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Applied Analytics through Case Studies Using SAS and R: Implementing Predictive Models and Machine Learning Techniques

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Product Overview

Specifications

Publisher	148423524X
ISBN 13	9781484235249
ISBN 10	9781484235249
Author	Deepti Gupta
Language	English

Book Description	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. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniquesCreate and apply analytical strategiesWho This Book Is ForData scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.
About the Author	Deepti Gupta completed her MBA in Finance and PGPM in operation research in 2010. She has worked with KPMG and IBM private limited as Data Scientist and is currently working as a data science freelancer. Deepti has extensive experience in predictive modeling and machine learning with an expertise in SAS and R. Deepti has developed data science courses, delivered data science trainings, and conducted workshops for both corporate and academic institutions. She has written multiple blogs and white papers. Deepti has a passion for mentoring budding data scientists.
Publication Date	4 August 2018
Number of Pages	426 pages

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