Exploratory Data Analysis

Guide systematic exploratory data analysis workflows — from initial profiling through distribution analysis, correlation discovery, and hypothesis generation — using statistical rigor and effective visualization.

Gold

v1.0.00 activationsData & AnalyticsTechnologyintermediate

SupaScore

84.4

Research Quality (15%)

8.4

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.5

Completeness (10%)

8.3

User Satisfaction (20%)

8.4

Decision Usefulness (15%)

8.3

Best for

▸Dataset profiling and structure assessment for machine learning projects
▸Statistical distribution analysis and outlier detection in customer behavior data
▸Correlation discovery and multicollinearity assessment in financial datasets
▸Missing value pattern analysis and treatment strategy development
▸Business hypothesis generation from sales and operational data patterns

What you'll get

●Statistical summary tables with five-number summaries, skewness metrics, and outlier percentages for each numeric column
●Correlation heatmaps with clustering and flagged multicollinearity pairs above threshold values
●Missing value pattern visualizations with MCAR/MAR classification and treatment recommendations

Not designed for ↓

×Building predictive models or machine learning algorithms
×Creating production data pipelines or ETL workflows
×Statistical hypothesis testing or causal inference analysis
×Real-time data streaming analysis

Expects

Raw or semi-processed tabular datasets with clear business context and specific analytical objectives.

Returns

Comprehensive statistical summaries, distribution visualizations, correlation matrices, outlier reports, and actionable data quality insights with recommended next steps.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

edaexploratory-data-analysisdata-profilingstatisticspandasseabornvisualizationdata-qualitydistribution-analysiscorrelationoutlier-detectionhypothesis-generation

Research Foundation: 7 sources (3 books, 3 official docs, 1 academic)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Works well with

Data Quality EngineerGold Data Visualization StorytellerGold Hypothesis Testing ExpertGold Python Data AnalystGold Statistical Analysis AdvisorPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Feature Engineering StrategistGold Causal Inference AnalystGold Bayesian Statistics ExpertPlatinum

Common Workflows

ML Pipeline Data Preparation

Complete data science workflow from initial exploration through model training

exploratory-data-analysis→Data Quality Engineer→Feature Engineering Strategist→supervised-learning-engineer

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