MARC状态：已编 文献类型：西文图书 浏览次数：34
- 题名/责任者:
- Modern data science with R / Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton.
- 出版发行项:
- Boca Raton, FL : CRC Press, 2017.
- ISBN:
- 9781315113760
- 载体形态项:
- 1 online resource (xxvi, 551 pages) : illustrations
- 丛编统一题名:
- Texts in statistical science.
- 个人责任者:
- Baumer, Benjamin, author.
- 附加个人名称:
- Kaplan, Daniel, author.
- 附加个人名称:
- Horton, Nicholas J., author.
- 论题主题:
- Data mining.
- 论题主题:
- Big data.
- 论题主题:
- Mathematical statistics-Data processing.
- 中图法分类号:
- TP311.13
- 一般附注:
- "A Chapman & Hall book."
- 书目附注:
- Includes bibliographical references (pages 499-512) and indexes.
- 内容附注:
- I. Introduction to Data Science -- 1. Prologue: Why data science? -- 2. Data visualization -- 3. A grammar for graphics -- 4. Data wrangling -- 5. Tidy data and iteration -- 6. Professional Ethics -- II. Statistics and Modeling -- 7. Statistical foundations -- 8. Statistical learning and predictive analytics -- 9. Unsupervised learning -- 10. Simulation -- III. Topics in Data Science -- 11. Interactive data graphics -- 12. Database querying using SQL -- 13. Database administration -- 14. Working with spatial data -- 15. Text as data -- 16. Network science -- 17. Epilogue: Towards "big data" -- IV. Appendices -- A. Packages used in this book -- B. Introduction to R and RStudio -- C. Algorithmic thinking -- D. Reproducible analysis and workflow -- E. Regression modeling -- F. Setting up a database server.
- 摘要附注:
- Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions. Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses. --
- 随书光盘:
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