Benjamin S. Baumer - Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

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Library of Congress CataloginginPublication Data

ISBN: 9780367191498 (hbk)

ISBN: 9780367745448 (pbk)

ISBN: 9780429200717 (ebk)

Typeset in Latin Modern font

by KnowledgeWorks Global Ltd.

Benjamin S. Baumer is an associate 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 Analyzing Baseball Data with R. He received the 2019 Waller Education Award and 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. Danny received the 2006 Macalester Excellence in Teaching award and the 2017 CAUSE Lifetime Achievement Award.

Nicholas J. Horton is the Beitzel Professor of Technology and Society (Statistics and Data Science) at Amherst College. He is a Fellow of the ASA and the AAAS, co-chair of the National Academies 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 data science curriculum efforts to help students think with data.

Background and motivation

The increasing volume and sophistication of data poses new challenges for analysts, who need to be able to transform complex data sets to answer important statistical questions. A consensus report on data science for undergraduates (National Academies of Science, Engineering, and Medicine, 2018) noted that data science is revolutionizing science and the workplace. They defined a data scientist as a knowledge worker who is principally occupied with analyzing complex and massive data resources.

Michael I. Jordan has described data science as the marriage of computational thinking and inferential (statistical) thinking. Without the skills to be able to wrangle or marshal the increasingly rich and complex data that surround us, analysts will not be able to use these data to make better decisions.

Demand is strong for graduates with these skills. According to the company ratings site Glassdoor, data scientist was the best job in America every year from 20162019 (Columbus, 2019).

New data technologies make it possible to extract data from more sources than ever before. Streamlined data processing libraries enable data scientists to express how to restructure those data into a form suitable for analysis. Database systems facilitate the storage and retrieval of ever-larger collections of data. State-of-the-art workflow tools foster well-documented and reproducible analysis. Modern statistical and machine learning methods allow the analyst to fit and assess models as well as to undertake supervised or unsupervised learning to glean information about the underlying real-world phenomena. Contemporary data science requires tight integration of these statistical, computing, data-related, and communication skills.

Intended audience

This book is intended for readers who want to develop the appropriate skills to tackle complex data science projects and think with data (as coined by Diane Lambert of Google). The desire to solve problems using data is at the heart of our approach.

We acknowledge that it is impossible to cover all these topics in any level of detail within a single book: Many of the chapters could productively form the basis for a course or series of courses. Instead, our goal is to lay a foundation for analysis of real-world data and to ensure that analysts see the power of statistics and data analysis. After reading this book, readers will have greatly expanded their skill set for working with these data, and should have a newfound confidence about their ability to learn new technologies on-the-fly.