Exploring Data Science with R and the Tidyverse
eBook - ePub

Exploring Data Science with R and the Tidyverse

A Concise Introduction

  1. 479 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Exploring Data Science with R and the Tidyverse

A Concise Introduction

About this book

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.

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Yes, you can access Exploring Data Science with R and the Tidyverse by Jerry Bonnell,Mitsunori Ogihara in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Dedication Page
  6. Contents
  7. Welcome
  8. 1 Data Types
  9. 2 Data Transformation
  10. 3 Data Visualization
  11. 4 Building Simulations
  12. 5 Sampling
  13. 6 Hypothesis Testing
  14. 7 Quantifying Uncertainty
  15. 8 Towards Normality
  16. 9 Regression
  17. 10 Text Analysis
  18. Index