What you'll get
  • 9+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Hands-on analytics and data handling using the SAS platform
  • Learn regression tools, interpret results, and create flow diagrams
  • Build a strong foundation for advanced statistical analysis
  • Practice predictive modeling with SAS Enterprise Miner

Content

Courses No. of Hours Certificates Details
SAS - Predictive Modeling with SAS Enterprise Miner9h 19m✔View Curriculum

Description

This course provides a comprehensive introduction to predictive analytics and modeling using leading tools such as SAS, Minitab, and SPSS. You will learn to create, test, and validate predictive models using statistical, machine-learning, and AI-based techniques. The course emphasizes the use of multiple predictors (attributes) to forecast future outcomes and equips you with practical skills widely applied in IT, business, finance, healthcare, and research.

You will learn to analyze data, spot important patterns, and understand model results through practical examples and hands-on exercises. You will also gain industry-relevant coding experience and build a strong conceptual foundation in regression, classification, neural networks, SVMs, ROC curves, and more.

This training builds the skills you need to meet the rising demand for predictive modeling and data science careers, ranked among the top opportunities by McKinsey, Gartner, and other industry leaders.

What You Will Learn:

  • Analyze data, identify patterns, and understand data distributions

  • Build and interpret predictive models using SAS, SPSS, and Minitab

  • Apply key machine learning concepts such as regression, classification, SVMs, and neural networks

  • Work with real-life datasets and develop hands-on coding skills

  • Strengthen your understanding of industry-relevant predictive modeling techniques

Requirements

  • Prior knowledge of Quantitative Methods

  • Basic familiarity with MS Office (Excel, PowerPoint)

  • Understanding of basic data concepts

  • Fundamental knowledge of statistics (mean, variance, correlation)

  • Comfort working with spreadsheets or simple data files

  • Basic analytical and problem-solving skills

  • Familiarity with any programming or statistical tool (R, Python, SAS, SPSS) is a plus.

Target Audience

  • Students from technical, computer science, mathematics, or statistics backgrounds

  • Entry-level professionals in software, banking, insurance, stock markets, and IT

  • Managers and experienced professionals seeking roles in data analysis or data science.