9781498724487-1498724485-Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

ISBN-13: 9781498724487
ISBN-10: 1498724485
Edition: 1
Author: Nicholas J. Horton, Benjamin S. Baumer, Daniel T. Kaplan
Publication date: 2017
Publisher: Chapman and Hall/CRC
Format: Hardcover 582 pages
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Book details

ISBN-13: 9781498724487
ISBN-10: 1498724485
Edition: 1
Author: Nicholas J. Horton, Benjamin S. Baumer, Daniel T. Kaplan
Publication date: 2017
Publisher: Chapman and Hall/CRC
Format: Hardcover 582 pages

Summary

Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science) (ISBN-13: 9781498724487 and ISBN-10: 1498724485), written by authors Nicholas J. Horton, Benjamin S. Baumer, Daniel T. Kaplan, was published by Chapman and Hall/CRC in 2017. With an overall rating of 4.1 stars, it's a notable title among other Statistics (Education & Reference) books. You can easily purchase or rent Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science) (Hardcover) from BooksRun, along with many other new and used Statistics books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.62.

Description

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions.

Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.

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