| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046025295 | |
| 005 | 20200421124632 | |
| 008 | 200420s2017 flua b 001 0 eng | |
| 010 | ▼a 2016042514 | |
| 020 | ▼a 9781498724487 (acid-free paper) | |
| 035 | ▼a (KERIS)REF000018184406 | |
| 040 | ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a QA76.9.D343 ▼b B38 2017 |
| 082 | 0 0 | ▼a 006.3/12 ▼2 23 |
| 084 | ▼a 006.312 ▼2 DDCK | |
| 090 | ▼a 006.312 ▼b B347m | |
| 100 | 1 | ▼a Baumer, Benjamin. |
| 245 | 1 0 | ▼a Modern data science with R / ▼c Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton. |
| 260 | ▼a Boca Raton : ▼b CRC Press,Taylor & Francis Group, CRC Press is an imprint of the Taylor & Francis Group, an informa business, ▼c c2017. | |
| 300 | ▼a xxvi, 551 p. : ▼b ill. (soem col.). ; ▼c 26 cm. | |
| 490 | 1 | ▼a Texts in statistical science |
| 504 | ▼a Includes bibliographical references (p. 499-512) and indexes. | |
| 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. |
| 700 | 1 | ▼a Horton, Nicholas J. |
| 830 | 0 | ▼a Texts in statistical science. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.312 B347m | 등록번호 111827730 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
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.
정보제공 :
목차
This site includes additional resources: http://mdsr-book.github.io/ Introduction to Data Science Prologue: Why data science? Data visualization A grammar for graphics Data wrangling Tidy data and iteration Professional Ethics Statistics and Modeling Statistical foundations Statistical learning and predictive analytics Unsupervised learning Simulation Topics in Data Science Interactive data graphics Database querying using SQL Database administration Working with spatial data Text as data Network science Epilogue: Towards \big data" Appendices Packages used in this book Introduction to R and RStudio Algorithmic thinking Reproducible analysis and workflow Regression modeling Setting up a database server