DOWNLOAD SECTION: Applying Data Science - Business Case Studies Using SAS

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This is the DOWNLOAD page of the new SAS Press book Applying Data Science - Business Case Studies Using SAS of Gerhard Svolba. Other books of the Author: Data Preparation for Analytics and Data Quality for Analytics


Site is currently populated with content on a very frequent basis. Please check back for more details and content! In case of questions contact the author.

  • The files in chapter 6, 7, and 8 are directly created by the SAS Code. However: in the current version, the path where they are written to by the SAS code is hardcoded: it is 'c:\tmp'. --> So please amend this path to an appropriate path for your machine or create a c:\tmp directory on your machine. I will update and change this soon!
  • I am working on a download version of the SAS programs for the SAS University Edition that accounts for the fact that some procedures are not available there. Hope to have this available mid of June. Please contact me, if you need this more urgently.
  • I also am working on a version that can direclty be opened in SAS Studio as a process flow. Should be available mid of June.

Quick Link: Download Everything in one Go

Use this link to download a ZIP File that contains

  • SAS Programs
  • SAS Macros
  • SAS Datasets (in Windows SAS7BDAT Format and in extractable format as a SAS Programm)
  • SAS Enterprise Guide Project that contains all this content and the Results
  • Selected Graphs of the Book in Color

Individual Downloads

  • Download the SAS Programs and the Macros
  • Download the SAS Datasets in Windows SAS7BDAT Format
  • Download the SAS Datasets in extractable format as a SAS Programm
  • Download the SAS Enterprise Guide Project that contains all this content and the results
  • Download Selected Graphs of the Book in Color

Selected Graphs of the Book in Color as Visuals

Case Study 1 – Performing Headcount Survival Analysis for Employee Retention

This case study uses employee retention data to illustrate how analytical methods allow you to draw conclusions about the average length of time intervals, even if most of the endpoints have not yet been observed.

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Case Study 2 – Detecting Structural Changes and Outliers in Longitudinal Data

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Case Study 3 – Explaining Forecast Errors and Deviations

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Case Study 4 – Forecasting the Demand for New Products

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Case Study 5 – Checking the Alignment with Predefined Pattern

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Case Study 6 - Listening to Your Data – Discover Relationships with Unsupervised Analysis Methods

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Case Study 8 – Studying Complex Systems – Simulating the Monopoly® Board Game

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Changes and Enhancements

  • page xxvii - Adaption to the Content of the Data Sets: Dataset name in chapter 6 is PATIENTS (not PATIENTS_XT and PATIENTS_MART_TMP)
  • Page 81 and 82 - section 5.5.4 and 5.5.5: in the SAS Code for the CENTERED SMOOTHING WINDOW the moving weights for the first and last period are incorrect. Should be (CMOVAVE (0.0417 0.0833 0.0833 … 0.0833 0.0417)) instead of (CMOVAVE (0.0467 0.0833 0.0833 … 0.0833 0.0467))