📊 SQL Analytics Portfolio

A comprehensive collection of SQL projects demonstrating database design, complex queries, data analysis, and business intelligence solutions.

📋 Table of Contents

• About
• Projects Overview
• Skills Demonstrated
• Database Technologies
• Setup Instructions
• Project Details
• Key Learnings
• Contact

🎯 About

This repository showcases my SQL expertise through various projects involving database design, data manipulation, complex querying, and analytics. Each project addresses real-world business problems and demonstrates industry-standard database practices.

Focus Areas:

• Database schema design and normalization
• Complex SQL queries and joins
• Data analysis and reporting
• Performance optimization
• Business intelligence solutions

🗂️ Projects Overview

2. [HR Analytics] - HR Employee Analytics

Business Problem: Understand employee demographics, attrition, and department performance
Database: PostgreSQL
Key Queries:

• Department-wise headcount and salary analysis
• Employee turnover rate calculations
• Performance rating distributions
• Salary gap analysis by role and department

Skills Used: GROUP BY, HAVING, Aggregations, Date Functions

🔧 Skills Demonstrated

Core SQL Skills

• ✅ DDL (Data Definition Language): CREATE, ALTER, DROP
• ✅ DML (Data Manipulation Language): SELECT, INSERT, UPDATE, DELETE
• ✅ Complex Joins: INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF
• ✅ Aggregation Functions: COUNT, SUM, AVG, MIN, MAX
• ✅ Window Functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
• ✅ CTEs: Common Table Expressions for readable queries
• ✅ Subqueries: Correlated and non-correlated subqueries
• ✅ Views: Creating reusable virtual tables
• ✅ Indexes: Query optimization strategies
• ✅ Stored Procedures: Reusable SQL code blocks

Advanced Concepts

• Database normalization (1NF, 2NF, 3NF, BCNF)
• Entity-Relationship (ER) modeling
• Query optimization and performance tuning
• Transaction management (ACID properties)
• Data integrity constraints (Primary Keys, Foreign Keys, Unique, Check)

💾 Database Technologies

Database	Version	Use Cases
MySQL	8.0+	E-commerce, General analytics
PostgreSQL	14+	Complex queries, Advanced analytics

🚀 Setup Instructions

Prerequisites

• MySQL 8.0+ or PostgreSQL 14+
• MySQL Workbench / pgAdmin / DBeaver (Database GUI)
• Git

Installation Steps

1. Clone the repository

git clone https://github.com/nushant22/SQL-Analytics-Portfolio.git
cd SQL-Analytics-Portfolio

2. Choose a database system (MySQL or PostgreSQL)

3. Create database and import data

For MySQL:

# Login to MySQL
mysql -u root -p

# Create database
CREATE DATABASE project_name;

# Import SQL dump
mysql -u root -p project_name < project_folder/schema.sql
mysql -u root -p project_name < project_folder/data.sql

For PostgreSQL:

# Login to PostgreSQL
psql -U postgres

# Create database
CREATE DATABASE project_name;

# Import SQL dump
psql -U postgres -d project_name -f project_folder/schema.sql
psql -U postgres -d project_name -f project_folder/data.sql

4. Run queries Navigate to the specific project folder and execute the SQL files in your database client.

📊 Project Details

Project Structure

SQL-Analytics-Portfolio/
│
│
├── hr_analytics/
│   ├── schema.sql
│   ├── data.sql
│   ├── queries.sql
│   └── README.md
│
└── README.md

Sample Query Examples

Complex JOIN with Window Function

-- Calculate running total of sales by salesperson
SELECT 
    s.salesperson_name,
    o.order_date,
    o.order_amount,
    SUM(o.order_amount) OVER (
        PARTITION BY s.salesperson_id 
        ORDER BY o.order_date
    ) AS running_total
FROM 
    orders o
JOIN 
    salespersons s ON o.salesperson_id = s.salesperson_id
ORDER BY 
    s.salesperson_name, o.order_date;

CTE for Customer Segmentation

-- Segment customers by purchase frequency
WITH customer_metrics AS (
    SELECT 
        customer_id,
        COUNT(order_id) AS total_orders,
        SUM(order_amount) AS total_spent,
        AVG(order_amount) AS avg_order_value
    FROM orders
    GROUP BY customer_id
)
SELECT 
    customer_id,
    total_orders,
    total_spent,
    CASE 
        WHEN total_orders >= 10 THEN 'VIP'
        WHEN total_orders >= 5 THEN 'Regular'
        ELSE 'Occasional'
    END AS customer_segment
FROM customer_metrics
ORDER BY total_spent DESC;

📈 Key Business Insights

Each project includes:

• Problem Statement: Clear business question to answer
• Data Analysis: SQL queries to extract insights
• Visualizations: Charts and graphs (where applicable)
• Recommendations: Data-driven business recommendations

Example Insights:

• 📊 Identified top 20% products generating 80% revenue (Pareto principle)
• 👥 Discovered customer retention rate dropped 15% in Q4
• 💰 Found opportunity to increase revenue by 12% through cross-selling
• 📍 Revealed highest-performing regions for targeted marketing

🎓 Key Learnings

Through these SQL projects, I've developed:

• Strong foundation in relational database concepts
• Ability to design normalized database schemas
• Proficiency in writing complex, optimized SQL queries
• Experience with real-world business analytics scenarios
• Understanding of database performance optimization
• Skills in translating business requirements to SQL
• Capability to derive actionable insights from data

📚 Additional Resources

Helpful Links:

• MySQL Documentation
• PostgreSQL Documentation
• SQL Style Guide
• Mode Analytics SQL Tutorial

Learning Path:

1. Basic SQL (SELECT, WHERE, ORDER BY)
2. Joins and Relationships
3. Aggregations and GROUP BY
4. Subqueries and CTEs
5. Window Functions
6. Performance Optimization

🤝 Contributing

Suggestions and improvements are welcome! Feel free to:

• Report issues
• Suggest new project ideas
• Improve existing queries
• Add new analysis scenarios

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

Nushant Ghimire

• LinkedIn: nushant-ghimire-861b87325
• GitHub: @nushant22
• Email: [nushantghimire22@gmail.com]

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Last Updated: February 2026