Course

Course Summary
Credit Type:
Course
ACE ID:
STAT-0029
Organization's ID:
#601
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 introduce the basic concepts in predictive analytics, the most prevalent form of data mining. This online course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. This course is useful for marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters and professionals who want to understand what predictive modeling might do for their organization, undertake pilots with minimum setup costs, or manage predictive modeling projects or ongoing predictive modeling deployments. The use of Excel Solver, R or Python is supported in this course.

Learning Outcomes:

  • visualize and explore data to better understand relationships among variables
  • Partition data to provide an assessment basis for predictive models
  • Choose and implement appropriate performance measures for predictive models
  • Specify and implement models with the following algorithms: k-nearest-neighbor, naive Bayes, classification and regression trees
  • understand how ensemble models improve predictions.
  • explore and visualize data using four modeling techniques: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers
  • Use XLMiner software to combine different models to obtain results
  • use partitioning to divide the data into training data, validation data, and test data

General Topics:

  • What is supervised learning?
  • Classification and prediction
  • Bayesian classifiers
  • Cart ensembles
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