Apr 6, 2025
MySQL Exploratory Data Analysis (EDA) on Layoffs Dataset
This SQL-based project explores the global layoffs landscape through the lens of structured data analysis. Using the cleaned version of the Layoffs dataset from the previous Data Cleaning Project, the goal was to identify significant patterns, trends, and outliers to understand the scope and nature of tech layoffs across companies, industries, and time. The analysis was performed using MySQL, and the full code can be found on GitHub.
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Following a detailed data cleaning phase from the previous Project, this exploratory analysis picks up with a ready-to-query staging table: layoffs_staging2. The aim was to generate descriptive insights that could help answer real-world questions, such as:
Which companies had the highest layoffs?
How widespread were layoffs across different countries and industries?
Were there notable peaks during specific months or years?
Did early-stage companies suffer more?
These insights are drawn purely from SQL queries, demonstrating the power of SQL in deriving analytical value from well-structured relational data.


Basic Exploration & Initial Metrics
We began by viewing the cleaned dataset:
Then, we explored maximum values:
Detecting Extreme Layoffs
To find companies that laid off 100% of their workforce:
Ordering by their fundraising to highlight scale:
Largest Layoffs (Single Event & Cumulative)
Single largest layoffs:
Companies with the highest total layoffs:
Geographic Impact
By city and country:
Time-Based Trends
Layoffs by year:
By industry and funding stage:
Advanced Aggregations
Top companies per year by layoffs:
Rolling monthly layoffs:

This analysis brought to light the scale and depth of layoffs in recent years. We found companies that laid off entire teams, regions most affected (with the U.S. standing out significantly), and periods when layoffs peaked. Industries like tech and crypto appeared disproportionately represented, especially among early-stage startups.
What made this project especially valuable was not just the numbers, but the story that emerged:
Some companies raised millions, only to fold months later.
A few firms repeatedly appeared at the top of layoff charts, signaling ongoing instability.
Layoffs surged in specific periods—often aligning with broader economic downturns or shifts in investment climates.
The next steps will include:
Data visualization: Transforming these insights into interactive dashboards with tools like Tableau or Power BI.
Predictive analysis: Exploring if layoffs can be forecasted based on funding stages or market trends.
Cross-dataset enrichment: Merging with stock performance, hiring data, or social sentiment for richer insights.


