Exploratory Data Analysis in Practice

Compute and interpret summary statistics to characterize a dataset's distribution.

Create and interpret histograms, box plots, and scatter plots using matplotlib and seaborn.

Identify potential outliers using statistical methods like the IQR rule or z-scores.

Analyze relationships between variables using pair plots and correlation heatmaps.

Formulate initial hypotheses about a dataset based on EDA findings.

Synthesize EDA findings into a concise summary of key patterns and data quality issues.