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Bad Data Handbook

Cleaning Up The Data So You Can Get Back To Work

By: Q. Ethan McCallum

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Bad Data Handbook

Cleaning Up The Data So You Can Get Back To Work

By: Q. Ethan McCallum

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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Page Count

265

Publisher

"O'Reilly Media, Inc."

Published Date

ISBN 10

1449324975

ISBN 13

9781449324971

Parallel R

Data Analysis in the Distributed World

By: Q. Ethan McCallum,Stephen Weston

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Parallel R

Data Analysis in the Distributed World

By: Q. Ethan McCallum,Stephen Weston

It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t. With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop’s power with R’s language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

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Page Count

123

Publisher

"O'Reilly Media, Inc."

Published Date

ISBN 10

1449320333

ISBN 13

9781449320331