Define Exploration Goals
2-4 hoursIdentify key questions, target variables, and expected data quality issues.
Field context
This workflow is part of 2 niche fields
Complete guide for dataset exploration project — step-by-step workflow, tools, checklist, and expert tips to get started.
Identify key questions, target variables, and expected data quality issues.
Run summary statistics, missing value analysis, and correlation matrices.
Create distribution plots, heatmaps, and investigate outliers and anomalies.
Write EDA report with findings, data quality issues, and modeling recommendations.
Compute pairwise correlations to identify feature relationships.
Assess linear relationships between features and target variable.
Test independence between categorical variables during exploration.
Interpret statistical significance of observed relationships.
Key benchmarks for dataset exploration project.
| Step | Tool | Output |
|---|---|---|
| Summary stats | describe() | Distribution |
| Missing | isnull() | Impute plan |
| Correlations | heatmap | Feature selection |
Skipping EDA leads to models that encode data bugs — garbage in, garbage out.
Tools like ydata-profiling or pandas-profiling generate comprehensive reports in minutes.
Explore on a random sample first — full-dataset EDA on millions of rows wastes time.