Exploratory Data Analysis: Discovering Insights

Learning Objectives: After this lesson, you'll master the systematic approach to understanding data—summarizing, finding patterns, detecting anomalies, and forming hypotheses through effective EDA workflows.

What is EDA?

Exploratory Data Analysis (EDA) is the process of investigating data to discover patterns, spot anomalies, test hypotheses, and check assumptions. It's detective work with data.

The EDA Workflow

The EDA process follows a systematic approach. Explore this interactive workflow diagram:

Step 1: Load and Inspect

Use this interactive DataFrame explorer to practice EDA techniques. Try switching between Table, Statistics, Distributions, and Correlations views:

Step 2: Data Quality Assessment

Step 3: Univariate Analysis

Analyze each variable individually.

Numerical Variables

Categorical Variables

Step 4: Bivariate Analysis

Explore relationships between pairs of variables. Here's an interactive scatter plot showing the relationship between income and monthly charges:

Numerical vs Numerical

Numerical vs Categorical

Categorical vs Categorical

Step 5: Document Insights

EDA Checklist

Key Takeaways

✅ EDA is systematic — Follow a workflow: load → quality → univariate → bivariate → insights

✅ Ask questions — Let curiosity guide exploration, not confirmation bias

✅ Document everything — Findings, anomalies, hypotheses, and decisions

✅ Use multiple views — Statistics AND visualizations complement each other

✅ Iterate — EDA is not linear; discoveries lead to new questions

✅ Quality first — Address data quality before analysis

Connections: EDA in the Data Science Pipeline

🔗 Connection to Machine Learning

EDA directly informs modeling decisions:

🔗 Connection to Business

Practice Exercise

Next Steps

In the next lesson, we'll build the Statistical Foundations needed for data science—distributions, hypothesis testing, and correlation analysis.

Ready to add statistical rigor to your analysis? Let's dive into statistics!