Applying Data Science: Business Case Studies Using SAS
This post was published 8 years ago. Download links are most likely obsolete. If that's the case, try asking the uploader to re-upload.

English | 1 Mar. 2017 | ISBN: 160764889X | 490 Pages | AZW3 | 24.05 MB
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.
Quick check before we show the links
Helps us keep automated scrapers from hammering the filehosts.