Unless otherwise indicated, a grade of C or higher is required for all prerequisite
Introduction to data warehousing architecture, data extraction, management, and load.
Also covered are metadata management, dimensional modeling, data aggregation, data
mining and Business Intelligence. Both SQL and NoSQL databases will be employed. Introduction
to Big Data architecture, technologies and analytics. Selection, processing and querying
of Big Data stores for disparate data sets are also covered. Other topics such as
Cloud computing, security management, machine learning, Agile methodology and Big
Data tools will be introduced.
Grade Option (Letter Grade or Pass/No Pass)
Lecture hours/semester: 48-54
Lab hours/semester: 48-54
Homework hours/semester: 96-108
Recommended: Completion of or concurrent enrollment in CIS 132.