Identifying Data Quality Issues

Summary

The first, often-skipped step — profiling a new dataset for missing values, inconsistent formats, duplicates and impossible values before trusting a single number from it.

• 1Run a basic profile (row counts, null counts, unique value counts) on any dataset before analyzing it.
• 2Build a personal checklist of the top 5 issues you always check for first.
• 3Practice on a deliberately messy public dataset to build pattern recognition.