@datascibykashi: Think your data’s clean? Think again with this quick SQL fix! Most beginners think a dataset is ready after removing null values. But real data science starts after that. Hidden problems still exist: • Duplicate rows • Wrong data types • Outliers distorting models • Inconsistent labels (Male / male / M) • Hidden missing values (” “, ?, NA) • Skewed distributions • Data leakage Your model may look accurate… but it’s learning bad data. This is why data cleaning is the most important step in data science. 💬 Comment Booster: What do you check first? A) Missing values B) Duplicates C) Outliers D) Data types Comment A / B / C / D — I’ll reply with the best workflow. Save this before your next dataset. #creatorsearchinsights #DataScience #DataTips #DataCleaning #TechHacks

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Monday 06 April 2026 17:24:36 GMT