整理数据时总会遇到一些坏数据，不想“垃圾进垃圾出”，就要面临着坏数据处理问题。
本书作者Q. Ethan McCallum联合19位同行给出他们工作中坏数据处理的经验。
目录：
Chapter 1 Setting the Pace: What Is Bad Data?
Chapter 2 Is It Just Me, or Does This Data Smell Funny?
Chapter 3 Data Intended for Human Consumption, Not Machine Consumption
Chapter 4 Bad Data Lurking in Plain Text
Chapter 5 (Re)Organizing the Web’s Data
Chapter 6 Detecting Liars and the Confused in Contradictory Online Reviews
Chapter 7 Will the Bad Data Please Stand Up?
Chapter 8 Blood, Sweat, and Urine
Chapter 9 When Data and Reality Don’t Match
Chapter 10 Subtle Sources of Bias and Error
Chapter 11 Don’t Let the Perfect Be the Enemy of the Good: Is Bad Data Really Bad?
Chapter 12 When Databases Attack: A Guide for When to Stick to Files
Chapter 13 Crouching Table, Hidden Network
Chapter 14 Myths of Cloud Computing
Chapter 15 The Dark Side of Data Science
Chapter 16 How to Feed and Care for Your Machine-Learning Experts
Chapter 17 Data Traceability
Chapter 18 Social Media: Erasable Ink?
Chapter 19 Data Quality Analysis Demystified: Knowing When Your Data Is Good Enough

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这本书还不错，推荐给大家。