جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://localhost/handle/Hannan/869
عنوان: Applied Analytics through Case Studies Using SAS and R
عنوان دیگر: Implementing Predictive Models and Machine Learning Techniques /
پدیدآورنده: Gupta, Deepti. ;
کلید واژه ها: Big data. ;;Open source software. ;;Computer Programming;Computer Science;Business mathematics. ;;Big Data. ; http://scigraph.springernature.com/things/product-market-codes/I29120. ;;Open Source. ; http://scigraph.springernature.com/things/product-market-codes/I29090. ;;Probability and Statistics in Computer Science. ; http://scigraph.springernature.com/things/product-market-codes/I17036. ;;Business Mathematics. ; http://scigraph.springernature.com/things/product-market-codes/523000. ;;QA76.9.B45 ;
تاریخ انتشار: 2018
محل نشر: Berkeley, CA :
ناشر: Apress :
Imprint: Apress,
چکیده: Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. . ;
توضیحات : 
SpringerLink (Online service) ;
005.7 ; 23 ;
Printed edition: ; 9781484235249. ;


Printed edition: ; 9781484235263. ;


Printed edition: ; 9781484240465. ;
آدرس: http://localhost/handle/Hannan/869
شابک : 9781484235256 ;
9781484235249 (print) ;
9781484235263 (print) ;
9781484240465 (print) ;
اطلاعات بیشتر: XX, 404 p. 99 illus. ; online resource. ;
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9781484235249.pdf8.16 MBAdobe PDFتصویر
مشاهده فایل
عنوان: Applied Analytics through Case Studies Using SAS and R
عنوان دیگر: Implementing Predictive Models and Machine Learning Techniques /
پدیدآورنده: Gupta, Deepti. ;
کلید واژه ها: Big data. ;;Open source software. ;;Computer Programming;Computer Science;Business mathematics. ;;Big Data. ; http://scigraph.springernature.com/things/product-market-codes/I29120. ;;Open Source. ; http://scigraph.springernature.com/things/product-market-codes/I29090. ;;Probability and Statistics in Computer Science. ; http://scigraph.springernature.com/things/product-market-codes/I17036. ;;Business Mathematics. ; http://scigraph.springernature.com/things/product-market-codes/523000. ;;QA76.9.B45 ;
تاریخ انتشار: 2018
محل نشر: Berkeley, CA :
ناشر: Apress :
Imprint: Apress,
چکیده: Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. . ;
توضیحات : 
SpringerLink (Online service) ;
005.7 ; 23 ;
Printed edition: ; 9781484235249. ;


Printed edition: ; 9781484235263. ;


Printed edition: ; 9781484240465. ;
آدرس: http://localhost/handle/Hannan/869
شابک : 9781484235256 ;
9781484235249 (print) ;
9781484235263 (print) ;
9781484240465 (print) ;
اطلاعات بیشتر: XX, 404 p. 99 illus. ; online resource. ;
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9781484235249.pdf8.16 MBAdobe PDFتصویر
مشاهده فایل
عنوان: Applied Analytics through Case Studies Using SAS and R
عنوان دیگر: Implementing Predictive Models and Machine Learning Techniques /
پدیدآورنده: Gupta, Deepti. ;
کلید واژه ها: Big data. ;;Open source software. ;;Computer Programming;Computer Science;Business mathematics. ;;Big Data. ; http://scigraph.springernature.com/things/product-market-codes/I29120. ;;Open Source. ; http://scigraph.springernature.com/things/product-market-codes/I29090. ;;Probability and Statistics in Computer Science. ; http://scigraph.springernature.com/things/product-market-codes/I17036. ;;Business Mathematics. ; http://scigraph.springernature.com/things/product-market-codes/523000. ;;QA76.9.B45 ;
تاریخ انتشار: 2018
محل نشر: Berkeley, CA :
ناشر: Apress :
Imprint: Apress,
چکیده: Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. . ;
توضیحات : 
SpringerLink (Online service) ;
005.7 ; 23 ;
Printed edition: ; 9781484235249. ;


Printed edition: ; 9781484235263. ;


Printed edition: ; 9781484240465. ;
آدرس: http://localhost/handle/Hannan/869
شابک : 9781484235256 ;
9781484235249 (print) ;
9781484235263 (print) ;
9781484240465 (print) ;
اطلاعات بیشتر: XX, 404 p. 99 illus. ; online resource. ;
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9781484235249.pdf8.16 MBAdobe PDFتصویر
مشاهده فایل