Q. Ethan Mccallum

Bad Data Handbook (eBook, ePUB)

Cleaning Up The Data So You Can Get Back To Work

• Format: ePub

Produktbeschreibung

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 theyve 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, youll discover how to:Test drive your data to see if its ready for analysisWork spreadsheet data into a usable formHandle encoding problems that lurk in text dataDevelop a successful web-scraping effortUse NLP tools to reveal the real sentiment of online reviewsAddress cloud computing issues that can impact your analysis effortAvoid policies that create data analysis roadblocksTake a systematic approach to data quality analysis

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 264
• Erscheinungstermin: 7. November 2012
• Englisch
• ISBN-13: 9781449324971
• Artikelnr.: 52992243

Autorenporträt

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.

Inhaltsangabe

* About the Authors
* Preface
* 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
* Colophon