Course

Course Summary

Credit Type:

Course

ACE ID:

STAT-0030

Organization's ID:

#602

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 continue work from Predictive Analytics 1 and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. The course includes hands-on work with R, Python or Excel Solver software. This course is especially useful for analysts and managers who want to undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.

Learning Outcomes:

distinguish between profiling (explanation) tasks and prediction tasks for linear and logistic regression
Specify and interpret linear regression models to predict continuous outcomes
Specify and interpret logistic regression models for classification
Use discriminant analysis for classification
Use neural nets for prediction and classification
Preprocess text for text mining
use a predictive model with the resulting matrix
use the predictive analytics approach, methodology and context
Approach a predictive task, develop predictive models and assess performance
Use popular data mining algorithms for predictive applications
Understand the logic behind these algorithms, when they are appropriate, how to use them to generate predictions, and how to choose and employ such methods judiciously
build and assess predictive models using multiple datasets and software