LEADER 03953nam 2200493 450 001 9910555146203321 005 20200423115832.0 010 $a1-119-25643-7 010 $a1-119-25642-9 010 $a1-119-25644-5 035 $a(CKB)4330000000009719 035 $a(MiAaPQ)EBC5847433 035 $a(CaSebORM)9781119256410 035 $a(EXLCZ)994330000000009719 100 $a20190913d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSAS for R users $ea book for budding data scientists /$fAjay Ohri 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d[2020] 210 4$dİ2020 215 $a1 online resource (211 pages) 311 $a1-119-25641-0 320 $aIncludes bibliographical references and index. 327 $aAbout SAS and R -- Data input, import and print -- Data inspection and cleaning -- Handling dates, strings, numbers -- Numerical summary and group by analysis -- Frequency distributions and cross tabulations -- Using SQL with SAS and R -- Functions, loops, arrays, macros -- Data visualization -- Data output -- Statistics for data scientists. 330 $aBRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics. AJAY OHRI is the founder of analytics startup Decisionstats.com. His research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change, and knowledge flows. He currently advises startups in analytics off shoring, analytics services, and analytics. He is the author of Python for R Users: A Data Science Approach (Wiley), R for Business Analytics, and R for Cloud Computing. 606 $aSAS (Computer program language) 606 $aR (Computer program language) 606 $aStatistics$xData processing 615 0$aSAS (Computer program language) 615 0$aR (Computer program language) 615 0$aStatistics$xData processing. 676 $a005.55 700 $aOhri$b A$g(Ajay),$01060276 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910555146203321 996 $aSAS for R users$92816264 997 $aUNINA