Skill

Data Cleansing

Data Science, Analytics and AI/ML

Data cleansing (or data cleaning) is the process of detecting and correcting inaccurate, incomplete, duplicate, or inconsistent records within a dataset. Data analysts and engineers perform cleansing tasks such as removing duplicates, standardizing formats, and filling missing values to ensure data quality before analysis or reporting. It is a foundational step in any data pipeline, since flawed data can lead to misleading conclusions.

Open roles requiring Data Cleansing (1)

• ETL
• Data Validation
• Data Wrangling
• Data Quality Management
• Data Quality
• Data Maintenance
• Data Imputation
• Data Compilation