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Bad Data Handbook
Q. McCallum
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Description for Bad Data Handbook
Paperback. 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. This is a necessary evil, but we can still make the most of it. Num Pages: 264 pages, Illustrations. BIC Classification: UNA. Category: (XV) Technical / Manuals. Dimension: 231 x 178 x 14. Weight in Grams: 442.
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 ... Read more
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 ... Read more
Product Details
Publisher
O´Reilly Media United States
Number of pages
264
Format
Paperback
Publication date
2012
Condition
New
Number of Pages
250
Place of Publication
Sebastopol, United States
ISBN
9781449321888
SKU
V9781449321888
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1
About Q. McCallum
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. ... Read more
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