Courses

Key tasks and functions required of a database administrator in a business environment. Credit will not be given for both ISMN 5680 and ISMN 6680/6686. 380 Department of Business Analytics and Information Systems

Key tasks and functions required of a database administrator in a business environment. Credit will not be given for both ISMN 5680 and ISMN 6680/6686.

This course is a follow-up course to CS 302 for non-majors wishing to learn more about the design and use of database systems. Now that the underpinnings of data, data representation, and data visualization are in place from CS 302, students will undertake an investigation into the uses of data and the construction of and understanding of databases design principles. Computing proficiency is required for a passing grade in this course. Computer Science

This course discusses design and implementation issues associated with relational and object-oriented databases. Topics include E-R modeling, relational modeling, normal forms, data storage, and concepts of object-oriented data modeling.

An intensive examination of organizational databases, including data validity, reliability, security, and privacy. Generating reports using structured query languages is emphasized. Distributed databases, data mining, and data warehousing are introduced. The roles of database administrator and data administrator will be explored including understanding data integrity and security. A current enterprise DBMS will be used.

This course provides an introduction to the concepts and technologies of big data. Key concepts and techniques allow organizations to analyze structured and unstructured data/information collected from transaction processing systems, data warehouses, and distributed systems. The ultimate purpose of descriptive, predictive, and prescriptive analytics is to support high quality decision support for executives and managers.

The focus here is on the management of data and the technologies which specifically targets mass data storage with a view to online and after-the-fact examination of data to acquire new insights. The major topics include: data warehouse planning, data warehouse models, and supporting software, date mining concepts and tools, creation of data mining models for the tools and matching the tool to the task.

This course delves into advanced techniques and methodologies for extracting knowledge and meaningful insights from large and complex data sources. It covers data mining methodologies, including measurement, evaluation, and visualization, alongside advanced algorithms and applications. Students will learn to address real-world challenges across diverse domains such as business, healthcare, finance, and social networks.

The first part of the course covers how to build a cloud infrastructure based on a cloud computing reference model. The second part of the course cover backup and recovery infrastructure.

Advanced concepts, technologies, and methodologies used to electronically store and manage data using Storage Area Network, Network Attached Storage, and other technologies. The structure of the course combines lecture and labs. Credit is not given this course and INFO 4150. Prerequisite INFO 6140 or INFO 4100.