About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-10149207294X

ISBN-139781492072942

eBay Product ID (ePID)3038764333

Product Key Features

Number of Pages360 Pages

LanguageEnglish

Publication NamePractical Statistics for Data Scientists : 50+ Essential concepts Using Rand Python

SubjectData Processing, Databases / Data Warehousing, Databases / Data Mining, Mathematical Analysis

Publication Year2020

TypeTextbook

Subject AreaMathematics, Computers

AuthorPeter Bruce, Peter Gedeck, Andrew Bruce

FormatTrade Paperback

Dimensions

Item Height0.8 in

Item Weight21.9 Oz

Item Length9.4 in

Item Width7.3 in

Additional Product Features

Edition Number2

Intended AudienceScholarly & Professional

LCCN2018-420845

Dewey Edition23

IllustratedYes

Dewey Decimal001.4/22

SynopsisStatistical 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 popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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, 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

LC Classification NumberQA276.4.B78 2020