Data Science in R ─ A Case Studies Approach to Computational Reasoning and Problem Solving

This book presents case studies in statistical computing for data analysis. Each case study addresses a statistical application with a focus on comparing different computational approaches and explaining the reasoning behind them. The case studies can serve as material for instructors teaching courses in statistical computing and applied statistics. The book aids readers in understanding the thought process of data analysis and how to reason about computing.

Deborah Nolan holds the Zaffaroni Family Chair in Undergraduate Education at the University of California, Berkeley. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research.

Duncan Temple Lang is the director of the Data Science Initiative at the University of California, Davis. He has been involved in the development of R and S for 20 years and has developed over 100 R packages. His research focuses on statistical computing, data technologies, meta-computing, reproducibility, and visualization.