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Coursera

Predictive Analytics with SAS: Build & Deploy Models

EDUCBA via Coursera

Overview

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Master predictive analytics with SAS Enterprise Miner and learn to turn raw data into decision-ready insights. You’ll begin by navigating the SAS workspace and configuring data sources, then progress through data exploration, variable selection, frequency tables, fit statistics, and transformations that improve model reliability and predictive accuracy. As you advance, you’ll build and refine regression, decision tree, neural network, and ensemble models. You’ll interpret diagnostic plots, tree structures, neural network weights, ROC charts, iteration plots, and lift charts, then compare model performance using ASE and event-based overlays. You’ll also use Auto Neural and Dmine Regression, construct flow diagrams, and document workflows that support model deployment and business decision-making. Designed for learners pursuing predictive analytics roles or applying modeling in finance, healthcare, and marketing, this course combines foundational concepts with structured practice, graded quizzes, interactive analysis, and real-world case scenarios. Its step-by-step progression helps you confidently prepare data, evaluate competing models, select the strongest performer, and deploy predictive analytics workflows. Enroll to develop practical SAS modeling skills for real-world business applications.

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.

Taught by

EDUCBA

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