Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0024
Organization's ID:
#603
Organization:
Statistics.com
Location:
Online
Length:
8 weeks (120 hours)
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 predictive analytics
Description

Objective:

The course objective is to cover key unsupervised learning techniques including association rules, principal components analysis, and clustering. Students will also review integration of supervised and unsupervised learning techniques. Participants will apply data mining algorithms to real data, and will interpret the results. A final project will integrate an unsupervised task with supervised methods covered in predictive Analytics 1 and 2. Students will use either R, Excel Solver or Python software.

Learning Outcomes:

  • identify the issues related to using too many predictors (the 'curse of dimensionality')
  • 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
  • Use association rules to find patterns of 'what goes with what' in transaction data
  • combine unsupervised and supervised learning methods in a final project
  • use unsupervised methods for detecting relationships between records, between measurements, and other entities, by illustrating different unsupervised approaches, methods and context
  • Identify and approach for an unsupervised task, apply appropriate tools, and interpret or utilize the results
  • run unsupervised analyses using multiple datasets using software

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:

  • Audio Visual Materials
  • Case Studies
  • Classroom Exercise
  • Coaching/Mentoring
  • Computer Based Training
  • Discussion
  • Practical Exercises
  • Project-based Instruction

Methods of Assessment:

  • Other
  • Quizzes
  • Project

Minimum Passing Score:

80%
Supplemental Materials