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The essentials of data science : knowledge discovery using R

The essentials of data science : knowledge discovery using R (Loan 1 times)

Material type
단행본
Personal Author
Williams, Graham J.
Title Statement
The essentials of data science : knowledge discovery using R / Graham J. Williams.
Publication, Distribution, etc
Boca Raton :   Taylor & Francis, CRC Press,   c2017.  
Physical Medium
xix, 322 p. : ill. (some col.) ; 24 cm.
ISBN
9781138088634 (pbk. : alk. paper) 9781498740005 (hardback : alk. paper)
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Databases. Data mining. R (Computer program language).
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

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.312 W723e Accession No. 121242234 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

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.


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