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Modern data science with R Ed 2

By: Contributor(s): Material type: TextTextLanguage: English Publication details: New York CRC Press 2021Edition: 2Description: xvii,631p. HB 25x17.5ISBN:
  • 9781032941677
Subject(s): DDC classification:
  • 006.312 BAUM
Summary: 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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Holdings
Item type Current library Collection Call number Status Barcode
Book Book St Aloysius Engineering Library Computer Science and Engineering (Artificial Intelligence & Machine Learning) 006.312 BAUM (Browse shelf(Opens below)) Available SE000132
Total holds: 0

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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