Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning.
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Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University. He has held visiting faculty appointments at Harvard, UC Berkeley, and Imperial College London. Efron has worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. He received the National Medal of Science in 2005, the Guy Medal in Gold of the Royal Statistical Society in 2014, and the International Prize in Statistics in 2019.
Trevor Hastie is John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University. He is coauthor of The Elements of Statistical Learning (2009), a key text in the field of modern data analysis. He is also known for his work on generalized additive models, and for his contributions to the R computing environment. Hastie was elected to the National Academy of Sciences in 2018, received the Sigillum Magnum from the University of Bologna in 2019, and the Leo Breiman award from the American Statistical Association in 2020.
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