📊 Layoffs Data Cleaning & Exploratory Data Analysis using SQL

This project demonstrates a full SQL-based data analytics workflow: starting from cleaning raw layoff data and ending with exploratory data analysis (EDA) to extract meaningful business insights. The dataset was taken from the Alex The Analyst Data Analyst Bootcamp.

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📁 Dataset Information

• Name: layoffs.csv
• Source: GitHub (via Alex The Analyst Bootcamp)
• Content: Layoffs from global tech companies during 2020–2023
• Columns include:
  • Company, Location, Industry
  • Total Laid Off, % Laid Off
  • Date of Layoff
  • Company Stage (e.g., Series A, Series C)
  • Country, Funding Raised

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🛠 Tools & Skills Used

Tool	Purpose
MySQL	SQL scripting, transformations, and analysis
SQL Techniques	CTEs, ROW_NUMBER, GROUP BY, JOINS, CASE, TRIM, REPLACE, DATE FORMATTING, WINDOW FUNCTIONS, DENSE_RANK

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🔧 Phase 1: Data Cleaning (DATA_CLEANING_PROJECT.sql)

✅ Cleaning Objectives:

1. Remove duplicates using ROW_NUMBER() in a CTE
2. Standardize inconsistent entries like:
   • Company names (trim extra spaces)
   • Industry names (e.g., 'Crypto/Blockchain' → 'Crypto')
   • Country names (e.g., remove trailing '.' in 'United States.')
3. Fix date formatting using STR_TO_DATE()
4. Handle missing values by:
   • Replacing empty strings with NULL
   • Updating NULLs using inferred data from other rows
5. Delete irrelevant records
   • Rows with both total_laid_off and percentage_laid_off as NULL
6. Drop helper columns like row_num after cleaning

🧹 Key Queries Used:

-- Assign row numbers to detect duplicates
ROW_NUMBER() OVER (
  PARTITION BY company, location, industry, total_laid_off, percentage_laid_off, date, stage, country, funds_raised_millions
)

-- Trim company names
UPDATE layoffs_staging2
SET company = TRIM(company);

-- Format date column
UPDATE layoffs_staging2
SET date = STR_TO_DATE(date, '%m/%d/%Y');

-- Drop extra column
ALTER TABLE layoffs_staging2
DROP COLUMN row_num;