Layoff Analysis - (2020 - 2023)
Project Overview
The dataset of layoffs spanning from 2020 to 2023 was imported into Excel for initial processing. Then performed data cleaning and Exploratory Data Analysis using SQL and for visualization, an interactive dashboard was created using Tableau. The dashboard provided a clear and interactive representation of the data, showcasing key metrics such as layoffs by year, affected industries, and regional breakdowns. The visualizations enabled a deeper understanding of how layoffs evolved over the years and helped identify critical patterns.
Objective
Analyze Layoff Trends
Understand Regional Impact
Correlate Layoffs with industry and location
Dataset Description
The dataset used in this layoff analysis project spans from 2020 to 2023 and contains detailed information about layoffs across various companies, industries, and regions. It includes 9 key columns: the company name, location, industry, total number of employees laid off, percentage of the workforce affected, and the date of the layoffs.
Link to Dataset
Tools Used
Excel
SQL
Tableau
Process Steps
The first step involved comprehensive data cleaning to handle missing values, remove duplicates, and standardize formats. This ensured that the dataset was ready for accurate analysis using SQL.
Next, exploratory data analysis (EDA)Â was performed to understand key trends and patterns within the data. Using Excel's features, the data was analyzed by factors such as industry, company size, region, and the volume of layoffs over time. Significant patterns were uncovered, revealing the industries most affected and the periods of highest layoffs.
Link to SQL Data cleaning code
Link to SQL Exploratory Data Analysis code
Finally, a dashboard was created using Tableau to visualize the insights and trends. The dashboard provided a clear and interactive representation of the data, showcasing key metrics such as layoffs by year, affected industries, and regional breakdowns. The visualizations enabled a deeper understanding of how layoffs evolved over the years and helped identify critical patterns.
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