Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0064
Organization's ID:
602
Organization:
Location:
Online
Length:
4 weeks
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 Predictive Modelling
Description

Objective:

The course objective is to continue work from Predictive Analytics 1, and introduce additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Four modeling techniques are used: linear regression, logistic regression, discriminant analysis, and neural networks. The course includes hands-on work with Python.

Learning Outcomes:

  • Use discriminant analysis for classification.
  • Preprocess text for text mining and use a predictive model with the resulting matrix.
  • Specify and interpret linear regression models to predict continuous outcomes.
  • Distinguish between profiling (explanation) tasks and prediction tasks for linear and logistic regression.
  • Specify and interpret logistic regression models for classification.
  • Use neural nets for prediction and classification.

General Topics:

  • Linear and Logistic Regression
  • Discriminant Analysis and Neural Nets
  • Text Mining
Instruction & Assessment

Instructional Strategies:

  • Discussion
  • Lectures
  • Textbook readings

Methods of Assessment:

  • Other
  • Quizzes
  • Capstone case study project, graded practical exercises, and discussion forums

Minimum Passing Score:

80%
Supplemental Materials