes
Exemplar for Hands-On Activity: Standardize UTM Entries5 minutes

2 assignmentsTotal 25 minutes

Hands-On Activity: Standardize UTM Entries20 minutes
Knowledge Check: Cleaning Routines5 minutes

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

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.

What's included

2 videos2 readings2 assignments

2 videosTotal 12 minutes

Joining Marketing Tables5 minutes
Building the Master Campaign Query7 minutes

2 readingsTotal 13 minutes

SQL: The Marketing Analyst’s Superpower8 minutes
Exemplar for Hands-on Activity: Generate Campaign Performance Metrics5 minutes

2 assignmentsTotal 25 minutes

Hands-on Activity: Generate Campaign Performance Metrics20 minutes
Knowledge Check: Marketing SQL Joins5 minutes

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.

What's included

1 video2 readings3 assignments

1 videoTotal 8 minutes

Optimizing for Speed and Accuracy8 minutes

2 readingsTotal 10 minutes

Common Marketing Aggregation Pitfalls5 minutes
Exemplar for Hands-on Activity: Optimize a Performance Query5 minutes

3 assignmentsTotal 35 minutes

Quiz: Querying Marketing Data10 minutes
Hands-on Activity: Optimize a Performance Query20 minutes
Knowledge Check: Query Optimization5 minutes

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

What's included

3 videos2 readings2 assignments

3 videosTotal 15 minutes

Trust, But Verify3 minutes
Profiling with SQL5 minutes
Profiling with Excel7 minutes

2 readingsTotal 10 minutes

The Three Pillars of Marketing Data Quality5 minutes
Exemplar for Hands-On Activity: Assess Campaign Data Health5 minutes

2 assignmentsTotal 25 minutes

Hands-On Activity: Assess Campaign Data Health20 minutes
Knowledge Check: Profiling Metrics5 minutes

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.

What's included

2 videos2 readings3 assignments

2 videosTotal 11 minutes

Establishing Data Baselines5 minutes
Writing a Remediation Plan from a Profiling Report6 minutes

2 readingsTotal 11 minutes

Interpreting Profiling Outputs to Prioritize Remediation6 minutes
Exemplar for Hands-On Activity: Prepare a Data Remediation Plan5 minutes

3 assignmentsTotal 35 minutes

Quiz: Data Profiling10 minutes
Hands-On Activity: Prepare a Data Remediation Plan20 minutes
Knowledge Check: Remediation Strategy5 minutes

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.

What's included

2 videos1 reading2 assignments

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.

What's included

3 readings1 assignment

Instructors

John Whitworth

30 Courses3,567 learners

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245 Courses17,473 learners

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