analytics-data-analysis

Keep raw inputs unchanged, make cleaning steps explicit and deterministic, separate configuration from logic, and run the full analysis from a clean start. Record assumptions, methods, and any random seeds so another person can rerun and audit the result.

Check missingness, value ranges, uniqueness, referential integrity, and impossible values before transforming anything. Separate genuine source defects from intentional filtering, and quarantine records when a defect would invalidate the result.

State the purpose and assumptions before the first analysis cell, then organize setup, loading, validation, transformation, analysis, and conclusions into visible sections. Cells must run top to bottom without relying on stale state.

No specific libraries are mandated. The workflow uses tools already available in the workspace and explains the smallest installation or fallback when a dependency is missing, keeping paths portable across environments.

Significance tests should be paired with effect size or practical magnitude, and the underlying assumptions must be tested with a fallback when they fail. Never infer causation from association alone.