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|a 9781484210017
|9 978-1-4842-1001-7
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|a Tripathi, Subhashini Sharma.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Learn Business Analytics in Six Steps Using SAS and R
|h [electronic resource] :
|b A Practical, Step-by-Step Guide to Learning Business Analytics /
|c by Subhashini Sharma Tripathi.
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|a 1st ed. 2016.
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|a Berkeley, CA :
|b Apress :
|b Imprint: Apress,
|c 2016.
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|a XV, 219 p. 156 illus., 131 illus. in color.
|b online resource.
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|a text
|b txt
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|a online resource
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|a text file
|b PDF
|2 rda
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|a 1.The process of Analytics - DCOVA and I -- 2. Accessing SAS and R -- 3 Data Manipulation using SAS and R - Collect and Organising the data -- 4.Discover basic information about data -- 5.Visualisation -- 6.Analyse - Probability and Distributions -- 7.Analyse- Sampling and Sampling Distributions -- 8.Analyse- Confidence Intervals and sanctity of Analysis -- 9.Insight Generation.
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|a Apply analytics to business problems using two very popular software tools, SAS and R. No matter your industry, this book will provide you with the knowledge and insights you and your business partners need to make better decisions faster. Learn Business Analytics in Six Steps Using SAS and R teaches you how to solve problems and execute projects through the "DCOVA and I" (Define, Collect, Organize, Visualize, Analyze, and Insights) process. You no longer need to choose between the two most popular software tools. This book puts the best of both worlds-SAS and R-at your fingertips to solve a myriad of problems, whether relating to data science, finance, web usage, product development, or any other business discipline. You will: Use the DCOVA and I process: Define, Collect, Organize, Visualize, Analyze and Insights. Harness both SAS and R, the star analytics technologies in the industry Use various tools to solve significant business challenges Understand how the tools relate to business analytics See seven case studies for hands-on practice.
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|a Big data.
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|a Computer software.
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|a Computer science-Mathematics.
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|a Mathematical statistics.
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|a Database management.
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|a Data mining.
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|a Big Data.
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|a Mathematical Software.
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|a Probability and Statistics in Computer Science.
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|a Database Management.
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650 |
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|a Data Mining and Knowledge Discovery.
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710 |
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9781484210024
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|i Printed edition:
|z 9781484210031
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|i Printed edition:
|z 9781484283684
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|u https://doi.uam.elogim.com/10.1007/978-1-4842-1001-7
|z Texto Completo
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|a ZDB-2-CWD
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|a ZDB-2-SXPC
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|a Professional and Applied Computing (SpringerNature-12059)
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950 |
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|a Professional and Applied Computing (R0) (SpringerNature-43716)
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