• Online, Self-Paced
Course Description

There are many software and programming tools available to data scientists. Before applying those tools effectively, you must understand the underlying concepts. In this course, you'll explore the underlying data analysis concepts needed to employ the software and programming tools effectively.

Learning Objectives

Data Science Math

  • start the course
  • perform basic math operations required by data scientists
  • perform basic vector math operations required by data scientists
  • perform basic matrix math operations required by data scientists
  • perform a matrix decomposition

Data Analysis Concepts

  • identify different forms of data
  • describe probability in terms of events and sample space size
  • describe basic properties of outcomes
  • apply probability rules in calculation
  • identify common continuous probability distributions
  • identify common discrete probability distributions
  • apply bayes theorem and describe how it is used in email spam algorithms

Estimates and Measures

  • apply random sampling to A/B tests
  • identify and describe various statistical measures
  • describe the difference between an unbiased and biased estimator
  • describe sampling distributions and recognize the central limit theorem
  • define confidence intervals and work with margins of error
  • carrying out hypothesis tests and working with p-values
  • apply the chi-square test for categorical values

Practice: Identifying Data

  • identify the given data set descriptions by their types

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.