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    Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - okladka książki

    Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - okladka książki

    Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - audiobook MP3

    Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools David Mertz - audiobook CD

    Autor:
    David Mertz
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    498
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way.

    In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with.

    Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.

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    O autorze książki

    David Mertz, Ph.D. is the founder of KDM Training, a partnership dedicated to educating developers and data scientists in machine learning and scientific computing. He created a data science training program for Anaconda Inc. and was a senior trainer for them. With the advent of deep neural networks, he has turned to training our robot overlords as well.



    He previously worked for 8 years with D. E. Shaw Research and was also a Director of the Python Software Foundation for 6 years. David remains co-chair of its Trademarks Committee and Scientific Python Working Group. His columns, Charming Python and XML Matters, were once the most widely read articles in the Python world.

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