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Data Cleaning – The Backbone of Data Science👇 Key Steps in Data Cleaning: 🔹 Select Choose relevant columns and rows needed for analysis 🔹 Filter Remove unwanted records, outliers, or invalid entries 🔹 Handle Missing Values Drop, fill (mean/median/mode), or interpolate missing data 🔹 Remove Duplicates Ensure each record is unique 🔹 Data Type Conversion Convert strings to numbers, dates to datetime format 🔹 Normalize / Standardize Scale data for better model performance 🔹 Rename Columns Us
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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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