`Data Wrangling is the process of cleaning, transforming, and organizing raw data into a structured format suitable for analysis. This involves identifying and correcting errors, filling in missing values, standardizing data formats, and reshaping data sets to make them consistent and usable. Data wrangling is a crucial step in the data preparation workflow, enabling more accurate and meaningful analysis by ensuring that the data is reliable and ready for use in decision-making processes.
In a world where data is vast, messy, and often unstructured, data wrangling is a foundational step in making data analytics-ready and AI-worthy.`
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