Data Cleansing
Data cleansing, also known as data cleaning or data scrubbing, is the process of detecting and correcting (or removing) corrupt, inaccurate, incomplete, or irrelevant records from a dataset. It involves tasks like handling missing values, removing duplicates, correcting inconsistencies, and standardizing formats to ensure data quality and reliability. This methodology is essential for preparing raw data for analysis, machine learning, or reporting purposes.
Developers should learn data cleansing when working with data-driven applications, analytics pipelines, or machine learning projects, as dirty data can lead to incorrect insights, biased models, or system failures. It is crucial in scenarios like ETL (Extract, Transform, Load) processes, data warehousing, and real-time data processing to maintain data integrity and support accurate decision-making. For example, in a web analytics tool, cleansing user logs by removing bot traffic ensures valid metrics.