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000800642 08204 $$a001.4/226$$223
000800642 1001_ $$aBruce, Peter C.,$$d1953-$$eauthor.
000800642 24510 $$aPractical statistics for data scientists :$$b50 essential concepts /$$cPeter Bruce and Andrew Bruce.
000800642 24630 $$a50 essential concepts
000800642 2463_ $$aFifty essential concepts
000800642 250__ $$aFirst edition.
000800642 264_1 $$aSebastopol, CA :$$bO'Reilly Media, Inc.,$$c[2017]
000800642 264_4 $$c©2017
000800642 300__ $$a1 online resource (298 pages) :$$billustrations
000800642 336__ $$atext$$btxt$$2rdacontent
000800642 337__ $$acomputer$$bc$$2rdamedia
000800642 338__ $$aonline resource$$bcr$$2rdacarrier
000800642 504__ $$aIncludes bibliographical references and index.
000800642 5050_ $$aExploratory data analysis -- Data and sampling distributions -- Statistical experiments and significance testing -- Regression and prediction -- Classification -- Statistical machine learning -- Unsupervised learning.
000800642 506__ $$aAccess limited to authorized users.
000800642 520__ $$a"Statistical methods are a key part of 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"--Provided by publisher.
000800642 588__ $$aDescription based on print version record.
000800642 650_0 $$aMathematical analysis$$xStatistical methods.
000800642 650_0 $$aQuantitative research$$xStatistical methods.
000800642 650_0 $$aBig data$$xMathematics.
000800642 7001_ $$aBruce, Andrew,$$d1958-$$eauthor.
000800642 77608 $$iPrint version:$$aBruce, Peter C., 1953-$$tPractical statistics for data scientists.$$bFirst edition.$$dSebastopol, CA : O'Reilly Media, Inc., [2017]$$z9781491952962$$w(OCoLC)936003166
000800642 852__ $$bacq
000800642 85280 $$bebk$$hEBSCOhost
000800642 85640 $$3eBooks on EBSCOhost$$uhttps://univsouthin.idm.oclc.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1517577$$zOnline Access
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000800642 980__ $$aEBOOK
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000800642 982__ $$aEbook
000800642 983__ $$aOnline
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