Data Cleaning — Making Messy Data Usable — Free Data Analytics Tutorial
Learn Data Cleaning — Making Messy Data Usable in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Data Cleaning — Making Messy Data Usable in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on…
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Data Cleaning — Making Messy Data Usable in Data Analytics
Data cleaning (also called data wrangling or data munging) is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data. It is the most time-consuming part of data analytics — typically 60-80% of any project.
Common data quality issues: Missing values (empty cells), Duplicates (same record entered twice), Inconsistent formats ("Delhi", "delhi", "DELHI" are the same city), Outliers (extreme values that may be errors), Wrong data types (age stored as text), Typos and spelling errors.
Missing value strategies: Delete rows with missing data (if few rows affected), Fill with mean/median (for numeric data), Fill with mode (for categorical data), Forward-fill or back-fill (for time series data), Leave as-is if missing has meaning.
Data cleaning is not glamorous but it's critical — bad data leads to wrong analysis, which leads to bad decisions. "Garbage in, garbage out" is the most important rule in data analytics.
Data Cleaning — Making Messy Data Usable — Syntax
# Data cleaning checklist: # 1. Check for None/null values # 2. Remove duplicates # 3. Standardise formats (lowercase, trim spaces) # 4. Fix data types # 5. Handle outliers # 6. Validate ranges (age 0-120, marks 0-100)
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