• Online, Self-Paced
Course Description

At the core of predictive analytics lie the models used to make predictions after the data has been collected and preprocessed. Explore predictive techniques, including A/B testing, Bayesian Networks, and the support vector machine (SVM).

Learning Objectives

A/B Testing

  • start the course
  • recognize what A/B testing is and where it is applicable
  • establish an A/B test hypothesis and determine what to test
  • implement A/B testing for web site optimization

Naïve Bayes and Bayesian Belief Networks

  • recognize key features of Naïve Bayes
  • calculate the probability of an event occurring with Naïve Bayes
  • identify various limitations of Naïve Bayes
  • recognize features of Bayesian Belief Networks

Support Vector Machines

  • identify features of Support Vector Machines
  • recognize how to transform linear non-separable data to linear separable data
  • determine the optimal hyperplane

Practice: Applying Predictive Approaches

  • predict outcomes using A/B testing

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.