SQL Layoffs Data Cleaning & Exploratory Data Analysis

Overview

This project demonstrates an end-to-end SQL workflow using a global layoffs dataset. The project covers both Data Cleaning and Exploratory Data Analysis (EDA) in MySQL to transform raw data into actionable business insights.

---

Objectives

Data Cleaning

• Remove duplicate records
• Standardize text values
• Handle null and blank values
• Format date fields
• Prepare an analysis-ready dataset

Exploratory Data Analysis

• Analyze layoffs by company, industry, and funding stage
• Identify yearly and monthly layoff trends
• Find companies with 100% workforce reductions
• Rank top companies by layoffs each year
• Generate business insights from the data

---

Tools Used

• MySQL
• MySQL Workbench
• Git & GitHub
• CSV / Excel

---

SQL Skills Demonstrated

• Data Cleaning
• Common Table Expressions (CTEs)
• Window Functions
• ROW_NUMBER()
• DENSE_RANK()
• Aggregate Functions (SUM, AVG, MIN, MAX)
• Joins
• Date Functions
• String Functions
• Trend Analysis
• Rolling Totals

Key Analyses

• Total layoffs by company
• Total layoffs by industry
• Layoffs by funding stage
• Monthly and yearly trends
• Rolling cumulative layoffs
• Top 5 companies by layoffs each year
• Companies with 100% layoffs

---

Key Insights

• Several companies laid off their entire workforce.
• The technology sector experienced significant layoffs.
• Layoffs varied considerably across industries and years.
• Both startups and established companies were affected.

---

Key Learning Outcomes

This project strengthened my skills in:

• SQL Data Cleaning
• Exploratory Data Analysis
• Window Functions
• Ranking & Trend Analysis
• Writing efficient SQL queries
• Deriving business insights from data

---

Author

Sunilkumar Labana

Aspiring Data Analyst

Skills: SQL | Excel | Tableau | Power BI | Python