Data cleaning is the process of correcting errors and inconsistencies in a dataset to enhance its quality and reliability. It involves various steps including removing duplicates, handling missing data, correcting structural errors, and validating data. Effective data cleaning is essential for obtaining reliable insights from data.

2. What is Data Cleaning ?
It is the process of identifying and correcting errors, inconsistencies, and
inaccuracies in a dataset to improve its quality and reliability.

3. Why Data Cleaning is Important?
Messy
Missing
values
Unreliable Data
Insights

4. Steps in Data Cleaning
Data Import and Understanding
01

5. Steps in Data Cleaning
Remove Duplicates
02

6. Steps in Data Cleaning
Handle Missing Data
03

7. Steps in Data Cleaning
Correct Structural Errors
04

8. Steps in Data Cleaning
Standardization and Normalization
05

9. Steps in Data Cleaning
Handling Outliers
06

10. Steps in Data Cleaning
Filter Unnecessary Data
07

11. Steps in Data Cleaning
Handle Inconsistent Data
08

12. Steps in Data Cleaning
Data Validation and Integration Checks
09

13. Steps in Data Cleaning
Export Cleaned Data
10

14. Tools for Data Cleaning

15. QUIZ
Which of the following is NOT a common Data Cleaning Technique ?
(A) Removing duplicate records
(B) Handling missing data (D) Adding random noise to data
(C) Normalizing data