Layoffs SQL Project – Part 1: Data Cleaning

This project focuses on cleaning and preparing a dataset containing global layoff events across industries and time. The aim of this stage is to ensure the data is reliable and ready for analysis.

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Overview

Before drawing any insights, the raw data needed significant preparation. In this part of the project, I focused on:

• Removing duplicates
• Standardizing inconsistent entries
• Converting date formats
• Addressing missing values

All steps were performed using SQL, and a separate staging table was used throughout to keep the original data intact.

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Key Steps

1. Created Staging Tables

To avoid altering the raw data, a copy was created and used as a working table (layoff_staging → layoffs_staging2).

2. Identified and Removed Duplicates

Used ROW_NUMBER() with a window function across multiple columns to flag and delete duplicate records.

3. Standardized Text Fields

• Trimmed white spaces from company and industry names
• Corrected inconsistent entries (e.g., "CryptoCurrency" standardized to "Crypto")
• Cleaned up country and location names, including encoding errors (e.g., "Düsseldorf" → "Dusseldorf")

4. Converted Date Formats

Transformed date from string to SQL DATE format using STR_TO_DATE() and updated the table accordingly.

5. Handled Missing and Blank Values

• Replaced blank strings with NULL
• Inferred missing values where possible using existing company data
• Removed rows with no layoff-related data (i.e. both total_laid_off and percentage_laid_off were null)

6. Final Cleanup

Removed the helper column row_num used for deduplication.

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Layoffs SQL Project – Part 2: Exploratory Data Analysis

After cleaning the data, the next step was to explore it and look for trends in layoffs across companies, industries, and countries over time.

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Overview

Using SQL, I answered key questions such as:

• Which companies had the highest number of layoffs?
• What were the trends over time by year and by month?
• Which countries and industries were most affected?
• How many companies laid off their entire workforce?
• Who were the top 5 companies with the most layoffs each year?

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What I Looked At

1. Biggest Layoffs

• Found the highest total layoffs and companies that laid off 100% of their staff
• Listed companies with the most layoffs overall

2. Trends Over Time

• Checked layoffs by year and by month
• Created a rolling total to see how layoffs built up over time

3. Industry and Country Insights

• Looked at total layoffs by industry
• Found which countries had the most layoffs

4. Company Trends by Year

• Broke down layoffs by company and year
• Used window functions to rank the top companies each year

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Sample Insights

• Companies like Meta, Amazon, and Google reported the highest total layoffs.
• The retail and consumer industry experienced the most significant impact during the analyzed period.
• Layoffs peaked in late 2022 and early 2023.
• The U.S. saw the largest share of total layoffs across all countries.