For the Summer 2014 term, Franklin University is offering, for the first time, a 12-week,
hands-on course in the field of data warehousing from the perspective of building an
information database to support organization-critical information analytic modeling –
descriptive, predictive and prescriptive. In today’s environment, organizations that
successfully use the data, information and knowledge stored in their corporate information
systems to describe where they are now, predict where they must go, and even in some
case prescribe how to get there are the organizations that thrive, that get out ahead of their
competition and stay there. Individuals who can determine what data, information and
knowledge needs to be gathered together, what transformations must be performed, what
data structures the transformed data, information and knowledge need to be placed in, what
information modeling formulas need to be supported are key to that success. INFA 415 is
designed to give students a firm grounding in all of those areas of information analytics,
using state-of-the-art, industry leading tools from Oracle Corporation.
INFA 415
Information Analytics Architecture
Course Description
Upon successful completion of this course, students will be able to design and implement
data gathering processes and information analytics architectures within data warehousing
environments appropriate for supporting data mining and information analytics modeling
applicable to the solving of typical operational, supply and demand problems encountered
by organizations. Learning will be supported by relevant texts, lectures, research papers,
collaboration sessions and projects, both individual and team-based. The database, data
warehouse and computational tools used in this course are Oracle Corporation products.
Course Objectives
1. Define information analytics architecture
2. Apply techniques and strategies for effectively organizing and labeling information in
a business analytics context
3. Explain the relationship of information analytics architecture to achieving effective
navigation, searching, retrieval, modeling and analyzing of information analytics
4. Explain design versus organization of information analytics
5. Evaluate information analytics to effectively organize and categorize it within a
predictive business analytics context
6. Design metadata for specific information analytic sets to support effective retrieval
7. Apply industry standard frameworks, models, and systems for categorizing and
labeling information analytics