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Course Criteria
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3.00 Credits
This course introduces social network analysis as the study of social ties through the lens of networks and graph theory. It explores essential concepts and techniques to investigate and find meaningful patterns in social networks, e.g., Twitter. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): GPA of 2.5 or higher; CS 2420 with a C- or better Prerequisite Recommendation(s): MATH 2270; CS 5665 Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Dual-listed as: CS 6840 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course is intended for students interested in gaining hands-on experience applying computational techniques to solve big data analysis problems. The introduced topics help students learn practical tools to perform the necessary steps of a data analysis project pipeline. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): CS 2420 with a grade of C- or better; a minimum GPA of 2.5 Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Dual-listed as: CS 6850 Repeatable for credit: No Grade Mode: Standard
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1.00 - 4.00 Credits
Current topics in computer science as determined by advances in the field. Prerequisite(s): CS 2420 with a grade of C- or better; a minimum GPA of 2.5 Registration Restriction(s): Not available to pre-Computer Science majors or graduate students in the Computer Science department Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course provides the independent study of selected topics. Prerequisite(s): GPA of 2.5 or higher; CS 2420 with a C- or better Registration Restriction(s): Not available to pre-Computer Science majors Registration Restriction Special Approval: Instructor permission Repeatable for credit: No Grade Mode: Standard
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4.00 Credits
This course introduces high-performance computing, leveraging parallel computing power to solve complex computational problems faster using clusters and supercomputers. Students learn the main programming models, optimized coding for modern multi-core processors, clusters, and modern computing architecture. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. Cross/Dual Listed as: CS 5030 Prerequisites/Restrictions: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
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3.00 Credits
This course introduces principles, methods and techniques for visual analysis of scientific data. Students create visualization of scalar, vector and tensor field data using state-of-the-art techniques. They acquire hands-on experience using visualization software on real science and engineering use cases. Additional coursework is required for those enrolled in the graduate-level course. Crosslisted as: CS 5040 Prerequisites/Restrictions: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
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3.00 Credits
This course presents algorithms and data structures of computational geometry. Students learn algorithm design techniques for solving geometric problems as well as their applications in data processing, computer graphics, robotics, computer-aided design, and many others. CS 6050 and CS 7050 are cross listed, but CS 7050 requires additional work. Prerequisite: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science Also Taught as: CS 7050
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3.00 Credits
This course examines state-of-the-art research on time series (TS) data mining. It includes the applications of TS data in finance, medicine and numerous fields in science and engineering. Topics covered include data representation, anomaly detection, similarity search, classification, visualization of TS, etc. Additional coursework is required for those enrolled in the graduate-level course. Crosslisted as: CS 5080 Prerequisites/Restrictions: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
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4.00 Credits
This course introduces software systems that are both autonomous and social, engaging in cooperation, coordination, and negotiation. Students study coalitions, auctions, game theory, voting systems, and types of agents. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. Registration Restriction(s): Enrollment in one of the following programs, or instructor permission: Master of Computer Science - MCS Computer Science - MS Data Science - MS Computer Science - PhD Dual-listed as: CS 5110 Repeatable for credit: No Grade Mode: Standard
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1.00 - 9.00 Credits
Provides credit for students working at a participating firm under faculty supervision. Prerequisite/Restriction: 3.0 GPA; permission of instructor and enrollment in Computer Science master's or PhD program. Repeatable for credit. Pass/Fail only.
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