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Practical Statistics for Data Scientists by Bruce, Andrew Paperback / softback

FREE US DELIVERY | ISBN: 1491952962 | Quality Books
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Características del artículo

Estado
En muy buen estado: Libro que se ha leído y que no tiene un aspecto nuevo, pero que está en un ...
ISBN
1491952962
EAN
9781491952962
Date of Publication
2017-06-06
Release Title
Practical Statistics for Data Scientists
Artist
Bruce, Andrew
Brand
N/A
Colour
N/A
Book Title
Practical Statistics for Data Scientists

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Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1491952962
ISBN-13
9781491952962
eBay Product ID (ePID)
219384832

Product Key Features

Number of Pages
315 Pages
Publication Name
Practical Statistics for Data Scientists : 50 Essential concepts
Language
English
Publication Year
2017
Subject
Probability & Statistics / General, Databases / Data Warehousing, Data Processing, Databases / General
Type
Textbook
Subject Area
Mathematics, Computers
Author
Peter Bruce, Andrew Bruce
Format
Trade Paperback

Dimensions

Item Height
0.7 in
Item Weight
19.5 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2017-302988
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
001.4/22
Synopsis
Statistical methods are a key part 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
LC Classification Number
HA29

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