- COMPUTER INFORMATION SCIENCE
- COMMUNICATION STUDIES
- HOSPITALITY AND TOURISM MANAGEMENT
- MEDIA COMMUNICATIONS AND PUBLIC RELATIONS
- OPERATIONS MANAGEMENT
- POLITICAL SCIENCE
- SOFTWARE ENGINEERING
- LEGAL STUDIES
- COMMUNICATION STUDIES
COMPUTER SCIENCE 101: Problem Solving and Programming I W/Lab (COMP 101, 4 Credits)
Programming and project driven lab. Students gain fluency in integrating technology to efficiently and effectively solve problems using computational thinking.
COMPUTER SCIENCE 201: Problem Solving and Programming II W/Lab (COMP 201, 4 Credits)
Problem solving with state-of-the-art programming language. Expressions, statements, basic control flow and methods. Data, data aggregation, and usage. Uses a structured personal software development process to implement solutions representative of common computing applications. Programming exercises and demonstrations to reinforce concepts learned and provide additional practice in programming.
COMPUTER SCIENCE 220: Introduction to Computer System Fundamentals (COMP 220, 3 Credits)
Logic design, number systems and arithmetic, boolean algebra; digital systems components, and hardware description languages.
COMPUTER SCIENCE 240: Introduction to Programming Languages (COMP 240, 3 Credits)
Introduces the procedural, applicative, and declarative languages. Focus on practical exercises to highlight the practical differences in use.
COMPUTER SCIENCE 251: Introduction to Computer Networking (COMP 251, 3 Credits)
Examines the systems aspects of the different network models, including topics such as protocols, network operating systems, applications, management and wireless communication systems. It also examines how the different models are interconnected using bridges and routers.
COMPUTER SCIENCE 271: Introduction to Data Structures (COMP 271, 3 Credits)
Design, implementation and use of core data structures; object-oriented software development: design, analysis and programming.
COMPUTER SCIENCE 280: Introduction to Computer Architecture (COMP 280, 3 Credits)
Digital Logic and Data Representation, process architecture and instruction sequencing, memory hierarchy and bus-interfaces and functional organization.
COMPUTER SCIENCE 291: Discrete Structures II (COMP 291, 3 Credits)
Recurrence relations and their use in the analysis of algorithms. Graphs, trees, and network flow models. Introduction to Finite state machines, grammars, and automata.
COMPUTER SCIENCE 320: Microcomputer Architectures (COMP 320, 3 Credits)
Microcomputer architecture, instruction set, assembly language programming and debugging, I/O considerations, memory interface, peripherals and busses, exception/interrupt handling.
COMPUTER SCIENCE 420: Introduction to Database Management Systems (COMP 420, 3 Credits)
Fundamental methods in modeling and managing data-oriented systems. Relational, object, and hierarchical data modeling techniques. Query languages including SQL. Semantics of transaction processing. Database system architectures including cloud-based, client-server, and embedded databases. Security and privacy issues. Modern trends in data management including managing data on the cloud, unstructured data type management, data mining, and business analytics, and NoSQL data management platforms.
COMPUTER SCIENCE 420L: Introduction to Database Management Systems Lab (COMP 420L, 1 Credit)
Project driven lab for COMP 420.
COMPUTER SCIENCE 401: Advanced concepts in Software Engineering (COMP 401, 3 Credits)
In-depth focus on an area of topical interest to individual Computer Science students.
COMPUTER SCIENCE 450: Operating Systems (COMP 450, 3 Credits)
Fundamentals of operating systems, process management, scheduling, synchronization techniques and file management. Network technology, topologies, protocols, application control; network and operating system security.
COMPUTER SCIENCE 460: Introduction to Machine Learning and Artificial Intelligence (COMP 460, 3 Credits)
Introduction to analytical techniques used in data science and prepares students for advanced courses in machine learning. Covers multivariate distributions, information theory, linear algebra, supervised/unsupervised learning, classification/regression, linear/non-linear learning, introduction to Bayesian learning and parametric/non-parametric estimation.
COMPUTER SCIENCE 491: Computer Science Capstone Project (COMP 491, 3 Credits)
Student lead, project driven deep exploration of a chosen topic with faculty approval and support