Practical statistics for data scientists : 50+ essential concepts using R and Python / Peter Bruce, Andrew Bruce, and Peter Gedeck.Publisher: Sebastopol, CA : O'Reilly Media, Inc., 2020Copyright date: ©2020Edition: Second editionDescription: xvi, 342 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781492072942; 149207294XSubject(s): Mathematical analysis -- Statistical methods | Quantitative research -- Statistical methods | R (Computer program language) | Statistics -- Data processing | R (Computer program language) | Statistics -- Data processingDDC classification: 001.4/22 LOC classification: QA276.4 | .B78 2020
|Item type||Current library||Call number||Copy number||Status||Date due||Barcode||Item holds|
|BOOK||NCAR Library Mesa Lab||QA276.4 .B78 2020||1||Available||50583020009274|
Includes bibliographical references and index.
Exploratory Data Analysis -- Data and Sampling Distributions -- Statistical Experiments and Significance Testing -- Regression and Prediction -- Classification -- Statistical Machine Learning -- Unsupervised Learning.
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.