Exploratory data analysis in this course means examining tabular data to understand its distributions, relationships, and overall quality before making stronger conclusions. The course emphasizes using Python to inspect data, visualize patterns, and deal with common issues like missing or duplicate rows.

You would use exploratory data analysis when you first receive a tabular dataset and need to understand what is in it before interpreting it further. In this course, that includes checking distributions, comparing variables, and spotting quality issues that could affect the analysis.

Exploratory data analysis sits near the beginning of a data workflow, after the data is loaded and before more formal analysis decisions are made. It helps you decide what needs cleaning, what patterns are worth investigating, and how the variables relate to each other.