Welcome to my SQL Data Analysis Projects portfolio! This repository showcases my expertise in SQL programming and database analytics through real-world data analysis projects. Each project demonstrates my ability to extract meaningful insights, perform complex data transformations, and solve business problems using SQL queries and database management techniques.
As a Business Analytics graduate student specializing in data-driven decision making, I leverage SQL as a primary tool for data exploration, analysis, and reporting across diverse industries and datasets.
Folder: Credit Card Analysis
Description: Comprehensive analysis of credit card transaction data to identify spending patterns, fraud detection indicators, and customer segmentation insights.
Key SQL Techniques:
- Complex JOINs (INNER, LEFT, RIGHT, FULL OUTER)
- Aggregate functions (SUM, AVG, COUNT, MAX, MIN)
- Window functions for running totals and rankings
- Date/Time manipulation for transaction analysis
- GROUP BY with HAVING clauses for filtered aggregations
Business Insights:
- Transaction pattern analysis by time periods
- High-value customer identification
- Spending category breakdowns
- Anomaly detection for potential fraud
Folder: Exploratory_Data_Analysis
Description: Systematic data exploration using SQL to uncover patterns, detect anomalies, and generate summary statistics across various datasets.
Key SQL Techniques:
- Statistical aggregate functions
- CASE statements for conditional logic
- Data profiling queries (NULL counts, DISTINCT values)
- Correlation analysis through JOINs
- Data quality assessment queries
Analysis Focus:
- Data distribution and frequency analysis
- Missing value identification
- Outlier detection
- Variable relationship exploration
- Summary statistics generation
Folder: Instagram User Engagement
Description: Deep dive into social media engagement metrics to understand user behavior, content performance, and platform growth trends.
Key SQL Techniques:
- Complex subqueries and CTEs (Common Table Expressions)
- RANK() and DENSE_RANK() for top performers
- Date arithmetic for engagement trends
- Self-joins for follower/following analysis
- Percentage calculations and rate metrics
Business Insights:
- Most engaging content types
- Peak engagement time periods
- User retention and churn analysis
- Influencer identification
- Hashtag performance analytics
Folder: Online Retail Data-Analysis
Description: E-commerce transaction analysis focusing on customer purchasing behavior, product performance, and sales optimization opportunities.
Key SQL Techniques:
- Advanced JOINs across multiple tables
- Sales funnel analysis queries
- Product basket analysis
- Time-series analysis for seasonal trends
- Customer lifetime value calculations
Business Insights:
- Revenue trends and seasonality patterns
- Product performance rankings
- Customer purchase frequency analysis
- Cross-selling opportunities
- Regional sales performance
Folder: RFM Analysis
Description: Customer segmentation using the RFM model to identify high-value customers and tailor marketing strategies.
Key SQL Techniques:
- NTILE() for percentile-based segmentation
- Date difference calculations for Recency
- Aggregate functions for Frequency and Monetary values
- CASE statements for scoring and classification
- Multi-level GROUP BY for segment analysis
Business Insights:
- Champion customers identification
- At-risk customer detection
- Customer segment distribution
- Targeted marketing recommendations
- Customer lifetime value estimation
RFM Segments Created:
- Champions (High R, F, M)
- Loyal Customers
- Potential Loyalists
- At Risk
- Can't Lose Them
- Hibernating
Folder: Walmart Sales Analysis
Description: Retail sales analysis examining store performance, product categories, and factors affecting sales volumes.
Key SQL Techniques:
- Complex aggregations across time periods
- Store performance comparisons
- Product category analysis
- Seasonal trend identification
- Holiday impact analysis
Business Insights:
- Top and bottom performing stores
- Best-selling product categories
- Seasonal sales patterns
- Holiday sales spikes
- Store-level recommendations
- Query Fundamentals: SELECT, FROM, WHERE, ORDER BY, LIMIT
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, CROSS JOIN, Self-Joins
- Aggregations: SUM, AVG, COUNT, MAX, MIN, GROUP BY, HAVING
- Subqueries: Correlated and non-correlated subqueries
- CTEs: Common Table Expressions for complex queries
- Window Functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD(), NTILE()
- String Functions: CONCAT, SUBSTRING, UPPER, LOWER, TRIM, REPLACE
- Date Functions: DATEADD, DATEDIFF, DATEPART, DATE_TRUNC
- Conditional Logic: CASE WHEN, IIF, COALESCE, NULLIF
- Data cleaning and transformation
- Multi-table complex JOINs (5+ tables)
- Performance optimization with indexes
- Query optimization and execution planning
- Data validation and quality checks
- Recursive queries
- Pivot and unpivot operations
- Customer Segmentation: RFM analysis, behavioral clustering
- Sales Analytics: Trend analysis, forecasting, performance metrics
- Product Analytics: Basket analysis, category performance
- Web/Social Analytics: Engagement metrics, user behavior
- Financial Analytics: Revenue analysis, profitability metrics
- KPI development and tracking
- Dashboard data preparation
- Automated reporting queries
- Data pipeline development
- Ad-hoc analysis capabilities
SQL-Data-Analysis-Projects/
├── Credit Card Analysis/
│ ├── queries.sql
│ ├── analysis_results/
│ └── insights.md
├── Exploratory_Data_Analysis/
│ ├── eda_queries.sql
│ └── findings.md
├── Instagram User Engagement/
│ ├── engagement_analysis.sql
│ └── metrics.md
├── Online Retail Data-Analysis/
│ ├── retail_queries.sql
│ └── insights.md
├── RFM Analysis/
│ ├── rfm_segmentation.sql
│ └── customer_segments.md
├── Walmart Sales Analysis/
│ ├── sales_queries.sql
│ └── analysis.md
└── README.md
- Mastered complex SQL query construction
- Developed efficient query optimization skills
- Learned database design best practices
- Gained experience with large-scale datasets
- Built reusable query templates
- Identified actionable customer insights
- Enabled data-driven decision making
- Improved reporting efficiency
- Uncovered revenue optimization opportunities
- Enhanced customer targeting strategies
- Database Systems: Microsoft SQL Server (T-SQL), MySQL, PostgreSQL
- Query Tools: SQL Server Management Studio (SSMS), MySQL Workbench
- Data Visualization: Power BI (for SQL query results)
- Version Control: Git, GitHub
- Documentation: Markdown, SQL comments
- Browse project folders to see diverse analytical applications
- Review SQL queries to assess technical proficiency
- Read insights documents to understand business acumen
- Check query complexity to evaluate skill level
- Explore query patterns for learning and inspiration
- Adapt queries for your own datasets
- Study optimization techniques used in complex queries
- Review segmentation approaches for customer analytics
- Feel free to suggest improvements or alternative approaches
- Share feedback on query efficiency
- Propose new analytical angles to explore
- Query Complexity: Ability to write complex multi-table queries with CTEs and window functions
- Business Translation: Converting SQL results into actionable business recommendations
- Optimization Focus: Writing efficient queries for large datasets
- Documentation: Clear commenting and documentation practices
- Diverse Applications: Experience across multiple industries and use cases
- Problem-Solving: Analytical approach to breaking down complex questions into SQL logic
- Total Projects: 6 major analysis projects
- Languages: 100% T-SQL / SQL
- Query Complexity: Beginner to Advanced
- Industries Covered: Retail, E-commerce, Finance, Social Media
- Analysis Types: Descriptive, Diagnostic, Segmentation, Trend Analysis
Last Updated: December 2025
This repository is actively maintained and regularly updated with new projects, query optimizations, and enhanced documentation.
This repository is available for educational and portfolio purposes. Feel free to reference or adapt queries for your own learning, with appropriate attribution.
"Data is the new oil, but SQL is the refinery that transforms raw data into valuable insights."
Interested in discussing SQL techniques, data analysis approaches, or potential opportunities?
Let's connect!