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Apr 16, 2024
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QBA 3720 - Predictive Analytics
Modern businesses can benefit by analyzing information stored within datasets. Predictive analytics is a form of analysis that aims to discover trends or patterns within datasets. The mathematical relationships that are found are used to make proactive, future-based, data-driven decisions. Predictive analytics is an important field of study because techniques that are used can find complex relationships that experts may overlook, or may not know at the time of the analysis. This course covers a variety of predictive analytics techniques and theories from the viewpoint of various business applications. To achieve the course learning objectives, students utilize software tools that are commonly used in business to develop, test, and apply continuous, classification and clustering models.
Requisites: QBA 2720 or 3710
Credit Hours: 3
Repeat/Retake Information: May be retaken two times excluding withdrawals, but only last course taken counts.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes: - Students will be able to apply appropriate testing techniques and metrics in order to determine the performance of a model.
- Students will be able to create and apply continuous, classification, and clustering models based on business scenarios.
- Students will be able to demonstrate a fundamental understanding of the terminology used in predictive analytics.
- Students will be able to identify the differences between supervised and unsupervised learning.
- Students will be able to post-process the results of a quantitative method so the results are more easily understood.
- Students will be able to pre-process a dataset so that it can be modeled using quantitative methods.
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