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Advanced Business Intelligence: Introduction to Predictive Analytics


This course introduces the predictive modeling process and basics of predictive analytics for business applications, including hands-on introduction to data preparation, model identification and validation, model documentation, and interpretation of model results.

Topics include:

  • Explain predicative analytics key concepts and terms, benefits, and applications
  • Identify and set up the business problem for predictive analytics
  • Understand the steps to creating a predictive model
  • Comprehend the data mining process, including data collection or selection, data cleansing, evaluation of results, best practices and common mistakes
  • Explore visualizing and sharing model results
  • Perform various data mining techniques, including decision trees, regression, cluster analysis, Artificial Neural Networks, and various ensemble methods

Practical experience:

  • Examine case studies of successful data mining applications in business, industry, and science

Software: R Programming and Weka 3 will be used in this course. Both are open source and can be downloaded at no additional cost.

Course typically offered: Online in Fall and Spring.

Prerequisites: CSE-41198: Introduction to Statistics Using R or previous background knowledge and experience with a programming language and statistics.

Next step: Upon completion of this course, consider taking Fundamentals of Data Mining for further learning.

More information: For more information about this course, please contact

Course Number: CSE-41288
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence AnalysisMachine Learning MethodsR for Data Analytics

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