Tech Layoffs — SQL Data Analysis

A two-phase SQL project that cleans a real-world tech layoffs dataset and performs exploratory analysis to surface trends across companies, countries, industries, and time.

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Project structure

 sql-layoffs-analysis
┣ 📄 Data_Cleaning_Project.sql
┗ 📄 Exploratory_Data_Analysis_Project.sql

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Phase 1 — Data cleaning

Transforms the raw layoffs table into a clean, analysis-ready layoffs_staging2 table.

Steps performed

• Duplicate removal — Uses ROW_NUMBER() OVER (PARTITION BY ...) to identify and delete exact duplicates across all key columns.
• Standardization — Trims whitespace, normalizes variant spellings (e.g. Crypto, CryptoCurrency → crypto), and strips trailing periods from country names.
• Date formatting — Converts text-format dates to proper DATE type using STR_TO_DATE() and ALTER TABLE … MODIFY COLUMN.
• Null handling — Blanks set to NULL; a self-join fills missing industry values from matching company rows.
• Row removal — Drops rows where both total_laid_off and percentage_laid_off are NULL.
• Cleanup — Drops the helper row_num column after deduplication.

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Phase 2 — Exploratory data analysis

Key analyses

• Max layoffs and max percentage laid off in a single event
• Companies that laid off 100% of staff — sorted by funding raised
• Total layoffs per company, country, and year
• Layoffs by company stage (Series A, IPO, Post-IPO, etc.)
• Monthly layoff totals with a running cumulative total using SUM() OVER (ORDER BY month)
• Top 5 companies by layoffs per year using DENSE_RANK() in a nested CTE

Sample — rolling monthly total

WITH Rolling_Total AS (
  SELECT SUBSTRING(`date`,1,7) AS `MONTH`,
         SUM(total_laid_off) AS total_off
  FROM layoffs_staging2
  WHERE SUBSTRING(`date`,1,7) IS NOT NULL
  GROUP BY `MONTH`
)
SELECT `MONTH`, total_off,
       SUM(total_off) OVER (ORDER BY `MONTH`) AS rolling_total
FROM Rolling_Total;

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🛠 Setup

1. Import the raw layoffs table into your MySQL database.
2. Run Data_Cleaning_Project.sql to generate the clean staging table.
3. Run Exploratory_Data_Analysis_Project.sql to explore the results.

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Skills demonstrated

CTEs · Window functions · DENSE_RANK · Self-joins · Data type conversion · NULL handling · String manipulation · Deduplication · Aggregation · Rolling totals

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Tool: MySQL Workbench*