• Online, Self-Paced
Course Description

Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.

Learning Objectives

Data Insights, Anomalies, & Verification: Handling Anomalies

  • list sources of data anomaly and compare the differences between data verification and validation
  • describe approaches of facilitating decomposition and forecasting, and list the steps and formulas used to achieve the desired outcome
  • recall data examination approaches, and use randomization tests, null hypothesis, and Monte Carlo
  • identify anomaly detection scenarios and categories of anomaly detection techniques
  • recognize prominent anomaly detection techniques
  • demonstrate how to facilitate contextual data and collective anomaly detection using scikit-learn
  • list prominent anomaly detection tools and their key components
  • recognize essential rules of anomaly detection
  • implement anomaly detection using scikit-learn, R, and boxplot

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.