This is a modern textbook in statistical inference, using the principles of data science through R and the Tidyverse. It assumes minimal background knowledge of the reader: there is no algebra, no calculus, and no prior programming/coding experience.

¿ Chester Ismay is a Data Science Evangelist for DataRobot and is based in Portland, Oregon, USA.

¿Albert Y. Kim is an Assistant Professor of Statistical and Data Sciences at Smith College in Northampton, Massachusetts, USA.

Preface
1 Getting Started with Data in R
I Data Science via the tidyverse
2 Data Visualization
3 Data Wrangling
4 Data Importing & "Tidy" Data
II Data Modeling via moderndive
5 Basic Regression
6 Multiple Regression
III Statistical Inference via infer
7 Sampling
8 Bootstrapping & Confidence Intervals
9 Hypothesis Testing
10 Inference for Regression
11 Tell the Story with Data
Appendix
A Statistical Background
B Information about R packages Used
Bibliography
Index