Handling missing data in Pandas involves detecting null values, deciding whether to drop, fill, or interpolate them, and validating results to avoid bias. Techniques like isna(), dropna(), and fillna() help maintain data quality and consistency. In a data analytics course, learners practice choosing the right strategy to preserve accuracy while keeping datasets reliable for reporting and insights.
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