Modern Data Science with R

Benjamin S. Baumer is an assistant professor in the Statistical & Data Sciences program at Smith College. He has been a practicing data scientist since 2004, when he became the first full-time statistical analyst for the New York Mets. Ben is a co-author of The Sabermetric Revolution and won the 2016 Contemporary Baseball Analysis Award from the Society for American Baseball Research. Daniel T. Kaplan is the DeWitt Wallace professor of mathematics and computer science at Macalester College. He is the author of several textbooks on statistical modeling and statistical computing, and received the 2006 Macalester Excellence in Teaching award. Nicholas J. Horton is a professor of statistics at Amherst College. He is a Fellow of the American Statistical Association (ASA), member of the NRC Committee on Applied and Theoretical Statistics, recipient of a number of national teaching awards, author of a series of books on statistical computing, and actively involved in curricular reform to help students "think with data."

This site includes additional resources:
http://mdsr-book.github.io/


Introduction to Data Science


Prologue: Why data science?


Data visualization


A grammar for graphics


Data wrangling


Tidy data and iteration


Professional Ethics


Statistics and Modeling


Statistical foundations


Statistical learning and predictive analytics


Unsupervised learning


Simulation


Topics in Data Science


Interactive data graphics


Database querying using SQL


Database administration


Working with spatial data


Text as data


Network science


Epilogue: Towards /big data"


Appendices


Packages used in this book


Introduction to R and RStudio


Algorithmic thinking


Reproducible analysis and workflow


Regression modeling


Setting up a database server