• Online, Self-Paced
Course Description

Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy.

Learning Objectives

Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools

  • describe the supervised and unsupervised approaches of anomaly detection
  • compare the prominent anomaly detection algorithms
  • demonstrate how to detect anomalies using R, RCP, and the devtools package
  • identify components of general online anomaly detection systems
  • describe the approaches of using time series and windowing to detect anomalies
  • recognize the real-world use cases of anomaly detection as well as the steps and approaches adopted to handle the entire process
  • demonstrate detecting anomalies using boxplot and scatter plot
  • demonstrate the mathematical approaches of detecting anomalies
  • implement anomaly detection using a K-means machine learning approach
  • implement anomaly detection with visualization, cluster, and mathematical approaches

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.