Applying Data Science: Business Case Studies Using SAS

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.

Table of Contents

Case Study 1 – Performing Headcount Survival Analysis for Employee Retention
Chapter 1: Using Survival Analysis Methods to Analyze Employee Retention Time
Chapter 2: Analyzing the Effect of Influential Factors on Employee Retention Time
Chapter 3: Performing Survival Data Mining – The Data Mining Approach for Survival Analysis
Chapter 4: Visualizing Employee Retention Data
Case Study 2 – Detecting Structural Changes and Outliers in Longitudinal Data
Chapter 5: Analyzing and Smoothing the Course of Longitudinal Data
Chapter 6: Detecting Structural Changes in Longitudinal Data
Chapter 7 – Detecting Outliers and Level Shifts in Longitudinal Data
Chapter 8 – Results from a Simulation Study with Longitudinal Data
Chapter 9 – Analyzing the Variability of Longitudinal Data
Case Study 3 – Explaining Forecast Errors and Deviations
Chapter 10 – Investigating Forecast Errors with Descriptive Statistics
Chapter 11 – Investigating Forecast Errors with General Linear Models
Chapter 12 – Interpreting the Coefficients of Categorical Variables in Regression Models
Chapter 13: Using Quantile Regression to Get More Than the Average Picture
Chapter 14 – Analyzing the Effect of Manual Overrides in Forecasting
Case Study 4 – Forecasting the Demand for New Products
Chapter 15 – Performing Demand Forecasting for New Products
Chapter 16 – Using Poisson Regression to Forecast the Demand for New Products
Chapter 17 – Using Similarity Search to Forecast the Demand for New Products
Case Study 5 – Checking the Alignment with Predefined Pattern
Chapter 18 – Checking Accounting Data for the Benford’s Law
Chapter 19 – Checking the Benford’s Law for Multiple Accounts
Chapter 20 – Checking Different Patterns in the Data
Case Study 6 – Listening to Your Data – Discover Relationships with Unsupervised Analysis Methods
Chapter 21 – Finding Relationships in Your Analysis Data with Association Analysis
Chapter 22 – Using Variable Clustering to Detect Relationships in Your Data
Chapter 23 – Investigating of Clinical Trial Data in an Explanatory Way
Case Study 7 – Using Monte Carlo Simulations to Understand the Outcome Distribution
Chapter 24 – Calculating the Outcomes of All Possible Scenarios
Chapter 25 – Using Monte Carlo Methods to Simulate the Distribution of the Outcomes
Case Study 8 – Studying Complex Systems – Simulating the Monopoly Board Game
Chapter 26 – Creating a Basic Framework to Simulate the Visit Frequency on the Fields of the Monopoly Board Game
Chapter 27 – Enhancing the Simulation Framework to Consider Special Rules
Chapter 28 – Simulating the Profitability of the Property Fields of the Monopoly Board Game
Appendix A – SAS Macros