Course Summary
Credit Type:
Organization's ID:
8 weeks (120 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 predictive analytics


The course objective is to continue work from Predictive Analytics 1 and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. The course includes hands-on work with R, Python or Excel Solver software. This course is especially useful for analysts and managers who want to undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Learning Outcomes:

  • distinguish between profiling (explanation) tasks and prediction tasks for linear and logistic regression
  • Specify and interpret linear regression models to predict continuous outcomes
  • Specify and interpret logistic regression models for classification
  • Use discriminant analysis for classification
  • Use neural nets for prediction and classification
  • Preprocess text for text mining
  • use a predictive model with the resulting matrix
  • use the predictive analytics approach, methodology and context
  • Approach a predictive task, develop predictive models and assess performance
  • Use popular data mining algorithms for predictive applications
  • Understand the logic behind these algorithms, when they are appropriate, how to use them to generate predictions, and how to choose and employ such methods judiciously
  • build and assess predictive models using multiple datasets and software

General Topics:

  • Linear and Logistic Regression
  • Discriminant Analysis and Neural Nets
  • Text Mining
  • Multiclass classification
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Case Studies
  • Classroom Exercise
  • Coaching/Mentoring
  • Computer Based Training
  • Discussion
  • Practical Exercises
  • Project-based Instruction

Methods of Assessment:

  • Other
  • Quizzes
  • Project

Minimum Passing Score:

Supplemental Materials