• Online, Self-Paced
Course Description

Predictive analytics involves a wide range of statistical tools and methods that allow an analyst to build a powerful predictive model. Explore the importance of statistics and probability theory in predictive analytics.

Learning Objectives

Importance of Statistics in Analytics

  • start the course
  • recognize the role of statistics in predictive analytics
  • recognize attributes of qualitative, quantitative, discrete, and continuous data
  • recognize features of data measurement scales
  • recognize features of descriptive and inferential statistics

Overview of Probability Theory

  • recognize basic features of probability and the types of probabilistic events
  • apply addition and multiplication rules for a probabilistic event
  • apply Bayes theorem in a given situation
  • distinguish between permutations and combinations

Using Statistics in Predictive Analytics

  • recognize how to reduce the margin of error
  • recognize how confidence intervals (CI) are used for hypothesis testing
  • recognize key features of testing for differences in mean and testing for differences in proportion
  • determine if a data sample is representative of the data population

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.