SQL Layoffs Data Analysis

End-to-end data cleaning and exploratory data analysis performed exclusively using SQL to uncover global layoff trends across companies, industries, countries, and time.

📘 Project Overview

This project analyzes a global layoffs dataset using pure SQL. The workflow includes deduplication, standardization, null handling, and exploratory analysis to derive meaningful business insights.

📂 Dataset Description

The dataset contains company-level layoff records including company name, industry, total and percentage layoffs, country, funding stage, date, and funds raised. Raw data required extensive cleaning due to duplicates, missing values, and inconsistent formatting.

🛠 Tools Used

MySQL • SQL Window Functions • Aggregations • Joins

📊 Exploratory Data Analysis (SQL)

Top 10 Companies by Total Layoffs

      
SELECT company, SUM(total_laid_off) AS total_layoffs
FROM layoff_staging2
GROUP BY company
ORDER BY total_layoffs DESC
LIMIT 10;
      
    

Identifies companies contributing the highest number of layoffs.

Industry-wise Layoffs

      
SELECT industry, SUM(total_laid_off)
FROM layoff_staging2
GROUP BY industry
ORDER BY 2 DESC;

Shows which industries were most affected by layoffs.

Country-wise Layoffs

      
SELECT country, SUM(total_laid_off)
FROM layoff_staging2
GROUP BY country
ORDER BY 2 DESC;

Highlights countries with the highest number of layoffs.

Year-wise Layoff Trends

      
SELECT YEAR(date) AS year, SUM(total_laid_off)
FROM layoff_staging2
GROUP BY YEAR(date)
ORDER BY year DESC;

Analyzes how layoffs changed over time.

🔍 Key Insights

Layoffs are concentrated among a small set of companies.
Technology-driven industries dominate layoff counts.
The United States records the highest layoffs globally.
Layoff activity spikes in specific years.