This detailed book covers the concepts of data cleaning in eight chapters, including “Outlier Detection,” “Data Deduplication,” “Data Transformation,” “Data Quality Rule Definition and Discovery,” “Rule-Based Data Cleaning,” and “Machine Learning and Probabilistic Data Cleaning.” A thorough set of references and an index are also provided.

Note that there are some typographical errors, particularly in the early chapters. Some of the errors include a failure to define terms, incorrect terms in formulas, errors in values in comparisons, logical errors in expressions, range definitions in bins, an error in a range expression that does not agree with the example, and misspelled words in table references. Although there are not many errors compared to the scope of the book, the material should be edited and corrected.

The book provides a comprehensive and detailed look at the different topics covered. It is generally well written and readable, but individuals who are not conversant with the concepts presented will be challenged by some of the material. I would recommend it to more advanced readers with the caveat that there are some typographical and logical errors in the first couple chapters.