"Clean Data: Practical Techniques for Data Scientists" focuses on methods to transform raw data into a usable, high-quality format. It covers essential techniques such as handling missing values, removing duplicates, detecting outliers, and dealing with inconsistent data. Data preprocessing strategies like normalization, encoding categorical variables, and feature scaling are also explored. The guide emphasizes the importance of data exploration, understanding data types, and the application of tools like Python libraries (Pandas, NumPy) for efficient cleaning. It provides step-by-step instructions, ensuring that data scientists can handle real-world datasets effectively for analysis, modeling, and decision-making.
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