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 of Big
Data stores for disparate data sets, processing large data sets and querying them
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: Eligibility for ENGL 838 or ENGL 848 or ESL 400. CIS 132 or CIS 363