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The most crucial step in the data science process is data preparation and cleaning. This phase involves gathering, cleaning, and transforming raw data into a usable format. It includes handling missing values, removing duplicates, and ensuring consistency across datasets. Proper data preparation is essential because the quality and structure of the data directly impact the accuracy and effectiveness of subsequent analyses and models. Without clean, well-organized data, even the most sophisticated algorithms will produce unreliable results.

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