Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

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From the Publisher

From the Preface

This guide is aimed on the information scientist with some familiarity with the R and/or Python programming languages, and with some prior (maybe spotty or ephemeral) publicity to statistics. Two of the authors got here to the world of knowledge science from the world of statistics, and have some appreciation of the contribution that statistics could make to the artwork of knowledge science. At the identical time, we’re nicely conscious of the restrictions of conventional statistics instruction: statistics as a self-discipline is a century and a half previous, and most statistics textbooks and programs are laden with the momentum and inertia of an ocean liner. All the strategies on this guide have some connection—historic or methodological—to the self-discipline of statistics. Methods that advanced primarily out of laptop science, reminiscent of neural nets, usually are not included.

In all circumstances, this guide provides code examples first in R and then in Python. In order to keep away from pointless repetition, we usually present solely output and plots created by the R code. We additionally skip the code required to load the required packages and information units. You can discover the whole code in addition to the info units for obtain at GitHub.

Two objectives underlie this guide:

To lay out, in digestible, navigable, and simply referenced type, key ideas from statistics which are related to information science. To clarify which ideas are necessary and helpful from a knowledge science perspective, that are much less so, and why.

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Publisher‏:‎O’Reilly Media; 2nd version (June 2, 2020)
Language‏:‎English
Paperback‏:‎368 pages
ISBN-10‏:‎149207294X
ISBN-13‏:‎978-1492072942
Item Weight‏:‎1.3 kilos
Dimensions‏:‎7 x 0.9 x 9.1 inches