Data Preparation and Modeling focus on transforming raw data into structured formats for analysis and machine learning. The process includes cleaning, transforming, and organizing data to improve quality and consistency. It also defines how data is structured, related, and stored for efficient processing and analytics. This training explains techniques such as data normalization, feature engineering, schema design, and data validation. These steps ensure that datasets are accurate, consistent, and ready for advanced analytics. You will learn how to build well-structured datasets and models that improve performance and decision-making. The course also highlights best practices for maintaining data quality, reusability, and consistency in analytical systems.
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