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.