Course

Credit Type:
Course
ACE ID:
STAT-0024
Version:
4
Organization's ID:
603
Organization:
Statistics.com
Location:
Online
Length:
4 weeks
Minimum Passing Score:
73
ACE Credit Recommendation Period:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Predictive Modelling
Description

Objective:

The course objective is to cover key unsupervised learning techniques: association rules, principal components analysis, and clustering. Predictive Analytics 3 will include an integration of supervised and unsupervised learning techniques.

Learning Outcomes:

  • Understand the issues related to using too many predictors (the “curse of dimensionality”)
  • Combine unsupervised and supervised learning methods in a final project
  • Use association rules to find patterns of “what goes with what” in transaction data
  • Use principal components analysis to reduce the number of predictors to a smaller number of “components” of correlated predictors
  • Use hierarchical clustering and k-means clustering to find and describe clusters of similar records

General Topics:

  • Dimension Reduction
  • Cluster Analysis
  • Association Rules and Recommender Systems
  • Integrating Supervised and Unsupervised Methods
  • Introduction to Network and Text Analytics
Instruction & Assessment

Instructional Strategies:

  • Discussion
  • Lectures
  • Practical Exercises
  • Textbook readings

Methods of Assessment:

  • Other
  • Graded practical exercises and discussion forums
Supplemental Materials
Equivalencies

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