May 2020
Beginner
360 pages
9h 16m
English
Content preview from Practical Statistics for Data Scientists, 2nd Edition
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Preface
This book is aimed at the data scientist with some familiarity with the R and/or Python programming languages, and with some prior (perhaps spotty or ephemeral) exposure to statistics. Two of the authors came to the world of data science from the world of statistics, and have some appreciation of the contribution that statistics can make to the art of data science. At the same time, we are well aware of the limitations of traditional statistics instruction: statistics as a discipline is a century and a half old, and most statistics textbooks and courses are laden with the momentum and inertia of an ocean liner. All the methods in this book have some connection—historical or methodological—to the discipline of statistics. Methods that evolved mainly out of computer science, such as neural nets, are not included.
Two goals underlie this book:
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To lay out, in digestible, navigable, and easily referenced form, key concepts from statistics that are relevant to data science.
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To explain which concepts are important and useful from a data science perspective, which are less so, and why.
Conventions Used in This Book
The following typographical conventions are used in this book:
- Italic
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Indicates new terms, URLs, email addresses, filenames, and file extensions.
Constant width-
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.