Welcome to my SQL Challenge Portfolio! This repository contains 25 carefully structured SQL exercises designed to simulate real-world business problems across various departments such as Sales, Marketing, HR, Finance, and Product.
Each challenge focuses on not only writing correct SQL queries, but also understanding the business context, interpreting results, and communicating insights effectively.
To keep things clean, every challenge is documented across three dedicated folders:
This folder contains the raw SQL problem-solving files. Each file includes:
- A clearly defined business scenario
- Why the problem matters
- SQL table creation and sample data insertion
- The final SQL query used to solve the problem
Here you'll find screenshots of the SQL queries being executed along with the actual output. These act as visual proof of query correctness and database behavior.
Each .md file here includes:
- A business explanation of the problem
- A breakdown of the SQL logic used
- Reasoning for how the query solves the problem
- A summary of what the output tells us
- Why the insight matters for the business
These files serve as analytical reflections to show how I translate raw data into actionable insights — a skill critical in business intelligence, analytics, and decision-making.
Each challenge showcases:
- 📚 Core SQL concepts (e.g.,
JOIN,GROUP BY,CASE,RANK,WINDOW FUNCTIONS,CTEs) - 🧠 Business-driven reasoning
- 🛠️ Practical SQL usage for reporting, segmentation, cohort analysis, and more
- 📈 Communication of results through analysis and screenshots
The purpose of this project is to:
- Sharpen my SQL problem-solving and analysis skills
- Simulate real-world analytics requests from cross-functional teams
- Build a solid foundation before progressing into larger projects using SQL + Tableau
- Create a transparent and organized showcase of my learning journey
- Identify top-spending customers for loyalty campaigns
- Calculate monthly active users (MAU)
- Detect duplicate customer emails
- Perform cohort retention analysis
- Classify orders into tiers (High vs. Normal)
- Segment customers by lifetime value
- Evaluate regional revenue for marketing budgets
- Track best-selling products
- Benchmark compensation fairness using RANK functions
After this challenge series, I will begin applying my SQL knowledge to:
- Larger datasets
- Visual analytics using Tableau
- Full case studies and dashboards for business stakeholders
Stay tuned for more updates in future branches!
Feel free to connect or reach out for collaboration:
💡 “Data is not just numbers — it's a narrative waiting to be uncovered.”
— This repo is my proof of that.