Bad Data Handbook

Q. Ethan Mccallum

ISBN 10: 1449321887 / ISBN 13: 9781449321888

Published by O'Reilly Media, US, 2012

About this Item

Welcome to data science's dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing software developer, writing code to clean up data before they can actually do anything constructive with it. It's a necessary evil, but you can still make the most of it. This practical book walks you through several real-world examples to demonstrate the theory and practice behind working with and cleaning up dirty data. No one tool solves all of the problems well. Wise data scientists learn many tools and learn where each one shines. To that end, this book takes a polyglot approach: most examples will involve R and Python, but expect the occasional smattering of Groovy and sed/awk fun.

Seller Inventory # LU-9781449321888

Bibliographic Details

• Title: Bad Data Handbook
• Publisher: O'Reilly Media, US
• Publication Date: 2012
• Language: English
• ISBN 10: 1449321887
• ISBN 13: 9781449321888
• Binding: Paperback
• Edition: Illustrated.
• Condition: New
• Dimensions: 17.78W x 1.45H x 23.34D centimeters 17.78 centimeters width by 1.45 centimeters height by 23.34 centimeters depth

About this title

Synopsis

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

About the Author

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.