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🧹 Tired of messy customer data ruining your analysis? Here’s how to build a complete data cleaning pipeline in SQL that transforms chaos into crystal-clear insights! 💡 Example: You have customer records with mixed case names, inconsistent phone formats, duplicate emails, and missing values. Instead of manual cleanup, use SQL to automate the entire process and get analysis-ready data in minutes. Stop wasting hours on manual data cleanup. Build this pipeline once and transform any messy dataset
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Learning how to clean data in Python from fixing data types and duplicates to handling missing values and record linkage. Data cleaning is one of the most important steps in data science. Follow my journey as I learn data science step by step!! #WomenInTech #DataScience #STEM #DataCleaning
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