| |
Sep 07, 2026
|
|
|
|
|
MBA 6390 - Predictive Analytics
Predictive analytics encompasses a variety of statistical and machine learning techniques and applications within business environments. The primary goal of this course is to discover and apply relationships found within historic datasets to make predictions about the future or otherwise unknown events. In this hands-on course, students are introduced to concepts related to constructing, testing, and applying predictive models in various business settings. From this perspective, students utilize software tools to summarize key terminology, conduct an analysis of continuous, classification, and clustering models based on business scenarios, summarize the differences between supervised and unsupervised learning, design a pre-processing strategy for modeling, and select a post-processing technique to improve interpretation of results.
Requisites: Admission to COB graduate or certificate program or permission
Credit Hours: 3
Repeat/Retake Information: May not be retaken.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,PR,WP,WF,WN,FN,AU,I
Learning Outcomes:
- Students will be able to summarize terminology used in predictive analytics.
- Students will be able to develop continuous, classification, and clustering models based on business scenarios.
- Students will be able to summarize the differences between supervised and unsupervised learning.
- Students will be able to design a pre-processing strategy for a dataset so that it can be modeled.
- Students will be able to select a post-processing technique so the results of a model are more easily understood.
Add to Portfolio (opens a new window)
|
|
