SPRI | The Data Science Design Manual (2017 EN)

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  1. Kanka

    Kanka Well-Known Member Loyal User

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    Author: Steven S. Skiena
    Full Title: The Data Science Design Manual
    Publisher: Springer; 1st ed. 2017 edition (July 1, 2017)
    Year: 2017
    ISBN-13: 9783319554440 (978-3-319-55444-0), 9783319554433 (978-3-319-55443-3)
    ISBN-10: 3319554441, 3319554433
    Pages: 445
    Language: English
    Genre: Computer Science
    File type: PDF (True)
    Quality: 10/10
    Price: 55.11 €


    This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data.

    The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles.

    This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well.

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