https://github.com/macdon112/layoff-analysis

SQL data cleaning & analysis of global layoffs
https://github.com/macdon112/layoff-analysis

data-analysis data-cleaning data-exploration sql

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SQL data cleaning & analysis of global layoffs

• Host: GitHub
• URL: https://github.com/macdon112/layoff-analysis
• Owner: Macdon112
• Created: 2025-03-20T11:49:01.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-03-27T20:31:13.000Z (10 months ago)
• Last Synced: 2025-03-27T21:33:40.196Z (10 months ago)
• Topics: data-analysis, data-cleaning, data-exploration, sql
• Homepage:
• Size: 61.5 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Layoffs Data Analysis Project 

**Key Goals?** 

I cleaned up a messy dataset of layoffs of companies to make it reliable for analysis.  I removed duplicates, fixed typos, and filled in missing values, preparing it for analysis, and identifying trends, in layoffs  by company, industry and country.

### **Data cleaning steps:**  

1. **Removing Duplicates**

   Created a staging table (`layoffs_staging2`) to preserve raw data. (Made a backup of the raw data) 

   Spotted repeat entries (like two entries for "Casper") and deleted them.  

        

2. **Standardised messy details**  

   Fixed inconsistent categories  

   Merged *"cryptocurrency"* and *"crypto"* into one category: **"crypto"**.  

   Cleaned country names (e.g., *"United States."* became *"United States"*).  

   Trimmed spaces in company names (so "Google " became "Google").  

3. **Handled missing data**  

   Replaced blank *industry* fields with `NULL` to avoid confusion.  

   Filled in missing industries using existing data (e.g., used "Airbnb’s" industry for its missing entries).  

   Deleted 4 rows where layoff numbers were missing  

4. **Fixed dates**  

   Turned text-based dates (like *"3/12/2022"*) into proper `DATE` format for smoother analysis.  

## **How to run this project**  

1. **download the files**  

   **Dataset**: (Data/layoffs.csv)   

   **SQL scripts**: SQL-Scripts/Data_Cleaning.sql and Data_Exploration.sql.  

2. **Run the SQL scripts** *(in this order)*  

   **First**: `Data_Cleaning.sql` – cleaning the raw data.  

   **Then**: `Data_Exploration.sql` – digs into trends (like "Which industries laid off the most?").  

## **What I discovered**   

**12 duplicate rows** were removed (e.g., "Casper" was listed twice).  

**"Crypto"** became the standard name for all crypto-related industries.  

**4 incomplete rows** were removed – no half-baked data here  

## Key Insights 

**Biggest layoffs**: Amazon (18,000 employees in 2022).

**Peak layoffs**: March 2022.