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024 7 $a10.1007/978-1-4842-2671-1
035   $a(CKB)3710000001095376
035   $a(DE-He213)978-1-4842-2671-1
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035   $a(CaSebORM)9781484226711
035   $a(PPN)199770484
035   $a(OCoLC)983204760
035   $a(OCoLC)ocn983204760
035   $a(EXLCZ)993710000001095376
100   $a20170309d2017      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBeginning Data Science in R $eData Analysis, Visualization, and Modelling for the Data Scientist /$fby Thomas Mailund
205   $a1st ed. 2017.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2017.
215   $a1 online resource (XXVII, 352 p. 100 illus.)
311 08$a9781484226704 
311 08$a1484226704 
311 08$a9781484226711 
311 08$a1484226712 
320   $aIncludes bibliographical references and index.
327   $a1. Introduction to R programming -- 2. Reproducible analysis -- 3. Data manipulation -- 4. Visualizing and exploring data -- 5. Working with large data sets -- 6. Supervised learning -- 7. Unsupervised learning -- 8. More R programming -- 9. Advanced R programming -- 10. Object oriented programming -- 11. Building an R package -- 12. Testing and checking -- 13. Version control -- 14. Profiling and optimizing.
330   $aDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You?ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. You will: Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code.
606   $aBig data
606   $aProgramming languages (Electronic computers)
606   $aR (Computer program language)
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
615  0$aBig data.
615  0$aProgramming languages (Electronic computers)
615  0$aR (Computer program language)
615 14$aBig Data.
615 24$aProgramming Languages, Compilers, Interpreters.
676   $a006.312
700   $aMailund$b Thomas$4aut$4http://id.loc.gov/vocabulary/relators/aut$0846442
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910254561803321
996   $aBeginning Data Science in R$92217892
997   $aUNINA