📊 Exploratory Data Analysis (EDA) on Layoffs Data using SQL

📌 Project Description

This project performs Exploratory Data Analysis (EDA) using SQL on a layoffs dataset to uncover trends, patterns, and insights related to workforce reductions across companies, industries, countries, and time periods.

The analysis helps understand when, where, and how layoffs have occurred, supporting data-driven insights into labor market dynamics.

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🎯 Objectives

• Analyze layoff trends over time
• Identify industries and companies most impacted by layoffs
• Understand geographic distribution of layoffs
• Detect spikes and anomalies in layoff events
• Generate insights for economic and business analysis

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🗂 Dataset Overview

• Dataset: Company Layoffs Data
• Granularity: Company-level layoff events
• Key Columns:
  • Company
  • Industry
  • Country
  • Total Laid Off
  • Percentage Laid Off
  • Date of Layoff
  • Company Stage

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🛠 Tools & Technologies

• SQL
• SQL IDE (MySQL Workbench)

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🔍 EDA Process Using SQL

1️⃣ Data Understanding

• Reviewed table structure and data types
• Checked record counts and date ranges

2️⃣ Data Quality Checks

• Identified missing and NULL values
• Checked for duplicate layoff events
• Validated percentage and total layoff values

3️⃣ Trend Analysis

• Year-wise and month-wise layoff trends
• Identified peak layoff periods
• Analyzed post-pandemic layoff patterns

4️⃣ Industry & Company Analysis

• Industries most affected by layoffs
• Companies with highest layoff counts
• Layoffs by company stage (Startup, Growth, Enterprise)

5️⃣ Geographic Analysis

• Country-wise and region-wise layoff distribution
• Comparison of layoffs across major economies

6️⃣ Outlier & Spike Detection

• Identified sudden spikes in layoff numbers
• Detected unusually high layoff percentages

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🧠 SQL Concepts Used

• SELECT, WHERE, ORDER BY
• GROUP BY, HAVING
• Aggregate functions (SUM, COUNT, AVG)
• CASE WHEN
• Date functions
• CTEs (WITH clause)
• Window functions

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📊 Sample Business Questions Answered

• Which industries experienced the highest layoffs?
• Which companies had the largest layoff events?
• How did layoffs trend year-over-year?
• Which countries were most impacted by layoffs?
• Are layoffs more common in early-stage or late-stage companies?

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✅ Key Outcomes

• Identified major layoff trends across industries and regions
• Highlighted companies and sectors most impacted
• Built a strong analytical foundation for visualization and reporting

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🚀 Future Enhancements

• Visualize trends using Power BI or Tableau
• Build time-series dashboards
• Automate EDA using SQL scripts
• Combine EDA with economic indicators

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👤 Author

Pushpak Umale
Data Analyst | SQL | Power BI | Business Analytics