World's Layoffs

World's Layoffs Analysis

Objective: The "World's Layoffs Analysis" project aims to provide a comprehensive understanding of global layoff trends through data-driven insights. Using Power BI and SQL, the project focuses on extracting, transforming, and visualizing data to analyze the impact of layoffs across various industries, companies, and regions. The goal is to help stakeholders identify patterns, correlations, and outliers that could influence strategic decision-making.

Scope: The dataset includes key parameters such as company, industry, location, total employees laid off, percentage of employees laid off, date of layoffs, company stage, country, and fund-raising amounts. These data points are crucial for understanding the broader context of layoffs in the global market.

Key Features:

1. Data Cleaning & Transformation:

   • Performed extensive data cleaning in MySQL to ensure data accuracy and consistency.
   • Addressed missing values, standardized data formats, and removed duplicates to maintain data integrity.

2. Exploratory Data Analysis (EDA):

   • Conducted EDA to uncover key insights about layoff trends across different industries, countries, and company stages.
   • Analyzed the correlation between fund-raising amounts and layoff rates to understand how financial health impacts employee retention.

3. Visualization & Dashboard Creation:

   • Developed an interactive Power BI dashboard featuring key metrics, including total layoffs, layoffs by industry, layoffs by location, and the impact of fund-raising on layoffs.
   • Incorporated slicers and filters, such as company and industry, to allow users to explore the data dynamically.
   • Used scatter plots, bar charts, and pie charts to visualize complex relationships and make the data more accessible.

4. Insights & Recommendations:

   • Identified industries and regions most affected by layoffs.
   • Highlighted companies with significant layoffs despite large fund-raising efforts, suggesting potential financial mismanagement or strategic pivots.
   • Provided actionable recommendations based on data insights to help companies and policymakers mitigate future layoffs.

Tools & Technologies:

• SQL (MySQL): For data extraction, cleaning, and transformation.
• Power BI: For creating interactive visualizations and dashboards.
• Python: For additional data analysis and handling complex transformations.

Outcome: The World's Layoffs Analysis project delivers a robust analytical tool for understanding global layoff trends, offering valuable insights into how economic conditions, industry shifts, and financial strategies impact employment. The findings from this project can support better decision-making for companies, investors, and policymakers alike.

Github : abhishekvermacu20/World-s-Layoffs (github.com)