This project explores a real-world layoffs dataset using MySQL. The goal was to clean raw data, perform exploratory data analysis, and identify meaningful trends across companies, industries, countries, and time.
This is my first end-to-end SQL data analysis project, covering:
- Data cleaning
- Exploratory analysis
- Business insights
- MySQL
- SQL (CTEs, Window Functions, Aggregations, Ranking)
- GitHub for version control and portfolio presentation
01_data_cleaning.sql Data cleaning steps including:
- Removing duplicates
- Standardizing values
- Handling NULL values
- Formatting date fields
02_exploratory_analysis.sql Exploratory analysis including:
- Company-level layoffs
- Industry trends
- Country trends
- Time-based analysis
- Rolling totals
- Ranking top companies per year
03_key_insights.md Summary of business insights derived from the analysis.
- Which companies had the highest layoffs?
- Which industries were most affected?
- Which countries saw the most layoffs?
- How did layoffs trend over time?
- Which companies had the largest layoffs each year?
SQL Skills:
- GROUP BY and Aggregations
- JOIN logic concepts
- Window Functions
- CTEs
- Data Cleaning Techniques
- Time Series Analysis
- Ranking Functions
Analytical Skills:
- Trend analysis
- Identifying patterns
- Translating data into insights
The dataset contains global layoff records including:
- Company
- Industry
- Country
- Total laid off
- Percentage laid off
- Date
Through this project I learned:
- How real-world datasets require cleaning before analysis
- How to structure SQL scripts professionally
- How to present analysis in a portfolio-ready format
- How to translate SQL output into business insights
Possible next steps:
- Visualizing trends using Power BI or Tableau
- Building a dashboard
- Adding forecasting analysis
Bon Joseph Entry-level Data Analyst (SQL-focused)