Data science for mathematicians / Nathan Carter, ed.
| Other author | Carter, Nathan C. |
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Boca Raton, FL : CRC Press, 2020. |
| Description | volumes cm |
| Supplemental Content | Full text available from Taylor & Francis eBooks |
| Supplemental Content | Full text available from Ebook Central - Academic Complete |
| Subjects |
| Contents | Programming with data / Sean Raleigh -- Linear algebra / Jeffery Leader -- Basic statistics / David White -- Clustering / Amy S. Wagaman -- Operations research / Alice Paul and Susan Martonosi -- Dimensionality reduction / Sofya Chepushtanova, Elin Farnell, Eric Kehoe, Michael Kirby, and Henry Kvinge -- Machine learning / Mahesh Agarwal, Nathan Carter, and David Oury -- Deep learning / Samuel S. Watson -- Topological data analysis / Henry Adams, Johnathan Bush, Joshua Mirth. |
| Abstract | "Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science"-- Provided by publisher. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2020011719 |
| ISBN | 9780367027056 (hardback) |
| ISBN | 9780367528492 (paperback) |
| ISBN | (ebook) |