Bad Data Handbook
ISBN-13:
9781449321888
ISBN-10:
1449321887
Edition:
1
Author:
Q. McCallum
Publication date:
2012
Publisher:
O'Reilly Media
Format:
Paperback
245 pages
Category:
Data Modeling & Design
,
Databases & Big Data
,
Data Mining
,
Data Warehousing
,
Data Processing
,
Databases
,
Software
FREE US shipping
Book details
ISBN-13:
9781449321888
ISBN-10:
1449321887
Edition:
1
Author:
Q. McCallum
Publication date:
2012
Publisher:
O'Reilly Media
Format:
Paperback
245 pages
Category:
Data Modeling & Design
,
Databases & Big Data
,
Data Mining
,
Data Warehousing
,
Data Processing
,
Databases
,
Software
Summary
Bad Data Handbook (ISBN-13: 9781449321888 and ISBN-10: 1449321887), written by authors
Q. McCallum, was published by O'Reilly Media in 2012.
With an overall rating of 4.1 stars, it's a notable title among other
Data Modeling & Design
(Databases & Big Data, Data Mining, Data Warehousing, Data Processing, Databases, Software) books. You can easily purchase or rent Bad Data Handbook (Paperback) from BooksRun,
along with many other new and used
Data Modeling & Design
books
and textbooks.
And, if you're looking to sell your copy, our current buyback offer is $3.3.
Description
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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