Data Cleaning and Exploratory Analysis with MySQL

This project demonstrates how to clean and explore a raw dataset using SQL. It focuses on fixing common data quality issues and performing summary analyses to prepare the data for further insights.

Overview:

The workflow includes:

Data Cleaning:

• Remove duplicates
• Handle null and missing values
• Standardize date formats
• Group ages into categories
• Drop irrelevant columns

Exploratory Data Analysis (EDA):

• Salary statistics (min, max, average, standard deviation)
• Department-level salary summaries
• Identify top earners and salary distributions
• Find duplicate names, shared salaries, and upcoming birthdays or anniversaries

Features:

• Uses SQL window functions (ROW_NUMBER(), NTILE(), RANK()) for deduplication and ranking.
• Standardizes inconsistent date formats.
• Groups employees into age bands (20–30, 30–40, etc.).
• Identifies key insights like highest paid employees and department salary spend.

Example Insights:

• Top Earners: Who earns the most in the company.
• Department Summary: Total salaries and average salaries by department.
• Upcoming Events: Employees with birthdays or anniversaries this month.

Why This Matters: Clean data is the foundation for meaningful analysis. This project shows how SQL alone can resolve common data issues and uncover trends.