Bad Data Handbook

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"O'Reilly Media, Inc.", Nov 14, 2012 - Computers - 245 pages
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What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems.

From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it.

Among the many topics covered, you’ll discover how to:

  • Test drive your data to see if it’s ready for analysis
  • Work spreadsheet data into a usable form
  • Handle encoding problems that lurk in text data
  • Develop a successful web-scraping effort
  • Use NLP tools to reveal the real sentiment of online reviews
  • Address cloud computing issues that can impact your analysis effort
  • Avoid policies that create data analysis roadblocks
  • Take a systematic approach to data quality analysis
 

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Contents

What Is Bad Data?
1
Chapter 2 Is It Just Me or Does This Data Smell Funny?
5
Chapter 3 Data Intended for Human Consumption Not Machine Consumption
31
Chapter 4 Bad Data Lurking in Plain Text
53
Chapter 5 ReOrganizing the Webs Data
69
Chapter 6 Detecting Liars and the Confused in Contradictory Online Reviews
83
Chapter 7 Will the Bad Data Please Stand Up?
95
Chapter 8 Blood Sweat and Urine
107
A Guide for When to Stick to Files
151
Chapter 13 Crouching Table Hidden Network
163
Chapter 14 Myths of Cloud Computing
175
Chapter 15 The Dark Side of Data Science
187
Chapter 16 How to Feed and Care for Your MachineLearning Experts
195
Chapter 17 Data Traceability
205
Erasable Ink?
213
Knowing When Your Data Is Good Enough
225

Chapter 9 When Data and Reality Dont Match
119
Chapter 10 Subtle Sources of Bias and Error
129
Is Bad Data Really Bad?
143
Index
239
About the Author
246
Copyright

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About the author (2012)

Q Ethan McCallum is a consultant, writer, and technology enthusiast, though perhaps not in that order. His work has appeared online on The O Reilly Network and Java.net, and also in print publications such as C/C++ Users Journal, Doctor Dobb s Journal, and Linux Magazine. In his professional roles, he helps companies to make smart decisions about data and technology.

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