This course is built to give you a publishable portfolio project as the end product — a complete SQL data-cleaning and KPI pipeline you can put on GitHub, link on LinkedIn, and confidently talk through in interviews.

It’s a real-world simulation built around one messy dataset and a business brief with a clear target: deliver ten KPIs that are trustworthy enough to go on a dashboard.

Most SQL “data cleaning” courses either stay at the level of syntax drills, or they use clean toy datasets where nothing breaks. That’s not what you face in real data teams.

In this course you’ll work through the same workflow you’d use on a real project:

• Read the brief properly so you know what “correct” means

• Explore the raw schema and spot the mess early (mixed date formats, typos in categories, missing values, duplicates)

• Build a typed, safer silver layer where errors surface in a controlled way

• Enforce the business rules and deduplicate into one trusted clean_table

• Compute and standardise all KPI outputs into a consistent results table

• Validate results, understand tolerances/rounding, and debug mismatches like a professional

• Finish by turning the whole pipeline into a portfolio-ready GitHub project, with a clean repo structure, a strong README, and proof of results

Read the brief properly so you know what “correct” means

Explore the raw schema and spot the mess early (mixed date formats, typos in categories, missing values, duplicates)

Build a typed, safer silver layer where errors surface in a controlled way

Enforce the business rules and deduplicate into one trusted clean_table

Compute and standardise all KPI outputs into a consistent results table

Validate results, understand tolerances/rounding, and debug mismatches like a professional

Finish by turning the whole pipeline into a portfolio-ready GitHub project, with a clean repo structure, a strong README, and proof of results

Course outline (high level):

• Section 00: Course Introduction

• Section 01: The Verulam Blue Mint Environment

• Section 02: Understanding the Challenge Brief

• Section 03: Exploring Source Data Schema

• Section 04: Data Cleaning I – Sampling & Completeness

• Section 05: Data Cleaning II – Silver Layer & Normalisation

• Section 06: Data Cleaning III – Business Rules & Deduplication

• Section 07: Understanding the KPIs

• Section 08: Computing KPIs

• Section 09: Results

• Section 10: Portfolio project deployment (repo + README + LinkedIn-style project story)

Section 00: Course Introduction

Section 01: The Verulam Blue Mint Environment

Section 02: Understanding the Challenge Brief

Section 03: Exploring Source Data Schema

Section 04: Data Cleaning I – Sampling & Completeness

Section 05: Data Cleaning II – Silver Layer & Normalisation

Section 06: Data Cleaning III – Business Rules & Deduplication

Section 07: Understanding the KPIs

Section 08: Computing KPIs

Section 09: Results

Section 10: Portfolio project deployment (repo + README + LinkedIn-style project story)

By the end, you won’t just know “how to clean data using SQL”. You’ll have an end-to-end portfolio project you can explain clearly: what was wrong with the data, what you changed, what rules you enforced, and why your KPIs can be trusted.