| Titre : |
Bad Data Handbook |
| Type de document : |
texte imprimé |
| Auteurs : |
Q. Ethan McCallum, Auteur ; Kevin Fink, Auteur ; Paul Murrell, Auteur ; Josh Levy, Auteur ; Adam Laiacano, Auteur ; Jacob Perkins, Auteur ; Spencer Burns, Auteur ; Richard Cotton, Auteur ; Philipp K. Janert, Auteur ; Jonathan Schwabish, Auteur ; Brett Goldstein, Auteur ; Bobby Norton, Auteur ; Steve Francia, Auteur ; Tim McNamara, Auteur ; Marck Vaisman, Auteur ; Pete Warden, Auteur ; Jud Valeski, Auteur ; Reid Draper, Auteur ; Ken Gleason, Auteur ; Mike Loukides, Editeur scientifique ; Meghan Blanchette, Editeur scientifique |
| Editeur : |
Sebastopol, CA [Etats-Unis] : O'Reilly |
| Année de publication : |
2013 |
| Importance : |
1 vol. (245 p.) |
| Présentation : |
couv. ill. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-1-4493-2188-8 |
| Prix : |
39.99 $ |
| Note générale : |
La couverture porte en plus : "Mapping the World of Data Problems". - Index. |
| Langues : |
Français (fre) |
| Catégories : |
Analyse des données
Données massives
|
| Index. décimale : |
004 Informatique - Méthodes agiles - Gestion de projets (informatique) |
| Résumé : |
"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" (4e de couverture) |
| Note de contenu : |
1. Chapter 1 Setting the Pace: What Is Bad Data?
2. Chapter 2 Is It Just Me, or Does This Data Smell Funny?
3. Chapter 3 Data Intended for Human Consumption, Not Machine Consumption
4. Chapter 4 Bad Data Lurking in Plain Text
5. Chapter 5 (Re)Organizing the Web’s Data
6. Chapter 6 Detecting Liars and the Confused in Contradictory Online
Reviews
7. Chapter 7 Will the Bad Data Please Stand Up?
8. Chapter 8 Blood, Sweat, and Urine
9. Chapter 9 When Data and Reality Don’t Match
10. Chapter 10 Subtle Sources of Bias and Error
11. Chapter 11 Don’t Let the Perfect Be the Enemy of the Good: Is Bad Data Really Bad?
12. Chapter 12 When Databases Attack: A Guide for When to Stick to Files
13. Chapter 13 Crouching Table, Hidden Network
14. Chapter 14 Myths of Cloud Computing
15. Chapter 15 The Dark Side of Data Science
16. Chapter 16 How to Feed and Care for Your Machine-Learning Experts
17. Chapter 17 Data Traceability
18. Chapter 18 Social Media: Erasable Ink?
19. Chapter 19 Data Quality Analysis Demystified: Knowing When Your Data Is Good Enough
Index
|
| Permalink : |
http://catalogue.iessid.be/index.php?lvl=notice_display&id=20523 |
Bad Data Handbook [texte imprimé] / Q. Ethan McCallum, Auteur ; Kevin Fink, Auteur ; Paul Murrell, Auteur ; Josh Levy, Auteur ; Adam Laiacano, Auteur ; Jacob Perkins, Auteur ; Spencer Burns, Auteur ; Richard Cotton, Auteur ; Philipp K. Janert, Auteur ; Jonathan Schwabish, Auteur ; Brett Goldstein, Auteur ; Bobby Norton, Auteur ; Steve Francia, Auteur ; Tim McNamara, Auteur ; Marck Vaisman, Auteur ; Pete Warden, Auteur ; Jud Valeski, Auteur ; Reid Draper, Auteur ; Ken Gleason, Auteur ; Mike Loukides, Editeur scientifique ; Meghan Blanchette, Editeur scientifique . - Sebastopol, CA (Etats-Unis) : O'Reilly, 2013 . - 1 vol. (245 p.) : couv. ill. ; 24 cm. ISBN : 978-1-4493-2188-8 : 39.99 $ La couverture porte en plus : "Mapping the World of Data Problems". - Index. Langues : Français ( fre)
| Catégories : |
Analyse des données
Données massives
|
| Index. décimale : |
004 Informatique - Méthodes agiles - Gestion de projets (informatique) |
| Résumé : |
"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" (4e de couverture) |
| Note de contenu : |
1. Chapter 1 Setting the Pace: What Is Bad Data?
2. Chapter 2 Is It Just Me, or Does This Data Smell Funny?
3. Chapter 3 Data Intended for Human Consumption, Not Machine Consumption
4. Chapter 4 Bad Data Lurking in Plain Text
5. Chapter 5 (Re)Organizing the Web’s Data
6. Chapter 6 Detecting Liars and the Confused in Contradictory Online
Reviews
7. Chapter 7 Will the Bad Data Please Stand Up?
8. Chapter 8 Blood, Sweat, and Urine
9. Chapter 9 When Data and Reality Don’t Match
10. Chapter 10 Subtle Sources of Bias and Error
11. Chapter 11 Don’t Let the Perfect Be the Enemy of the Good: Is Bad Data Really Bad?
12. Chapter 12 When Databases Attack: A Guide for When to Stick to Files
13. Chapter 13 Crouching Table, Hidden Network
14. Chapter 14 Myths of Cloud Computing
15. Chapter 15 The Dark Side of Data Science
16. Chapter 16 How to Feed and Care for Your Machine-Learning Experts
17. Chapter 17 Data Traceability
18. Chapter 18 Social Media: Erasable Ink?
19. Chapter 19 Data Quality Analysis Demystified: Knowing When Your Data Is Good Enough
Index
|
| Permalink : |
http://catalogue.iessid.be/index.php?lvl=notice_display&id=20523 |
|  |
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