Data Cleaning Tricks Every Analyst Should Know
In the dynamic field of data analytics, raw data is almost never in a clean or ready-to-use state. It often comes with missing values, inconsistencies, duplicate entries, and formatting issues. Before an analyst can generate meaningful insights, they must transform this chaotic data into a clean, structured format. This process — known as data cleaning — forms the foundation of any successful analysis.