Computational Statistics in Data Science - by Walter W Piegorsch & Richard A Levine & Hao Helen Zhang & Thomas C M Lee (Hardcover)

About the Book

"The Wiley Handbook of Statistical Computing will provide a compendium of concepts, theories, techniques, and practices in statistical computing for users desiring a single, standalone sourcebook for this important area of contemporary data science. The Handbook will contain a series of sub-sections, each devoted to a central arena of statistical computing within which external authors -- including the co-Editors -- offer modern presentations on specific topical areas. Finalized entries in the new Handbook will also be available in Wiley StatsRef: Statistics Reference Online compendium, helping to build the source base for Wiley StatsRef while also providing authors with the opportunity to showcase their work in multiple locations for maximal impact"--

Book Synopsis

Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft

In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden.

Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser:
* Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen
* Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning

Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen.

From the Back Cover

An essential roadmap to the application of computational statistics in contemporary data science

In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. Computational Statistics in Data Science reproduces finalized entries from the Wiley StatsRef: Statistics Reference Online compendium, collected and edited into a valuable standalone collection. Readers will also find:

• A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas
• Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning

Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

About the Author

WALTER W. PIEGORSCH is Professor of Mathematics at the University of Arizona and Director of Statistical Research & Education at the University's BIO5 Institute. He is also a former Chair of the UArizona Interdisciplinary Program in Statistics, and a past editor of the Journal of the American Statistical Association (Theory & Methods Section). He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute.

RICHARD A. LEVINE is Professor of Statistics at San Diego State University and Faculty Advisor overseeing the Statistical Modeling Group in SDSU Analytic Studies and Institutional Research. He is former Chair of the SDSU Department of Mathematics and Statistics and past Editor of the Journal of Computational and Graphical Statistics. He is Associate Editor for Statistics of the Notices of the American Mathematical Society and is a fellow of the American Statistical Association.

HAO HELEN ZHANG is Professor of Mathematics at the University of Arizona and Chair of the UArizona Interdisciplinary Program in Statistics. She is Editor-in-Chief of STAT (the ISI journal) and Associate Editor of the Journal of the American Statistical Association and the Journal of the Royal Statistical Society. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute.

THOMAS C. M. LEE is Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis. He is a former Chair of the Department of Statistics at the same institution and a past editor of the Journal of Computational and Graphical Statistics. He is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.