An interview with statistician and author Daniela Witten

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By: Sacha Billett , Wed Feb 24 2021
Sacha Billet

Author: Sacha Billett

Continuing on our theme, Who’s afraid of statistics? this week we are talking to statistician and author Daniela Witten, about her research and approach to writing the accessible and well-loved statistics textbook, An Introduction to Statistical Learning.

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The first edition of "An Introduction to Statistical Learning" has proved to be one of Springer Nature's most popular textbooks, having been downloaded more than 3 million times from Springer Nature Link and cited over 2,500 times (source Crossref).


Can you introduce yourself and give an overview of your current research?


You co-authored the incredibly popular statistics textbook “An Introduction to Statistical Learning”: Why do you think the textbook is so popular? And what was the philosophy of you and your co-authors when writing it?


The 1st edition was written in 2013, what new features can readers expect in the 2nd edition?


In recent years’ statistics have gone from being a slightly scary subject to being a vital skill across all disciplines, largely due to big data and computational power. What are the most interesting developments you seen in the use of statistics? And what new trends can we expect to see in going forwards?


Coming soon!

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.

This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.


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Sacha Billet

Author: Sacha Billett

Sacha Billett is a Content Marketing Manager in the Institutional Marketing team, based in the Dordrecht office. Supporting the Sales and Account Development teams, she is enthusiastic about finding innovate ways to communicate with the library community.