Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0020
Organization:
Statistics.com
Location:
Classroom-based
Length:
4 weeks (60 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Statistics or Data Mining
Description

Objective:

The course objective is to cover statistical methods to identify possible clusters in multivariate data, including hierarchical clustering, k-means clustering, two-step clustering and normal mixture models.

Learning Outcomes:

  • Apply mixture models to multivariate data and interpret the output
  • Interpret and diagnose the output of different clustering procedures.
  • Conduct hierarchical cluster analysis and k-means clustering to identify clusters in multivariate data
  • Apply normalization of data appropriately in cluster analysis
  • Identify the assignment of cases to clusters

General Topics:

  • Hierarchical clustering (in which smaller clusters are nested inside larger clusters)
  • K-means clustering
  • Two-step clustering
  • Normal mixture models for continuous variables
Instruction & Assessment

Instructional Strategies:

  • Case Studies
  • Classroom Exercise
  • Computer Based Training
  • Discussion
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Case Studies
  • Quizzes

Minimum Passing Score:

70%
Supplemental Materials