| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045919619 | |
| 005 | 20171102165142 | |
| 008 | 171102s2017 flua b 001 0 eng d | |
| 010 | ▼a 2017028898 | |
| 020 | ▼a 9781138088634 (pbk. : alk. paper) | |
| 020 | ▼a 9781498740005 (hardback : alk. paper) | |
| 035 | ▼a (KERIS)REF000018475468 | |
| 040 | ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009 | |
| 050 | 0 0 | ▼a QA76.9.D32 ▼b W56 2018 |
| 082 | 0 0 | ▼a 006.3/12 ▼2 23 |
| 084 | ▼a 006.312 ▼2 DDCK | |
| 090 | ▼a 006.312 ▼b W723e | |
| 100 | 1 | ▼a Williams, Graham J. |
| 245 | 1 4 | ▼a The essentials of data science : ▼b knowledge discovery using R / ▼c Graham J. Williams. |
| 260 | ▼a Boca Raton : ▼b Taylor & Francis, CRC Press, ▼c c2017. | |
| 300 | ▼a xix, 322 p. : ▼b ill. (some col.) ; ▼c 24 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Databases. |
| 650 | 0 | ▼a Data mining. |
| 650 | 0 | ▼a R (Computer program language). |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W723e | 등록번호 121242234 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data.
Building on over thirty years’ experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets.
The book begins by introducing data science. It then reviews R’s capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.
정보제공 :
목차
Part I - An Overview for the Data Scientist. Data Science, Analytics, and Data Mining. From Rattle to R for the Data Scientist. Preparing Data. Building Models. Case Studies. R Basics. Part II - Data Foundations. Reading Data into R. Exploring and Summarising Data. Transforming Data. Presenting Data. Part III - Analytics. Descriptive Analytics. Predictive Analytics. Prescriptive Analytics. Text Analytics. Social Network Analytics. Part IV - Advanced Data Science in R. Dealing with Big Data. Parallel Processing for High Performance Analytics. Ensembles for Big Data.
정보제공 :