Data Wrangling is the process of cleaning and transforming raw data into a usable format for analysis. It involves various tasks such as removing duplicates, handling missing values, and converting data types. This step is crucial because raw data is often messy and inconsistent, making it difficult to derive meaningful insights. The goal of Data Wrangling is to prepare data for further analysis or visualization. By organizing and structuring the data properly, analysts can more easily identify patterns, trends, and relationships, ultimately leading to better decision-making and more accurate results.