This repository showcases practical applications of SQL for data cleaning and exploration tasks. It focuses on a real-world dataset related to layoffs sourced from publicly available CSV files.

Project Overview ➡️

Data Acquisition

• Identify and source relevant public datasets for analysis.

Data Cleaning

• Utilize SQL statements to identify and address inconsistencies, missing values, and formatting errors within the layoff data
• Handling missing values (imputation or deletion)
• Standardizing data formats (dates, currencies, etc.)
• Removing duplicates or outliers
• Data validation and filtering

Data Exploration

• Perform exploratory data analysis (EDA) on the cleaned dataset to derive meaningful insights and visualizations.

1) Database Creation

• First, create a database called World_layoffs, where we will import the raw data from layoffs.csv

2) Data Cleaning

• Remove duplicates
• Standardize the data
• Remove null or blank values
• Remove any columns that are not relevant

3) Data Exploration

• Analyze the cleaned data to uncover trends, patterns, and insights.
• Utilize various SQL queries to explore different aspects of the dataset.

LinkedIn: Joaquin Rodriguez Figueroa | GitHub: joaquin-codes