What is exploratory data analysis? A practical guide

This post details the importance of Exploratory Data Analysis (EDA) as a foundational step before building models or dashboards. It outlines key EDA techniques including data profiling (checking dimensions, data types, missing values), statistical summaries (distribution analysis, outlier detection, correlation analysis), and visualization (histograms, scatter plots, box plots). The post also discusses how AI assistants and collaborative tools like Hex are transforming EDA workflows by enabling faster iteration, insight generation, and real-time collaboration. It provides best practices for conducting effective EDA, such as starting with specific questions, documenting reasoning, systematic profiling, and sharing findings early.