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🧹 Global Job Layoffs — SQL Data Cleaning & Analysis 🧠 Project Overview

This project focuses on cleaning and analyzing a dataset containing records of global job layoffs across multiple industries and countries. The dataset includes company details, locations, industries, number of layoffs, company stages, and total funds raised (in millions).

Using MySQL, the project demonstrates how to clean raw, inconsistent data, standardize formats, fill missing values, and perform exploratory data analysis to uncover key insights about the global workforce trends.

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🎯 Objectives

1 Remove duplicate and inconsistent records from the dataset. 2 Standardize and transform data types for accuracy and usability. 3 Handle missing and null values appropriately. 4 Derive insights on layoffs by company, industry, country, and time. 5 Identify funding and layoff trends across industries.

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🧰 Tools & Technologies

SQL (MySQL Workbench) RDBMS: MySQL 8+ Functions Used:

• ROW_NUMBER(), TRIM(), STR_TO_DATE(), GROUP BY, JOIN, UPDATE, DELETE, ALTER, SUM(), etc.

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🧹 Data Cleaning Process

The data cleaning process followed a systematic approach:

1. Duplicate Removal

   . Used ROW_NUMBER() window function to identify and delete duplicate rows. . Created temporary tables (layoffs1, layoffs2) to ensure safe data cleaning.

2. Trimming & Standardization

   . Removed extra spaces using TRIM() on text columns. . Corrected inconsistent entries (e.g., industry names like "crypto currency" → "crypto"). . Removed trailing characters from country names.

3. Data Type Corrections

   . Converted the date column from TEXT to DATE using STR_TO_DATE(). . Standardized numeric columns (total_laid_off, funds_raised_millions) for accurate aggregation.

4. Handling Null or Blank Values

   . Set empty strings to NULL. . Filled missing industry, total_laid_off, and percentage_laid_off where logical matches existed using JOIN statements. . Replaced remaining nulls in numeric fields with 0 where appropriate.

5. Data Validation

   . Checked for incomplete or irrelevant rows and deleted unusable entries. . Verified corrections using multiple SELECT validation queries.

📊 Exploratory Analysis & Insights

After cleaning, SQL queries were used to extract valuable insights, including:

Analysis Goal	SQL Summary Query	Example Output / Finding
Total layoffs globally	SELECT SUM(total_laid_off)	Total layoffs: X employees
Funds raised by industry	GROUP BY industry ORDER BY SUM(funds_raised_millions)	Tech sector raised the most funding
Top company by funds raised	LIMIT 1 query	Company X raised the highest funds
Industries most affected by layoffs	GROUP BY industry	Consumer & Tech sectors had the most layoffs
Preferred company location	GROUP BY location ORDER BY COUNT(location)	San Francisco had the highest company count
Layoffs by year	WHERE date LIKE '2022%'	Major layoffs occurred in 2022
Cross-industry presence	WHERE industry LIKE 'health%' OR industry LIKE 'crypto%'	Healthcare and crypto industries span multiple countries

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📈 Key Insights

• Total layoffs and funds raised across the dataset • Industries most affected by layoffs • Companies with highest layoffs and fundraising • Geographic trends in layoffs and funding • Yearly patterns and 2022-specific layoffs

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📂 Repository Structure

📁 SQL-DC-IPROJECT/
│visuals/
│   └── Overview.png
|       Insights.png
|       FinalTableSample.png
|README.md
| 
|data/
│   └── layoffs.csv             
│
|scripts/
│   └── layoffs.sql  
                              

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🧠 Skills Demonstrated

. SQL-based data cleaning and transformation . Exploratory analysis and business insight extraction . Query optimization and stepwise validation . Attention to data consistency and quality assurance

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📬 Contact

Name: Onagadanalyst Role: Data & Market Analyst | Building Expertise in Augmented Analytics and AI Automation LinkedIn: GitHub: https://github.com/o-danalyst Email: onagatheanalyst@gmail.com