Data Sciences Minor

The course provides an overview of the theoretical foundation and applied use of Geographic Information Systems (GIS). At the conclusion of the course, students will have working knowledge of GIS and their appropriate application in various disciplines and organizational settings. The historical development of innovative and collaborative geographic information tools that assist with managing the challenges associated with positional data will also be introduced. Students will demonstrate acquired knowledge via application of the fundamental and principal concepts of geographical information systems by completing a series of lab assignments and a culminating research project. Prerequisites: EGR 121 and CIS 265. (3 units; Fall)

Economic concepts of supply, demand, and production; cost-benefit analysis and break-even analysis; return on investment; analysis of options; time value of money; management of money: economic analysis, accounting for risk applied to the engineering process. Prerequisite: MAT 245 or STA 144. (3 units; Fall/Spring)

Overview of current database technologies with an emphasis on relational database technology. Introduction to database design, entity relationship diagraming, structured query language, and stored procedures. Prerequisite: CIS 268 or EGR 121. (3 units; Spring)

This course provides an introduction to the field of Security in computing. Topics include common security standards and policies, cryptography and information security, access controls, attacks and countermeasures, and computer forensics. Prerequisite: EGR 222. (3 units; Fall)

This course explores state-of-the-art computer communication infrastructure from protocols and protocol architecture to organizational management issues. Special focus areas from TCP/IP Internet communication, protocol design, wireless data networking, to networked applications are studied. The emphasis is on business managers and implementers to design, realize and operate advanced networks that provide efficient and reliable services to users. Topics include data science, data communications, application software, networking, and organization management theory. Prerequisite: Permission of the Department Chair. (4 units; Fall)

This course examines at the entire knowledge discovery process. The focus on cutting edge, interesting data mining techniques that can be used in a wide variety of settings (business, science, web analytics, etc.). Topics include algorithmic details, implementation issues, advantages and disadvantages, and many examples of data mining. We will also include the newest topics, such as big data methods and deep learning. The course is a practical, hands-on course, covering knowledge discovery entails, the different groups of algorithms and their usefulness and shortcomings. The course culminates in a real-world project accomplished with open-source tools. Prerequisite: Permission of the Department Chair. (3 units; Summer)