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# MySQL_Exploratory_Data_Analysis | ||
Exploratory Data Analysis (EDA) of a dataset on company layoffs spanning from 2020 to 2023. This project aimed to uncover trends, identify key insights, and understand the broader impacts of layoffs over time. | ||
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Key Insights: | ||
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-Biggest Layoffs by Company: | ||
Google (2023): 12,000 employees | ||
Meta (2022): 11,000 employees | ||
Uber (2020): 7,525 employees | ||
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-Industries Hit the Hardest: | ||
Consumer Industry: 45,182 layoffs | ||
Retail Industry: 43,613 layoffs | ||
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-Global Layoff Trends: | ||
United States: 256,559 employees | ||
India: Significant impact | ||
Netherlands and Sweden: Notable layoffs | ||
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-Yearly Layoff Trends: | ||
2023: 125,677 layoffs | ||
2022: 160,661 layoffs | ||
2021: 15,823 layoffs | ||
2020: 80,998 layoffs | ||
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-Funding Stage Impact: | ||
Post-IPO: 204,132 layoffs | ||
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-Monthly Layoff Trends: | ||
Analyzed rolling totals to track peak periods and identify trends over time. | ||
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-Top Companies by Year: | ||
Identified the top 5 companies with the highest layoffs for each year. | ||
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Methodology: | ||
Data Cleaning: Removed duplicates, standardized data formats, and handled null values to ensure data accuracy. | ||
Analysis: Utilized advanced SQL techniques for detailed analysis. | ||
Visualization: Employed visualization tools to illustrate key trends and insights. | ||
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Conclusion: | ||
This project offered a comprehensive view of layoffs across various sectors, highlighting significant trends over recent years. It underscores the impact of global events, such as the COVID-19 pandemic, on employment |