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- Author: Gerhard Svolba
- Publisher: SAS Institute
- ISBN-10: 1642953415
- ISBN-13: 9781642953411
- Format: 21.6 x 27.9 x 2.7 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 22d.21:18:52
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Gerhard Svolba
- Publisher: SAS Institute
- ISBN-10: 1642953415
- ISBN-13: 9781642953411
- Format: 21.6 x 27.9 x 2.7 cm, kieti viršeliai
- Language: English English
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.
Reviews