50+ essential concepts using R and Python Bruce, Peter C. 1953- creator author. Bruce, Andrew 1958- author. Gedeck, Peter author. text bibliography cau 2020 2020 Second edition. monographic eng

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xvi, 342 pages : illustrations ; 24 cm Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.-- Exploratory Data Analysis -- Data and Sampling Distributions -- Statistical Experiments and Significance Testing -- Regression and Prediction -- Classification -- Statistical Machine Learning -- Unsupervised Learning. adult Peter Bruce, Andrew Bruce, and Peter Gedeck. Includes bibliographical references (pages 327-328) and index. Mathematical analysis Statistical methods Quantitative research Statistical methods R (Computer program language) Python (Computer program language) Statistics Data processing Python (Computer program language) R (Computer program language) Statistics Data processing QA276.4 .B78 2020 001.4/22 9781492072942 149207294X 2018420845 UTV 210909 20260113150700.0 22227145 eng