SQL Layoffs 
Personal Project

Project Type: Data Cleaning | EDA
Date: October 2023
Location: Dallas, TX
Skills & Tools: MySQL | Excel | Data Cleaning | Exploratory Data Analysis

Achievements of Project

• Accomplished duplicate removal measured by the count of unique entries after execution, doing meticulous comparisons of records across columns.

• Achieved data standardization measured by the uniformity in formatting of company names, industry descriptions, and country names, by implementing normalization rules in SQL.

• Handled null and blank values effectively measured by the reduction of missing data points, by converting blanks to NULLs and imputing missing industry data.

• Identified trends in layoffs by industry and location measured by the frequency and distribution of layoffs, using SQL queries to group and visualize data.

• Explored correlations between layoffs and company funding measured by statistical relationships, performing SQL operations to calculate and visualize these correlations.

• Analyzed monthly and yearly patterns in layoffs measured by temporal distribution, using date manipulations and aggregations in SQL to generate trend overviews.

• Determined the company with the biggest amount of layoffs measured by the total laid-off count, pinpointing key vulnerable players and industry shifts.

• Identified the industry with the most layoffs overall and in 2022 measured by aggregate layoffs, offering insights into sector-specific downturns and economic analysis.

• Analyzed layoffs by business stage measured by layoffs per stage category, revealing insights into the relative stability of startups versus mature companies.

• Provided a rolling total for layoffs throughout the months measured by the cumulative effect over time, utilizing SQL cumulative functions to display ongoing impacts.

• Tracked the top 5 companies with the most layoffs across multiple years measured by annual layoffs data, enabling long-term trend analysis and forecasting.

• Highlighted the top 3 hardest-hit industries in recent years measured by the count and impact of layoffs, guiding strategic decisions in policy and investments.