Summary of the Chapter#
Exploratory Data Analysis (EDA) is the first step in understanding any dataset. Before modeling or hypothesis testing, analysts examine data structure, quality, distributions, and relationships to transform raw data into meaningful insight.
In this chapter, we learned how to understand data structure and variable types, summarize categorical and numerical variables, visualize distributions and detect outliers, explore relationships, assess data quality, and apply EDA to real data.
EDA is systematic and iterative; each discovery leads to new questions and deeper understanding.
Student EDA Checklist#
Use this checklist whenever you explore a new dataset.
Dataset Understanding#
☐ What does each variable mean?
☐ Units?
☐ Numeric or categorical?
Data Quality#
☐ Missing values
☐ Duplicates
☐ Impossible values
Distribution#
☐ Histograms
☐ Outliers
Relationships#
☐ Scatter plots
☐ Correlations
Insight#
☐ Surprising findings
☐ Questions for modeling