Data Quality & Validation

Summary

Checking that data is accurate, complete, and consistent before drawing conclusions from it — analysis built on bad data produces confident-sounding, wrong recommendations.

• 1Audit a sample dataset for missing values, duplicates, or inconsistent formatting.
• 2Learn to build a quick data quality checklist you run before starting any analysis.
• 3Practice flagging a data quality concern to a stakeholder before presenting findings built on it.