Predictive Analytics with SAS: Build & Deploy Models

Overview

By the end of this course, learners will be able to identify, analyze, evaluate, and construct predictive models using SAS Enterprise Miner. They will gain hands-on skills in data preparation, variable selection, model building, performance evaluation, and deployment for real-world business applications. This course empowers learners to confidently transform raw data into actionable insights, compare and optimize models, and deploy decision-ready analytics workflows. Starting with the basics of SAS Enterprise Miner, participants progress through data preparation, variable transformations, decision tree modeling, neural network applications, and advanced regression techniques. Each module includes structured practice and graded quizzes, reinforcing learning and ensuring mastery of predictive modeling concepts. What makes this course unique is its step-by-step, module-based approach aligned with Bloom’s Taxonomy, combining theory with interactive practice, real-world case scenarios, and automated SAS tools like Auto Neural and Dmine Regression. Unlike traditional tutorials, this program integrates practical flow diagrams, ensemble modeling, and performance evaluation methods, making learners job-ready for predictive analytics roles in industries like finance, healthcare, and marketing.

Syllabus

Getting Started with SAS Enterprise Miner

This module introduces learners to the SAS Enterprise Miner environment, focusing on navigating the interface, preparing datasets, and understanding the foundational steps required to begin predictive modeling.

Preparing and Understanding Data

This module emphasizes data exploration and preparation, teaching learners to select variables, assess their statistical performance, and refine input predictors for stronger modeling accuracy.

Transformations and Model Building

This module covers variable transformation techniques, ensemble modeling, and regression analysis while equipping learners with advanced tools for refining predictive accuracy and handling complex data structures.

Decision Trees and Neural Networks

This module introduces decision tree construction and neural network modeling, focusing on visualization, interpretation, and comparison of advanced predictive techniques within SAS Enterprise Miner.

Model Evaluation and Deployment

This module focuses on evaluating predictive models through comparison metrics, regression with binary targets, and flow diagram design, preparing learners for real-world deployment of SAS models.