Practical Statistics for Data Scientists: 50+ Essential Concepts Using R an...

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
Book Title
Practical Statistics For Data Scientists: 50+ Essential Conc...
Subject
Statistics
ISBN
9781492072942
Category

About this product

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
149207294X
ISBN-13
9781492072942
eBay Product ID (ePID)
3038764333

Product Key Features

Number of Pages
360 Pages
Language
English
Publication Name
Practical Statistics for Data Scientists : 50+ Essential concepts Using Rand Python
Subject
Data Processing, Databases / Data Warehousing, Databases / Data Mining, Mathematical Analysis
Publication Year
2020
Type
Textbook
Subject Area
Mathematics, Computers
Author
Peter Bruce, Peter Gedeck, Andrew Bruce
Format
Trade Paperback

Dimensions

Item Height
0.8 in
Item Weight
21.9 Oz
Item Length
9.4 in
Item Width
7.3 in

Additional Product Features

Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
2018-420845
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
001.4/22
Synopsis
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 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 Number
QA276.4.B78 2020

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