• Online, Self-Paced
Course Description

Discover how to apply statistical algorithms like PDF, CDF, binomial distribution, and interval estimation for data research. How to implement visualizations to graphically represent the outcomes of data research is also covered.

Learning Objectives

Data Scientist 14: Data Research Statistical Approaches

  • describe the features provided by statistical methods and approaches in data research
  • identify the relevance of discrete vs continuous distribution in simplifying data research
  • recognize the features of PDF and CDF from the perspective of data research
  • implement binomial distribution using R
  • specify the types of interval estimation that can be used to enhance data research
  • implement point and interval estimation using R
  • describe the relevance of data visualization techniques in projecting the outcome of data research
  • plot visualizations using R to depict the outcome of data research graphically
  • recall the data integration techniques that facilitate using statistical methods
  • create Histograms, Scatter plots, and Box plots using Python libraries
  • implement missing values and outliers using Python
  • implement data research using various statistical 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.

Feedback

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