Aspiring Data Analyst | Data Cleaning • Google Sheets • SQL
In this project, I extended my previous SQL analysis by introducing a second dataset to explore relationships between tables. Using SQL joins, I analyzed how customer segmentation impacts revenue and identified missing or unmatched data across datasets.
Created and structured a customers table to categorize users into segments (VIP, Regular, New)
Combined datasets using INNER JOIN, LEFT JOIN, and RIGHT JOIN to analyze relationships
Simulated a FULL OUTER JOIN using UNION to capture all records from both tables
Identified missing relationships by analyzing NULL values in join results
Performed aggregation to evaluate revenue contribution by customer segment
The Regular segment generates the highest revenue, highlighting the impact of valuable customers
INNER JOIN provides complete matched data for accurate analysis
LEFT and RIGHT JOIN reveal missing or unmatched records, which can impact data quality
Simulating FULL OUTER JOIN allows a complete view of both datasets
SQL Joins (INNER, LEFT, RIGHT)
Data Integration & Multi-table Analysis
Handling Missing Data (NULL analysis)
Advanced SQL Techniques (UNION)
Business Insight Generation
Analyzed customer segmentation using SQL joins and UNION to evaluate revenue contribution and identify missing relationships between datasets.