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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
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
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.
- 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
| Erscheinungsjahr: | 2012 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 262 |
| Inhalt: | 250 S. |
| ISBN-13: | 9781449321888 |
| ISBN-10: | 1449321887 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | McCallum, Q. |
| Hersteller: |
O'Reilly Media
O'Reilly Media, Inc. |
| Maße: | 233 x 177 x 20 mm |
| Von/Mit: | Q. McCallum |
| Erscheinungsdatum: | 18.12.2012 |
| Gewicht: | 0,431 kg |
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.
- 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
| Erscheinungsjahr: | 2012 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 262 |
| Inhalt: | 250 S. |
| ISBN-13: | 9781449321888 |
| ISBN-10: | 1449321887 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | McCallum, Q. |
| Hersteller: |
O'Reilly Media
O'Reilly Media, Inc. |
| Maße: | 233 x 177 x 20 mm |
| Von/Mit: | Q. McCallum |
| Erscheinungsdatum: | 18.12.2012 |
| Gewicht: | 0,431 kg |
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