Oreilly.Bad.Data.Handbook.Nov.2012.pdf
大小:(10.53 MB)

只需: 5 个论坛币  马上下载

热辣出炉，亚马逊2012年11月开始销售，最新新书上架，本论坛首发。

做数据处理的童鞋必须要读的一本书 。 仅象征性的收费用于购买本论坛其它收费资料。

大家都知道：数据没有干净的(包含各种错误)，也可以说：数据都是脏的(Dirty Data)，甚至是坏的（Bad Data），那么坏的是否就没有价值呢！

错！大错特错！

坏的数据照样有他们巨大的内在价值，就看你去如何利用了。

本书的目的就是描述如何从：“令人讨厌的数据问题中找到数据的利用价值”。

Bad Data Handbook

Bad Data Handbook
O’R-lly Media (November 2012) | ISBN: 1449321887 | PDF + EPUB | 264 pages | 13.9 MB

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

Table of Contents

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