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Practical Statistics for Data Scientists : 50 Essential Concepts by Peter Bruce and Andrew Bruce (2017, Trade Paperback)
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2018 O'Reilly. PRACTICAL STATISTICS FOR DATA SCIENTISTS. Peter Bruce & Andrew Bruce. 50 Essential Concepts.
- Buy it nowPRACTICAL STATISTICS FOR DATA SCIENTISTS 50 Essential Concepts Bruce 2018 NEW
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101491952962
ISBN-139781491952962
eBay Product ID (ePID)219384832
Product Key Features
Number of Pages315 Pages
Publication NamePractical Statistics for Data Scientists : 50 Essential concepts
LanguageEnglish
Publication Year2017
SubjectProbability & Statistics / General, Databases / Data Warehousing, Data Processing, Databases / General
TypeTextbook
Subject AreaMathematics, Computers
AuthorPeter Bruce, Andrew Bruce
FormatTrade Paperback
Dimensions
Item Height0.7 in
Item Weight19.5 Oz
Item Length9.2 in
Item Width7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2017-302988
Dewey Edition23
IllustratedYes
Dewey Decimal001.4/22
SynopsisStatistical 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 NumberHA29
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Most relevant reviews
- 22 May, 2019
for data science
Useful for data science. Presents information about machine learning.Verified purchase: YesCondition: New
