LEADER 03104nam 2200517 450 001 9910580163903321 005 20221226004217.0 010 $a1-4842-8155-1 024 7 $a10.1007/978-1-4842-8155-0 035 $a(MiAaPQ)EBC7021726 035 $a(Au-PeEL)EBL7021726 035 $a(CKB)24056170500041 035 $a(OCoLC)1333434850 035 $a(OCoLC-P)1333434850 035 $a(CaSebORM)9781484281550 035 $a(PPN)266357253 035 $a(EXLCZ)9924056170500041 100 $a20221226d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBeginning data science in R 4 $edata analysis, visualization, and modelling for the data scientist /$fThomas Mailund 205 $aSecond edition. 210 1$aNew York, NY :$cApress Media, LLC,$d[2022] 210 4$dİ2022 215 $a1 online resource (528 pages) $cillustrations 300 $aIncludes index. 311 08$aPrint version: Mailund, Thomas Beginning Data Science in R 4 Berkeley, CA : Apress L. P.,c2022 9781484281543 320 $aIncludes index 327 $a1: Introduction 2: Introduction to R Programming 3: Reproducible Analysis 4: Data Manipulation 5: Visualizing Data 6: Working with Large Data Sets 7: Supervised Learning 8: Unsupervised Learning 9: Project 1: Hitting the Bottle 10: Deeper into R Programming 11: Working with Vectors and Lists 12: Functional Programming 13: Object-Oriented Programming 14: Building an R Package 15: Testing and Package Checking 16: Version Control 17: Profiling and Optimizing 18: Project 2: Bayesian Linear Progression 19: Conclusions 330 $aDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition 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. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well. 606 $aR (Computer program language) 606 $aStatistics$xData processing 615 0$aR (Computer program language) 615 0$aStatistics$xData processing. 676 $a519.502855133 700 $aMailund$b Thomas$0846442 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910580163903321 996 $aBeginning Data Science in R 4$92896465 997 $aUNINA