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<p><b>WALTER W. PIEGORSCH</b> 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 <i>Journal of the American Statistical Association</i> (Theory & Methods Section). He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. <p><b>RICHARD A. LEVINE</b> 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<i> Journal of Computational and Graphical Statistics</i>. He is Associate Editor for Statistics of the <i>Notices of the American Mathematical Society</i> and is a fellow of the American Statistical Association. <p><b>HAO HELEN ZHANG</b> is Professor of Mathematics at the University of Arizona and Chair of the UArizona Interdisciplinary Program in Statistics. She is Editor-in-Chief of <i>STAT</i> (the ISI journal) and Associate Editor of the <i>Journal of the American Statistical Association</i> and the <i>Journal of the Royal Statistical Society</i>. She is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. <p><b>THOMAS C. M. LEE</b> 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 <i>Journal of Computational and Graphical Statistics</i>. He is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.

<p><b>An essential roadmap to the application of computational statistics in contemporary data science</b></p> <p>In <i>Computational Statistics in Data Science</i>, 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. <i>Computational Statistics in Data Science</i> reproduces finalized entries from the Wiley StatsRef: Statistics Reference Online compendium, collected and edited into a valuable standalone collection. Readers will also find: <ul><li>A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas </li> <li>Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning</li></ul> <p>Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, <i>Computational Statistics in Data Science</i> will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

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