Exploratory Data Analysis

Table of contents

• Purpose
• Workflow
• Reproducibility
• Core checks
• Reports
• Boundaries
• Related guides

Purpose

EDA characterizes the data before modeling and provides evidence for data quality, feature design, and product interpretation.

Workflow

load versioned data
validate schema
profile distributions
inspect missingness
analyze temporal behavior
check target balance
study feature relationships
record findings

Reproducibility

Notebooks should:

use deterministic inputs
record dataset version
avoid hidden manual state
export reusable code where appropriate
separate exploration from production logic

Core checks

class balance
outliers
missingness
duplicates
temporal leakage
group representation
feature stability
unexpected proxies

Reports

EDA outputs can support future dashboard validation reports, but exploratory charts must not be presented as validated causal conclusions.

Boundaries

EDA identifies patterns and hypotheses. It does not establish psychometric, causal, or fairness validity by itself.

Related guides

• Data
• Modeling
• MLOps
• Documentation index