Data cleaning, also known as data cleansing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This essential step in data analysis ensures the quality and reliability of data, making it suitable for informed decision-making. Learn various data cleansing techniques and processes to improve data quality, reduce errors, and enhance business insights.

The blog post explains the importance of data cleaning for ensuring data quality. It covers common data issues like duplicates and missing values, and describes techniques such as removing duplicates, handling missing data, and standardizing formats. The post also outlines a step-by-step data cleaning process and shares best practices for maintaining high data standards.