Welcome! This repository showcases my hands-on experience and skills in SQL through real-world data analysis projects. Here, you'll find how I use SQL to transform raw data into actionable business insights. 📊🧑💻
Dive into HR analytics! This project explores employee data to reveal patterns in demographics, job satisfaction, attrition, and more. Using SQL, I answered key business questions and highlighted trends to support HR decision-making.
- Explored and validated HR datasets for accuracy
- Queried employee counts, averages, and distributions by department, age, education, etc.
- Identified top job roles and satisfaction levels by department
- Calculated attrition rates by age group to spot high-risk segments
- Investigated factors influencing attrition (age, education, satisfaction)
- Compared metrics across employee segments
- Highlighted top/bottom performers on satisfaction and attrition
- Leveraged SQL features: JOINs, GROUP BY, HAVING, CASE, and more
- Presented findings in a clear, actionable format for leadership
Analyzed car sales data to uncover pricing trends, model performance, and more. SQL was used to guide pricing, inventory, and marketing strategies with data-driven insights.
- Explored and cleaned car sales datasets
- Analyzed average selling prices by transmission, fuel type, ownership, etc.
- Identified high-mileage models for durability insights
- Assessed price variability within/across models
- Detected outliers in pricing and mileage
- Generated cumulative price trends and YoY comparisons
- Calculated moving averages for price smoothing
- Summarized mileage by transmission type
- Ranked models by price and historical averages
- Used advanced SQL: WINDOW functions, CTEs, JOINs
- Presented insights for business strategy and planning
Cleaned and analyzed call center data to improve service quality and customer satisfaction. The project focused on data preparation, trend analysis, and performance metrics.
- Imported and standardized call center data (dates, nulls, etc.)
- Generated summary statistics (row/column counts)
- Explored distinct values and distributions (sentiment, city, center)
- Analyzed call volume by day of week and max durations
- Evaluated customer satisfaction (min, max, avg CSAT)
- Filtered out invalid CSAT scores for accuracy
- Assessed service performance by response time and center ranking
SQL (Structured Query Language) is essential for working with relational databases. Key features include:
- CRUD operations (Create, Read, Update, Delete)
- Joins for combining tables
- Aggregations (SUM, COUNT, AVG, etc.)
- Subqueries and nested logic
- Stored procedures for reusable routines
- Window functions for advanced analytics
All projects were developed using Microsoft SQL Server Management Studio. 🖥️
- Install a SQL database (e.g., SQL Server Express)
- Import any
.sqlfile from this repo to run the queries - Adapt the queries for your own data as needed
✨ Thanks for visiting! Feel free to explore, learn, and reach out with any questions or feedback. Happy querying! ✨