Applying Data Science: Business Case Studies Using SAS

Applying Data Science: Business Case Studies Using SAS

by Gerhard Svolba
Applying Data Science: Business Case Studies Using SAS

Applying Data Science: Business Case Studies Using SAS

by Gerhard Svolba

eBook

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Overview

See how data science can answer the questions your business faces!

Applying Data Science: Business Case Studies Using SAS, by Gerhard Svolba, shows you the benefits of analytics, how to gain more insight into your data, and how to make better decisions. In eight entertaining and real-world case studies, Svolba combines data science and advanced analytics with business questions, illustrating them with data and SAS code.

The case studies range from a variety of fields, including performing headcount survival analysis for employee retention, forecasting the demand for new projects, using Monte Carlo simulation to understand outcome distribution, among other topics. The data science methods covered include Kaplan-Meier estimates, Cox Proportional Hazard Regression, ARIMA models, Poisson regression, imputation of missing values, variable clustering, and much more!

Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science bridges the gap between high-level, business-focused books that skimp on the details and technical books that only show SAS code with no business context.


Product Details

ISBN-13: 9781635260540
Publisher: SAS Institute
Publication date: 03/29/2017
Sold by: Barnes & Noble
Format: eBook
Pages: 490
Sales rank: 738,962
File size: 58 MB
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About the Author

Dr. Gerhard Svolba is a senior solutions architect and analytic expert at SAS Institute Inc. in Austria, where he specializes in analytics in different business and research domains. His project experience ranges from business and technical conceptual considerations to data preparation and analytic modeling across industries. He is the author of Data Preparation for Analytics Using SAS and teaches a SAS training course called "Building Analytic Data Marts."
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