Data Portfolio Builder SQL Data Cleaning for Dashboard KPIs

What you'll learn
Build a complete portfolio project you can publish: an end-to-end SQL data cleaning + KPI pipeline
Turn a messy e-commerce table into a trusted clean_table that's safe for reporting and dashboards
Profile data like a professional: row counts, null/completeness checks, category profiling, and "how bad is it?" diagnostics
Build a typed silver layer in SQL: safe casting, mixed-format date parsing, and text normalisation (without silently corrupting results)
Enforce a real business contract: filter invalid orders (amounts, costs, flags, hour ranges) and quantify exactly what each rule removes
Detect and remove duplicates using a business key, and understand the real-world risk of defining that key incorrectly
Implement 10 dashboard-ready KPIs in SQL using CTEs, aggregates, and window functions where needed
Standardise outputs into a single kpi_results table with one consistent schema a dashboard (or platform) can read
Debug KPI mismatches properly: trace issues back to the right layer (source → silver → clean → KPI) instead of guessing
Package the project professionally: clean SQL files, a strong README, evidence , and a LinkedIn-ready project summary
Requirements
Basic SQL knowledge (you should be comfortable with SELECT, WHERE, GROUP BY, and basic joins)
A laptop/PC and a modern web browser
You can use any SQL tool you already have
In the videos, I use Verulam Blue Mint (a free to use browser-based SQL workbench) to keep everything in one notebook workflow and support KPI checking/feedback - but the SQL approach is transferable
Description
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" meansExplore 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 wayEnforce the business rules and deduplicate into one trusted clean_tableCompute and standardise all KPI outputs into a consistent results tableValidate results, understand tolerances/rounding, and debug mismatches like a professionalFinish by turning the whole pipeline into a portfolio-ready GitHub project, with a clean repo structure, a strong README, and proof of resultsCourse outline (high level):Section 00: Course IntroductionSection 01: The Verulam Blue Mint EnvironmentSection 02: Understanding the Challenge BriefSection 03: Exploring Source Data SchemaSection 04: Data Cleaning I - Sampling & CompletenessSection 05: Data Cleaning II - Silver Layer & NormalisationSection 06: Data Cleaning III - Business Rules & DeduplicationSection 07: Understanding the KPIsSection 08: Computing KPIsSection 09: ResultsSection 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.
Who this course is for
Anyone who wants a portfolio project they can publish: a complete SQL cleaning + KPI pipeline you can put on GitHub and confidently explain in interviews
Data analysts, BI developers, and aspiring analytics/data engineers who already know basic SQL and want a serious, employer-facing project (not toy examples)
Learners who can write queries but haven't yet built a layered workflow end-to-end (raw → silver → clean → KPIs → standardised results)
Job seekers who want proof-of-skill in the areas employers actually care about: data quality reasoning, business-rule enforcement, deduplication, and metric reliability
Not ideal if you're brand new to SQL and need a fundamentals-first course.
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