Data Normalization

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May 1, 2024 Updated May 11, 2025 22 minute read

Data normalization is a fundamental process in data preparation, particularly vital in the realms of data analysis, data science, and machine learning. At its core, data normalization involves transforming the values of numerical columns in a dataset to a common scale, without distorting differences in the ranges of values or losing information. This rescaling ensures that all features contribute more equally to the analysis or model training, preventing features with larger values from disproportionately influencing outcomes.

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The Data Warehouse Toolkit

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This classic work on dimensional modeling highlights the importance of data normalization for effective data warehousing and business intelligence systems.

Advanced Analytics with Spark

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Covers data normalization techniques for large-scale data processing with Apache Spark, highlighting performance optimization and data quality considerations.

Practical Data Science with Python

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Covers data normalization as part of the data preparation process, focusing on real-world challenges and best practices for data cleaning and transformation.

Python Machine Learning

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Discusses data normalization as a critical step in machine learning pipelines, emphasizing the importance of data quality for model performance.

The Truthful Art

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Discusses data normalization as a necessary step in statistical data analysis, emphasizing the importance of preparing data for statistical inference and modeling.

Data Quality

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Includes a section on data normalization as part of data quality best practices, emphasizing the importance of accurate and consistent data for decision-making.

Data Science for Business

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Briefly covers data normalization as part of the data preprocessing phase for data science projects, highlighting its impact on data analysis and modeling.

Relational Database Design Clearly Explained

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This beginner-friendly book provides a step-by-step approach to relational database design, including normalization techniques for data integrity and consistency.

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Data Normalization

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