- Project Overview
- Dataset
- SQL Queries
- Methodology
- Outputs
- Key Findings
- Date Range of Layoff Data
- Largest Layoffs
- Companies with Entire Workforce Laid Off
- Companies with the Highest Total Layoffs
- Industries with the Highest Total Layoffs
- Countries with the Highest Total Layoffs
- Dates with the Highest Total Layoffs
- Total Layoffs by Year
- Layoffs by Company Stage
- Top Layoff Companies by Year
- Conclusion
- Future Work
- How to Use This Project
- Contact
This project performs an exploratory data analysis (EDA) on a dataset containing layoff information from various companies using MySQL. The dataset includes details on layoffs across different industries, countries, and years, starting from March 11, 2020, through November 22, 2024. The purpose of this analysis is to uncover trends, patterns, and insights related to layoffs over time.
The dataset used in this project is a cleaned version of layoff data, which was processed in my previous project:
The cleaned dataset is available in this repository as:
📄 cleaned_data_for_EDA.csv
The dataset used in this project is named layoffs_staging2, which contains information about layoffs such as:
- Company name
- Industry
- Country
- Date of layoff
- Number of employees laid off (
total_laid_off) - Percentage of employees laid off (
percentage_laid_off) - Company stage (e.g., startup, post-IPO, etc.)
The SQL queries used for this analysis are provided in the Exploratory Data Analysis for Layoffs.sql file within this repository. This file contains all the queries used to extract insights from the dataset, covering:
- Aggregations:
SUM,MAX,MIN - Grouping & Ordering:
GROUP BY,ORDER BY - Ranking Functions:
DENSE_RANK,RANK - Rolling Totals & Cumulative Sums
- Common Table Expressions (CTEs)
This analysis was conducted using MySQL queries, without the use of data visualization tools. The queries include:
- Aggregations (SUM, MAX, MIN)
- Grouping and ordering (GROUP BY, ORDER BY)
- Window functions (DENSE_RANK for ranking companies by year)
- CTEs (Common Table Expressions) for rolling totals and rankings
The results of the SQL queries have been saved in the outputs folder of this repository. These files contain key insights derived from the analysis, including:
- Total Layoffs by Year – Breakdown of layoffs per year.
- Total Layoffs by Industry – Summarized layoffs across different industries.
- Total Layoffs by Country – Layoffs categorized by country.
- Total Layoffs by Date – Layoffs recorded on specific dates.
- Total Layoffs by Month Each Year – Layoffs aggregated by month within each year.
- Rolling Total by Each Month – Cumulative layoffs calculated per month.
- Total Layoffs by Stage – Analysis of layoffs based on company growth stage.
- Layoffs by Companies per Year – Company-wise layoffs reported each year.
- Ranking of Total Layoffs by Companies per Year – Companies ordered by total layoffs per year.
- Dense Rank of Layoffs by Companies per Year – Layoff rankings using a dense ranking method.
- Top 5 Companies per Year – The five companies with the highest layoffs each year.
- Largest Layoffs – Companies with the highest number of layoffs in a single event.
- Companies with Entire Workforce Laid Off – List of companies that laid off 100% of their employees.
These outputs provide a structured way to review key trends and insights found in the dataset.
- The dataset includes layoffs from March 11, 2020, to November 22, 2024.
- The maximum number of employees laid off in one instance was 15,000.
- The maximum percentage laid off was 100%, meaning some companies completely shut down.
- 288 companies had a 100% layoff rate.
- The company with the largest total layoff in this category was Katerra (2,434 employees laid off).
| Company | Total Laid Off |
|---|---|
| Amazon | 27,840 |
| Meta | 21,000 |
| Intel | 16,057 |
| Microsoft | 14,708 |
| Tesla | 14,500 |
| Cisco | 14,300 |
| 13,472 | |
| Dell | 12,650 |
| Salesforce | 11,140 |
| SAP | 11,000 |
| Industry | Total Laid Off |
|---|---|
| Retail | 71,703 |
| Consumer | 71,046 |
| Other | 61,912 |
| Transportation | 60,548 |
| Hardware | 54,870 |
| Country | Total Laid Off |
|---|---|
| United States | 455,331 |
| India | 56,469 |
| Germany | 28,572 |
| United Kingdom | 20,090 |
| Netherlands | 19,005 |
| Date | Total Laid Off |
|---|---|
| 2023-01-04 | 16,171 |
| 2024-08-01 | 15,180 |
| 2022-11-16 | 14,926 |
| 2023-01-20 | 14,682 |
| 2024-04-15 | 14,000 |
| Year | Total Laid Off |
|---|---|
| 2024 | 149,006 |
| 2023 | 264,220 |
| 2022 | 164,319 |
| 2021 | 15,823 |
| 2020 | 80,998 |
- 2023 saw the highest number of layoffs overall.
- The high layoff numbers in 2020 may be due to COVID-19 lockdowns.
| Stage | Total Laid Off |
|---|---|
| Post-IPO | 375,102 |
- Post-IPO companies had the highest total layoffs.
| Year | Company | Total Laid Off |
|---|---|---|
| 2020 | Uber | 7,525 |
| 2021 | Bytedance | 3,600 |
| 2022 | Meta | 11,000 |
| 2023 | Amazon | 17,260 |
| 2024 | Intel | 15,062 |
This EDA provides insights into layoff trends across industries, countries, and years, revealing significant events such as mass layoffs in large corporations and post-COVID-19 effects. The data suggests that layoffs peaked in 2023, with Amazon, Meta, and Intel being the most affected companies.
- Data Visualization: Using Tableau or Power BI to create charts and graphs for better insights.
- Predictive Analysis: Applying machine learning techniques to forecast future layoffs.
- Deeper Industry-Specific Analysis: Investigating layoffs based on business performance.
- Clone the repository to your local machine.
- Load the dataset into MySQL.
- Run the provided SQL queries in the
Exploratory Data Analysis for Layoffs.sqlfile to explore the dataset. - Review the outputs in the
Outputsfolder for key insights and trends.
For questions or feedback, feel free to reach out to me on GitHub or via email.