Metropolitan State University

MIS 688 : Predictive Analytics

A. Course Description
Credits: 4
Prerequisites: MIS 687 Business Analytics  
Lab Hours/ Weeks: Corequisites: None
Lecture Hours/ Week :  
MnTC Goals: None
Currently, enterprises across almost every industry are seeking talent for predictive analytics. Predictive analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events. Coupled with other types of analytics available (i.e., Descriptive, Diagnostic, and Prescriptive analytics), enterprises can make predictions and then proactively act upon that insight to drive better business outcomes and achieve measurable competitive advantage. Naturally, such demand is met with creative and critical thinking professionals that have been prepared with theories that can be put into practice. The Predictive Analytics course offering includes modules, activities and projects that are structured for following topic objectives: ¿ Understanding the different purposes of analytics (i.e., Diagnostics analytics for data discovery ¿ Why did it happen?, Predictive analytics for forecasting, and simulation ¿ What will happen?, and Prescriptive analytics for planning, and optimization ¿ What should we do?) to choose appropriate analytic techniques for decision making. ¿ Teaching the skills needed to understand the integration of resources (i.e. people, processes, technologies and facilities) to decision making based on predictable data patterns and trends. ¿ Building the skills to apply business intelligence, machine learning, decision science, knowledge discovery, and business performance management to obtain business insight that is linkage to foresight. ¿ Development of knowledge to broker content from multiple sources and systems, integrate into a common vocabulary that is universally meaningful for enterprise economic growth.
B. Course Effective Dates: 08/19/2017 - Present
C. Outline of Major Content Areas:
See Course Description for major content areas.
D. Learning Outcomes (General)
  1. To assure students can understand how predictive analytics delivers value (in comparison to the lessons learned in Business Analytics); this course will leverage content that shows predictive analytics solutions. These solutions will be outlined by three key elements with the third providing supportive sub elements. 1. Data-driven business decisions 2. Deployment into processes and systems 3. The analytical process
E. Learning Outcomes (MN Transfer Curriculum)
This contains no goal areas.
G. Special Information