DATA 240 - Predictive Analytics

Credits: 5
Variable Credit Course: No

Lecture Hours: 55
Lab Hours: 0
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

Course Description: Explore the foundational concepts of predictive analytics and machine learning. Key topics include variable and model selection strategies, training and evaluating machine learning models, and utilizing web services to support model development. Fundamental algorithms, such as linear regression, decision trees, and logistic regression are covered.

Prerequisite: DATA 145 and DATA 215 and MATH& 146 all with a C or higher; or Dept. Chair permission.
Meets FQE Requirement: No
Integrative Experience Requirement: No

Student Learning Outcomes

Explain foundational concepts in predictive analytics including variable selection, model selection, training, and evaluation.
Describe modern use cases for predictive analytics.
Identify key models used in predictive analytics including linear regression, decision trees, and logistic regression.
Discuss the benefits and limitations of machine learning web services.
PROGRAM OUTCOME: Apply descriptive and predictive analytics techniques on complex datasets to empower ethical and informed decision-making in business contexts.

Course Contents

Variable selection.
Model selection.
Training and evaluation.
Use-cases for predictive analytics.
Linear regression, decision trees, and logistic regression.
Machine learning web services.

Instructional Units: 5