• Online, Self-Paced
Course Description

With predictive analytics, relevant data should be stored for easy retrieval, kept up-to-date, and attributes must be selected contingent on their predictive potential. Explore data reduction and graphic tools for exploratory data analysis.

Learning Objectives

Data Reduction with PCA and Factor Analysis

  • start the course
  • recognize key data reduction methodologies
  • use principal component analysis for feature selection
  • use the information theory approach for feature selection
  • recognize the key features of using Chi-square
  • recognize key features of the wrapper data reduction method
  • recognize the key features of factor analysis

Tools for Exploratory Data Analysis (EDA)

  • recognize key features of EDA and how quantitative techniques are used to perform EDA
  • use bar charts and box-and-whisker plots to perform EDA
  • use run charts and scatter plots to perform EDA
  • use histograms and stem-and-leaf plots to perform EDA

Practice: Using PCA for Feature Selection

  • recognize the direction, form, and strength of a scatter plot

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.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.