Description

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.

Bad Data Handbook: Cleaning Up the Data So You Can Get Back to Work

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Paperback / softback by Q. Ethan Mccallum

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Short Description:

Welcome to data science's dirty secret: real-world data is messy. Data scientists must spend a good deal of time playing... Read more

    Publisher: O'Reilly Media
    Publication Date: 18/12/2012
    ISBN13: 9781449321888, 978-1449321888
    ISBN10: 1449321887

    Number of Pages: 250

    Non Fiction , Computing

    Description

    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.

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