This project is an end-to-end data analysis using SQL and Power BI to explore global tech layoffs between 2020 and 2023 during Covid-19 pandemic. It includes data cleaning, transformation, and insightful visualizations.
- 🔍 Goal: Understand layoff trends by company, industry, country, and over time
- 🧹 Cleaned the raw data using SQL
- 📊 Visualized key insights using Power BI dashboards
- 📁 Tools Used: MySQL, Power BI
- Source: Layoffs data collected from public sources
- Fields include: Company, Location, Industry, Total Laid Off, Percentage Laid Off, Date, Stage, Country, Funds Raised
Key SQL steps:
- Removed duplicates
- Trimmed inconsistent values
- Standardized country and industry names
- Handled null or missing values
- Converted date formats
- Filtered unnecessary records
Here are the main dashboards created:
- Total layoffs by company, country, industry
- Highest percentage layoffs
- Time range: 2020 to 2023
- Monthly and yearly layoff trends
- Rolling totals for better trend visibility
- Layoffs by funding stage
- Year-wise company-wise layoff leaders
- Industry distribution over time
-
Total Layoffs by Year (2020–2023)
Bar chart showing the yearly distribution of layoffs. -
Layoffs by Country
Map visualization identifying the countries with the highest impact. -
Yearly Trend Line
Line graph representing the number of companies affected per year. -
Detailed Filters
Interactive slicers for:- Year
- Country
- Company
- Funding Stage
🖼️ Screenshots:
- The US saw the highest number of layoffs.
- 2022 was the peak year for layoffs.
- Startups in late-stage funding had more layoffs.
- The Tech & Crypto industries were hit hardest.
Feel free to connect with me:
- Name: Shah Md. Farhan Ruhullah
- 📧 farhanruhullah@gmail.com
This project demonstrates a full pipeline: Raw Data → SQL Cleaning → Power BI Dashboard → Insights