Da Data Cleaning — Free Data Analytics Tutorial
Learn Da Data Cleaning in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Da Data Cleaning in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Da Data Cleaning 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.
Da Data Cleaning — Syntax
# Data cleaning checklist: # 1. df.isnull().sum() → count missing per column # 2. df.duplicated().sum() → count duplicate rows # 3. df.dtypes → check data types # 4. df["age"].describe() → check value ranges # 5. df["gender"].value_counts() → check categories
Learn Da Data Cleaning step by step with Syllab's free interactive Data Analytics tutorial — runnable code examples, practice exercises and instant AI feedback, all free with no signup. Explore the full Data Analytics course →