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 Title Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
 Author(s) Bradley Efron and Trevor Hastie
 Publisher: Cambridge University Press; 1 edition (July 21, 2016); eBook (Stanford University 2016)
 Hardcover 495 pages
 eBook PDF (493 pages)
 Language: English
 ISBN10: 1107149894
 ISBN13: 9781107149892
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Book Description
A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twentyfirst century. Every aspiring data scientist should carefully study this book, use it as a reference, and carry it with them everywhere.
This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories  Bayesian, frequentist, Fisherian  individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more.
The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
About the Authors Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California.
 Trevor Hastie is John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He is coauthor of Elements of Statistical Learning, a key text in the field of modern data analysis.
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