Marketing 630

MKTG 630
Predictive Analytics & Data Mining

1. Catalog Entry

MKTG 630
Predictive Analytics & Data Mining

Prerequisites: MBA Status or permission from instructor and MKTG 625

Credit hours (3)

This course, the second Business Analytics course taken by MBA students, provides an in-depth understanding and application in Predictive Analytics and Data Mining techniques in order to solve strategic business problems.  

2. Detailed Description of Course

This course, provides an in-depth understanding and application in Predictive Analytics and Data Mining techniques in order to solve strategic business problems.  The course will provide MBA students with an in-depth understanding and application in Predictive Analytics and Data Mining and their extensive use of analytical reasoning and statistical and quantitative analysis.  Exploratory and predictive analytics in providing fact-based models to assist management in making decisions and determining appropriate actions will be emphasized.  

3. Detailed Description of Conduct of Course

Contemporary background readings from texts, contemporary articles from industry leaders and journal articles will provide the foundational knowledge of the various predictive analytics and data mining techniques.  Applied exercises and projects will be used to provide students with an understanding of applications of Data Mining and Predictive Analytics to managerial decisions using “big” data  through hands-on use of industry standard and emerging analytic tools and software including: forecasting and optimization algorithms; pattern recognition modeling; partitioning, hierarchical and linkage cluster based  segmentation procedures; classification and decision trees procedures; neural networks; multiple regression; logistical regression; discriminant analysis; and factor analysis.  Central to student learning is the realization that data mining is not just about numerical data, as 80% of the world’s data is unstructured, comprised of text, emails, photos, etc.  Student will learn the tools and techniques to make sense of both numerical, text, and other unstructured data.

4. Goals and Objectives of the Course

In completing this course student will:
• Recommend the appropriate Predictive Analytics and Data Mining techniques for a variety of business decision problems
• Apply the processes of Predictive Analytics and Data Mining for formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement analytic models for a variety of practical business applications
• Analyze large datasets typical in today’s corporate setting with using IBM SPSS advanced Data Mining software
• Apply predictive models such as classification and decision trees, neural networks, regressions, association analysis, and link analysis, to typical corporate Big Data
• Interpret analyses produced by advanced analytical procedures and explain the results to better inform management decision-making

5. Assessment Measures

Assessment measures may include but are not limited to applied assignments, applied projects, and examinations.

6. Other Course Information

None

Review and Approval
December 10, 2013