📉 Layoffs Data Analysis Using SQL

This project investigates global tech and startup layoffs from 2020 to 2023 using SQL-based data cleaning and exploratory analysis. The goal is to uncover patterns in layoffs by industry, funding stage, geography, and time period.

The project was completed using:

• MySQL for data wrangling and querying
• .csv dataset as the raw data source
• SQL scripts for both cleaning and analysis

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📁 Files Included

File	Description
layoffs.csv	Raw dataset containing layoff data
Data_Cleaning_layoffs.sql	SQL queries for cleaning and preprocessing
Data_Analysis_layoffs.sql	SQL queries for exploratory data analysis

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🔍 Dataset Overview

• Rows: 2,361
• Columns: 9
• Time Period: From 2020 to early 2023
• Source: Publicly aggregated startup and tech layoff data

Columns:

Column	Description
company	Company name
location	City of operation
industry	Industry sector
total_laid_off	Number of employees laid off
percentage_laid_off	Percent of workforce laid off
date	Date of layoff event
stage	Funding stage (e.g., Series A, Post-IPO)
country	Country of operation
funds_raised_millions	Total funding raised in USD (millions)

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🧹 Data Cleaning (in Data_Cleaning_layoffs.sql)

The raw layoffs.csv dataset was imported into MySQL and cleaned using structured SQL logic. Below are the precise data cleaning steps carried out:

1. 🔄 Create Working Table

• A duplicate table was created from the original to preserve the raw data.

2. ❌ Remove Duplicate Records

• Duplicate rows were identified based on all major columns and removed to ensure each layoff event was unique.

3. 🧽 Standardize Text Fields

• Company names were trimmed to remove extra whitespace.
• Industry labels were standardized (e.g., all variations of "Crypto" were unified under one label).
• Country names were made consistent by resolving variations (e.g., "United States." was replaced with "United States").

4. 🗓️ Fix Date Format

• The date field was converted from text to a proper date format to enable time-based analysis.

5. 🛠 Fill Missing Values

• Missing values in the industry column were filled using matching company names from other complete rows.

6. 🗑️ Remove Irrelevant Rows

• Rows where both the total number and percentage of layoffs were missing were removed, as they provided no analytical value.

7. ✅ Finalize Cleaned Table

• A final cleaned version of the dataset was saved for analysis, with helper columns removed and structure standardized.

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📊 Data Analysis (in Data_Analysis_layoffs.sql)

The cleaned dataset was explored using SQL queries to answer key questions:

📌 Key Analyses:

• Which industries had the most layoffs?
• Top 10 companies with the highest number of laid-off employees
• Monthly trends of layoffs over time
• Layoffs by funding stage (Seed, Series A, Post-IPO, etc.)
• Geographic distribution of layoffs by country

📈 Insights Uncovered:

• The tech and retail industries saw the most frequent layoffs
• Post-IPO and Late-stage companies were heavily represented
• Layoffs surged in mid to late 2022, peaking around Q4
• Most layof## 📊 Data Analysis (in Data_Analysis_layoffs.sql)

The cleaned dataset was explored using SQL queries in MySQL to extract patterns and trends related to layoffs.

📌 Key Analyses Performed:

• Company with the maximum and minimum number of employees laid off
• Company with the highest and lowest percentage of workforce laid off
• Total layoffs by industry
• Layoffs by country
• Layoffs by year
• Layoffs by company funding stage
• Rolling monthly layoff trend across all years
• Top companies by total layoffs per year
• Top 5 companies each year (ranked yearly by layoffs)

📈 Insights Uncovered:

• Tech, Retail, and Transportation industries had the highest total layoffs
• The majority of layoffs occurred in 2022, with a noticeable peak in Q4
• The United States had the highest number of layoffs, followed by India
• Post-IPO and Late Stage companies experienced large-scale layoffs
• Companies like Meta, Amazon, and Byju's frequently appeared in the yearly top 5
• A continuous rise in layoffs was visible through the rolling monthly total

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🛠 Tools Used

• MySQL (8.0) – SQL execution and logic
• MySQL Workbench / DBeaver – for querying and result visualization
• CSV (layoffs.csv) – original raw dataset used for importing fs occurred in United States, followed by India and Canada
• Companies with massive funding also contributed to high layoff numbers, indicating that funding does not guarantee stability

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👤 Contributor

• Azim Nahin

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✅ How to Reproduce

1. Load layoffs.csv into a SQL database table (e.g., layoffs_raw)
2. Run Data_Cleaning_layoffs.sql to clean and create layoffs_cleaned
3. Execute Data_Analysis_layoffs.sql to generate summaries and insights

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📌 Example Queries (from Analysis)

-- Top 5 industries with most layoffs
SELECT industry, SUM(total_laid_off) AS total_laid_offs
FROM layoffs_cleaned
GROUP BY industry
ORDER BY total_laid_offs DESC
LIMIT 5;

-- Monthly trend of layoffs
SELECT DATE_TRUNC('month', date) AS month, SUM(total_laid_off) AS total
FROM layoffs_cleaned
GROUP BY month
ORDER BY month;