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“Data Cleaning” books focus on the principles, techniques, and tools for assessing and improving data quality so information can be reliably analyzed and modeled. Titles in this category teach readers to profile datasets; detect and fix missing values, duplicates, outliers, inconsistent formats, and schema errors; apply standardization, normalization, validation, deduplication, and imputation; and build repeatable preprocessing pipelines. They balance core concepts of data quality (accuracy, completeness, consistency, validity, timeliness) with hands-on practice in spreadsheets, SQL, Python (pandas), and R (tidyverse), often through real-world case studies. Many works also cover automation, documentation, reproducibility, data governance, and metadata, and how cleaning integrates with ETL/ELT, business intelligence, and machine learning workflows. Aimed at analysts, data scientists, engineers, and business professionals, this category equips readers with practical methods to transform messy, heterogeneous data into trustworthy, analysis-ready assets that drive sound decisions.

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