Data Cleaning: The Most Common Mistakes

An analysis is never better than the data feeding it. Among the most common mistakes: ignoring missing values instead of explicitly deciding how to handle them, confusing null with zero, or merging datasets without checking key consistency. Data cleaning is not a side step — document every transformation you apply, so your analysis stays reproducible and verifiable.