📘 Introduction

This project explores workforce trends through SQL-based data analysis. It focuses on understanding job market dynamics, salary structures, and demand for different job roles using powerful SQL queries.

The goal is to demonstrate how SQL can extract valuable insights from large datasets and help make data-driven decisions in hiring and workforce planning.

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📚 Background

The dataset contains details about job postings, including:

• Job Titles
• Company Names
• Locations
• Employment Types
• Average Yearly Salaries

By analyzing this data, we can uncover trends such as the most in-demand roles, salary ranges, and patterns across industries and locations.

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🛠️ Tools I Used

Tool	Purpose
SQL (PostgreSQL / MySQL)	Data querying, filtering, and analysis
Excel / CSV Files	Dataset storage and preprocessing
GitHub	Version control and project collaboration
VS Code / DBeaver	Query execution and result visualization

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📊 Analysis Performed

The following key analyses were conducted using SQL:

1. 🔍 Job Demand Analysis – Find the most common job titles
2. 💰 Salary Analysis – Compare average and total salaries by job role
3. 🏙️ Location-Based Insights – Identify top-paying cities and regions
4. 🧩 Salary Categorization – Classify salaries into Low, Medium, High
5. 📈 Top Companies Analysis – Discover top employers based on salary and demand

Sample Query Example:

SELECT 
    job_title_short,
    CASE
       WHEN salary_year_avg < 50000 THEN 'Low'
       WHEN salary_year_avg BETWEEN 50000 AND 60000 THEN 'Medium'
       WHEN salary_year_avg > 60000 THEN 'High'
       ELSE 'Others'
    END AS salary_category,
    COUNT(*) AS total_jobs
FROM job_postings_fact
GROUP BY job_title_short, salary_category;

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💡 What I Learned

Through this project, I gained hands-on experience with:

• Data cleaning and validation in SQL
• Aggregation functions (SUM, AVG, COUNT)
• Joins and Subqueries for relational analysis
• CASE WHEN logic for conditional grouping
• Turning raw data into meaningful business insights

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🏁 Conclusion

The Workforce Analysis Project highlights how SQL serves as a strong foundation for data analytics. It shows how structured querying can answer real-world business questions about hiring, compensation, and job market trends.

“Data tells the story — SQL helps you read it.”

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📂 Project Structure

SQL_Project_WorkForce_Analysis/
│
├── data/
│   └── job_postings_fact.csv
├── queries/
│   └── workforce_analysis.sql
├── README.md
└── results/
    └── insights_summary.csv

🤝 Connect With Me

👤 Tirupathi Rao 🌐 GitHub Profile 📧 [tirulesnar@gmail.com]