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EDA Best Practices
Methodologies for conducting thorough exploratory data analysis.
6-Step EDA Framework
1. Initial Understanding
Questions:
- What does each column represent?
- What is the unit of observation and time period?
- What is the data collection methodology?
- Are there known quality issues?
Actions: Load data, inspect structure, review types, document context
2. Quality Assessment
Check: Missing data patterns, duplicates, outliers, consistency, accuracy
Red Flags:
- Missing data >20%
- Unexpected duplicates
- Constant columns
- Impossible values (negative ages, future dates)
- Suspicious patterns (too many round numbers)
3. Univariate Analysis
Numeric: Central tendency, dispersion, shape (skewness, kurtosis), distribution plots, outliers
Categorical: Frequency distributions, unique counts, balance, bar charts
Temporal: Time range, gaps, trends, seasonality, time series plots
4. Bivariate Analysis
Numeric vs Numeric: Scatter plots, correlations (Pearson, Spearman), detect non-linearity
Numeric vs Categorical: Group statistics, box plots by category, t-test/ANOVA
Categorical vs Categorical: Cross-tabs, stacked bars, chi-square, Cramér's V
5. Multivariate Analysis
Techniques: Correlation matrices, pair plots, parallel coordinates, PCA, clustering
Questions: Groups of correlated features? Reduce dimensionality? Natural clusters? Conditional patterns?
6. Insight Generation
Look for: Unexpected patterns, strong correlations, quality issues, feature engineering opportunities, domain implications
Visualization Guidelines
Chart Selection:
- Distribution: Histogram, KDE, box/violin plots
- Relationships: Scatter, line, heatmap
- Composition: Stacked bar
- Comparison: Bar, grouped bar
Best Practices: Label axes with units, descriptive titles, purposeful color, appropriate scales, avoid clutter
Statistical Analysis Guidelines
Check Assumptions: Normality, homoscedasticity, independence, linearity
Method Selection: Parametric when assumptions met, non-parametric otherwise, report effect sizes
Context Matters: Statistical ≠ practical significance, domain knowledge trumps statistics, correlation ≠ causation
Documentation Guidelines
Notes: Document assumptions, decisions, issues, findings
Reproducibility: Use scripts, version control, document sources, set random seeds
Reporting: Clear summaries, supporting visualizations, highlighted insights, actionable recommendations
Common Pitfalls
- Confirmation Bias: Seek disconfirming evidence, use blind analysis
- Ignoring Quality: Address issues first, document limitations
- Over-automation: Manually inspect subsets, verify results
- Neglecting Outliers: Investigate before removing - may be informative
- Multiple Testing: Use correction (Bonferroni, FDR) or note exploratory nature
- Association ≠ Causation: Use careful language, acknowledge alternatives
- Cherry-picking: Report complete analysis, including negative results
- Ignoring Sample Size: Report effect sizes, CIs, and sample sizes
Domain-Specific Considerations
Time Series: Check stationarity, identify trends/seasonality, autocorrelation, temporal splits
High-Dimensional: Dimensionality reduction, feature importance, regularization, domain-guided selection
Imbalanced: Report distributions, appropriate metrics, resampling, stratified CV
Small Samples: Non-parametric methods, conservative conclusions, CIs, Bayesian approaches
Big Data: Intelligent sampling, efficient structures, parallel computing, scalability
Iterative Process
EDA is iterative: Explore → Questions → Focused Analysis → Insights → New Questions → Deeper Investigation → Synthesis
Done When: Understand structure/quality, characterized variables, identified relationships, documented limitations, answered questions, have actionable insights
Deliverables: Data understanding, quality issue list, relationship insights, hypotheses, feature ideas, recommendations
Communication
Technical Audiences: Methodological details, statistical tests, assumptions, reproducible code
Non-Technical Audiences: Focus on insights, clear visualizations, avoid jargon, concrete recommendations
Report Structure: Executive summary → Data overview → Analysis → Insights → Recommendations → Appendix
Checklists
Before: Understand context, define objectives, identify audience, set up environment
During: Inspect structure, assess quality, analyze distributions, explore relationships, document continuously
After: Verify findings, check alternatives, document limitations, prepare visualizations, ensure reproducibility