Data Cleaning in Python Essential Training Online Class | LinkedIn Learning, formerly Lynda.com
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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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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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