Exploring Data Science with R and the Tidyverse
A Concise Introduction
1st Edition
Published on 14. August 2023
Book
Paperback/Softback
476 pages
978-1-032-34170-5 (ISBN)
Description
This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader.
Upon completion of the text, the reader will be able to:
Gain proficiency in R programming
Load and manipulate data frames, and "tidy" them using tidyverse tools
Conduct statistical analyses and draw meaningful inferences from them
Perform modeling from numerical and textual data
Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being represented
An accompanying R package "edsdata" contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.
Upon completion of the text, the reader will be able to:
Gain proficiency in R programming
Load and manipulate data frames, and "tidy" them using tidyverse tools
Conduct statistical analyses and draw meaningful inferences from them
Perform modeling from numerical and textual data
Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being represented
An accompanying R package "edsdata" contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.
More details
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Postgraduate, Professional, and Undergraduate Advanced
Illustrations
42 s/w Abbildungen, 158 farbige Abbildungen, 42 s/w Zeichnungen, 158 farbige Zeichnungen, 8 s/w Tabellen, 1 farbige Tabelle
1 Tables, color; 8 Tables, black and white; 158 Line drawings, color; 42 Line drawings, black and white; 158 Illustrations, color; 42 Illustrations, black and white
Dimensions
Height: 254 mm
Width: 178 mm
Thickness: 26 mm
Weight
916 gr
ISBN-13
978-1-032-34170-5 (9781032341705)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jerry Bonnell | Mitsunori Ogihara
Exploring Data Science with R and the Tidyverse
A Concise Introduction
Book
08/2023
1st Edition
Chapman & Hall/CRC
€317.70
Shipment within 10-20 days
Jerry Bonnell | Mitsunori Ogihara
Exploring Data Science with R and the Tidyverse
A Concise Introduction
E-Book
08/2023
1st Edition
Chapman & Hall/CRC
€107.99
Available for download
Jerry Bonnell | Mitsunori Ogihara
Exploring Data Science with R and the Tidyverse
A Concise Introduction
E-Book
08/2023
1st Edition
Chapman & Hall/CRC
€107.99
Available for download
Persons
Jerry Bonnell is a Ph.D. candidate in Computer Science at the University of Miami, and a University of Miami Fellow. His research areas include Machine Learning and Natural Language Processing. His research targets domain experts in Digital Humanities (DH) and is shown in publications in Digital Scholarship in the Humanities, Digital Humanities Quarterly, and the ACL Special Interest Group on Language Technologies for the Socio-Economic Sciences and Humanities (LaTeCH-CLfL). He also teaches the undergraduate-level course "Data Science for the World" at the University of Miami.
Mitsunori Ogihara is a Professor of Computer Science at the University of Miami, Coral Gables, Florida, USA, and is the Director of its Master of Science in Data Science program. He received his Ph.D. in Information Sciences from Tokyo Institute of Technology, Tokyo, Japan. He is an author/co-author/co-editor of four books and has published more than 200 peer-reviewed journals and conference papers. He serves on the editorial board for a few academic journals, including Theory of Computing Systems, Springer, for which he is the Editor-in-Chief.
Mitsunori Ogihara is a Professor of Computer Science at the University of Miami, Coral Gables, Florida, USA, and is the Director of its Master of Science in Data Science program. He received his Ph.D. in Information Sciences from Tokyo Institute of Technology, Tokyo, Japan. He is an author/co-author/co-editor of four books and has published more than 200 peer-reviewed journals and conference papers. He serves on the editorial board for a few academic journals, including Theory of Computing Systems, Springer, for which he is the Editor-in-Chief.
Content
1. Data Types 2. Data Transformation 3. Data Visualization 4. Building Simulations 5. Sampling 6. Hypothesis Testing 7. Quantifying Uncertainty 8. Towards Normality 9. Regression 10. Text Analysis