机读格式显示(MARC)

• 000 03264cam a2200385 i 4500

• 001 0000606806

• 005 20220330095500.0

• 008 161004s2017 flua ob 001 0 eng

• 010 __ |a 2016042514

• 020 __ |a 9781315113760 |q (ebook)

• 020 __ |z 9781498724487 |q (print)

• 040 __ |a DLC |b eng |e rda |c DLC |d BTCTA |d YDXCP |d OCLCO |d OCLCF |d MNG |d YDX |d OCLCO

• 042 __ |a pcc

• 050 00 |a QA76.9.D343 |b B38 2017

• 082 00 |a 006.3/12 |2 23

• 093 __ |a TP311.13 |2 5

• 099 __ |a CAL 020017079459

• 100 1_ |a Baumer, Benjamin, |e author.

• 245 10 |a Modern data science with R / |c Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton.

• 260 __ |a Boca Raton, FL : |b CRC Press, |c 2017.

• 300 __ |a 1 online resource (xxvi, 551 pages) : |b illustrations

• 490 1_ |a Chapman & Hall/CRC texts in statistical science series

• 500 __ |a "A Chapman & Hall book."

• 504 __ |a Includes bibliographical references (pages 499-512) and indexes.

• 505 0_ |a 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.

• 520 __ |a 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. -- |c back cover.

• 588 __ |a Description based on print version record.

• 650 _0 |a Data mining.

• 650 _0 |a Big data.

• 650 _0 |a Mathematical statistics |x Data processing.

• 650 _0 |a R (Computer program language)

• 700 1_ |a Kaplan, Daniel, |e author.

• 700 1_ |a Horton, Nicholas J., |e author.

• 830 _0 |a Texts in statistical science.

• 856 4_ |u http://www.itextbook.cn/f/book/bookDetail?bookId=52c9d469054a48b5af046f7a8aff301b |z An electronic book accessible through the World Wide Web; click to view