📊 Layoffs Data Analysis using SQL

🔍 Project Overview

This project explores a real-world layoffs dataset to uncover trends across companies, time periods, industries, and regions.

The goal is to answer key questions such as:

• Which companies laid off the most employees?
• When did layoffs peak?
• Which countries and stages were most affected?
• Are layoffs random or pattern-driven?

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📁 Dataset

The dataset contains:

• Company name
• Date of layoff
• Total employees laid off
• Percentage of workforce laid off
• Company stage
• Country
• Funds raised (in millions)

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⚙️ Tools & Technologies

• SQL (MySQL)
• Window Functions
• Common Table Expressions (CTEs)

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📌 Key Analysis Performed

1. Data Exploration

• Checked overall dataset structure
• Identified missing and extreme values

2. Company Analysis

• Total layoffs per company
• Companies with repeated layoffs

3. Time-Based Analysis

• Layoffs by year and date
• Monthly trend analysis
• Rolling cumulative layoffs over time

4. Geographic Analysis

• Layoffs by country

5. Stage Analysis

• Layoffs by company stage (Startup, Growth, etc.)

6. Extreme Cases

• Companies with 100% layoffs

7. Ranking Analysis

• Top 5 companies per year using DENSE_RANK()

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📊 Key Insights

• Layoffs are not random; they follow clear time-based trends
• Certain months show significant spikes, indicating economic events
• Some companies laid off 100% of employees, even after raising large funds
• A small number of companies contributed to a large share of total layoffs
• Layoffs occurred across all stages, including late-stage companies

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📈 Advanced SQL Concepts Used

• GROUP BY for aggregation
• DATE_FORMAT() and YEAR() for time analysis
• SUM() OVER() for rolling totals
• DENSE_RANK() for ranking within groups
• CTEs for structured and readable queries

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🧠 Sample Query (Top Companies Per Year)

WITH Company_Year AS (
    SELECT company, YEAR(date) AS years, SUM(total_laid_off) AS total_laid_off
    FROM layoff_staging2
    GROUP BY company, YEAR(date)
),
Company_Year_Rank AS (
    SELECT *,
    DENSE_RANK() OVER (PARTITION BY years ORDER BY total_laid_off DESC) AS ranking
    FROM Company_Year
    WHERE years IS NOT NULL
)
SELECT *
FROM Company_Year_Rank
WHERE ranking <= 5;

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🚀 Future Improvements

• Add data visualizations (Python / Power BI / Tableau)
• Clean and standardize company & country names
• Combine with external economic indicators
• Build an interactive dashboard

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📌 Conclusion

This project demonstrates how SQL can be used not just for querying data, but for extracting meaningful insights and identifying real-world trends.

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