Exploratory Data Analysis (EDA)

Overview

Exploratory Data Analysis (EDA) is the critical first step in data science projects, systematically examining datasets to understand their characteristics, identify patterns, and assess data quality before formal modeling.

Core Concepts

• Data Profiling: Understanding basic statistics and data types
• Distribution Analysis: Examining how variables are distributed
• Relationship Discovery: Identifying patterns between variables
• Anomaly Detection: Finding outliers and unusual patterns
• Data Quality Assessment: Evaluating completeness and consistency

When to Use

• Starting a new dataset analysis
• Understanding data before modeling
• Identifying data quality issues