500
Overview of computer organization, programming languages, various computer applications, information management systems and ethical implications of computing. Does not count toward CPS major or minor or the MS in Computer Science. This course may be offered in an online format. Prerequisite: Senior or graduate standing.
Credits
3(3-0)
Covers requirements analysis and techniques to develop a system from those requirements. Credit will not be given for both
CPS 410 and
CPS 510. Does not count for the M.S. in Computer Science. This course is approved for offering in a distance learning format. Prerequisites:
CPS 340;
MTH 175.
Credits
3(3-0)
Study of software architecture styles and quality attributes; achieving software quality; designing, documenting, and restructuring software architecture evaluation; tools for architectural design. Prerequisite:
CPS 410 or graduate status.
Credits
3(3-0)
Overview of tools and techniques to process text data with the aim of production actionable insights. Introduction to the cleaning, filtering, transforming and querying of text data. Prerequisites:
CPS 181,
CPS 240 or
CPS 280 or
CPS 285; or admitted to the Master of Science in Computer Science program.
Credits
3(3-0)
Study of design and implementation issues in distributed programming, including event model, client/server, distributed data processing, remote objects, network services, and security. Prerequisite:
CPS 340 or admission into CPS graduate program.
Credits
3(3-0)
Relational databases, data modeling, NoSQL databases, and data warehousing. Design and develop databases for various applications. Prerequisites:
CPS 340 or
ITC 341 or graduate standing.
Credits
3(3-0)
Models of computation. Analysis of algorithms including computing time and space requirements. Efficient algorithm design techniques. Introduction to parallel algorithms. Prerequisite:
CPS 340.
Credits
3(3-0)
Introduction to computer networks. Point-to-point, local area and wireless networks. Protocols, routing algorithms, and congestion control in networks. Prerequisites:
CPS 340, 360.
Credits
3(3-0)
Theory and tools for supervised machine learning. Overview and application of architectures and training techniques applicable to image classification, object recognition and text generation. Prerequisites:
CPS 340 or
ITC 510 or
CPS 285, DAS 450.
Credits
3(3-0)
Study of data engineering concepts and techniques, including data collection, cleaning, transformation, security and storage. Construction of a data pipeline in an applied setting. This course may be offered in an online or hybrid format. Prerequisites:
CPS 181;
CPS 240 or 280 or 285; or admitted to the Master of Science in Computer Science program. Recommended:
ITC 341.
Credits
3(3-0)
Selected topics in computer science not otherwise available in a course regularly offered. Topic(s) are listed in Course Search and Registration. Repeatable up to 6 credits when content does not duplicate previous special topic studies. Prerequisite: See Course Search and Registration. Determined by the specific topics presented.
Credits
1-6(Spec)
Exploration of topics in Computer Science agreed upon by student and instructor. May be taken for credit more than once, total credit not to exceed six hours. Prerequisite: Permission of instructor.
Credits
1-6(Spec)