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Practical Statistics for Data Scientists: 50 Essential Concepts  Bruce, Peter  G
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Practical Statistics for Data Scientists: 50 Essential Concepts Bruce, Peter G
US $17.22US $17.22
Fri, May 16, 05:21 PMFri, May 16, 05:21 PM
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Practical Statistics for Data Scientists: 50 Essential Concepts Bruce, Peter G

US $17.22
ApproximatelyC $23.69
Condition:
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    Located in: Colorado Springs, Colorado, United States
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    Item specifics

    Condition
    Good: A book that has been read but is in good condition. Very minimal damage to the cover including ...
    ISBN
    9781491952962

    About this product

    Product Identifiers

    Publisher
    O'reilly Media, Incorporated
    ISBN-10
    1491952962
    ISBN-13
    9781491952962
    eBay Product ID (ePID)
    219384832

    Product Key Features

    Number of Pages
    315 Pages
    Publication Name
    Practical Statistics for Data Scientists : 50 Essential concepts
    Language
    English
    Publication Year
    2017
    Subject
    Probability & Statistics / General, Databases / Data Warehousing, Data Processing, Databases / General
    Type
    Textbook
    Subject Area
    Mathematics, Computers
    Author
    Peter Bruce, Andrew Bruce
    Format
    Trade Paperback

    Dimensions

    Item Height
    0.7 in
    Item Weight
    19.5 Oz
    Item Length
    9.2 in
    Item Width
    7 in

    Additional Product Features

    Intended Audience
    Scholarly & Professional
    LCCN
    2017-302988
    Dewey Edition
    23
    Illustrated
    Yes
    Dewey Decimal
    001.4/22
    Synopsis
    Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data
    LC Classification Number
    HA29

    Item description from the seller

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        The book arrived quickly, and the tracking information provided was accurate throughout the shipping process. It was in excellent condition, just as described in the listing. While the packaging was simple, it did its job and protected the book well. Overall, a smooth and positive buying experience. I would definitely recommend this seller!
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