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.