Master of Science in Computer Science Online

Focuses on the basic principles of computer communication as well as hardware and software designs. Topics include transmission media, data encoding, transmission techniques, protocols, switching networks, broadcast networks, and local area networks.

Topics include the history and evolution of operating systems, the concepts behind and structure of various operating systems, process scheduling, inter-process communication, input and output, multiprogramming, memory management, and file systems. Concepts of parallel systems are introduced.

Design and analysis of algorithms. Turing machines, NP- Complete theory, best, average, and worst case analysis, divide-and-conquer, greedy method, dynamic programming, graph traversal, backtracking, and branch- and bound techniques. Sorting, searching, graph algorithms, and optimization.

Computer Architecture Principles and advanced topics of the instruction set architecture for uni-processor, embedded system processor, and multi-processor.

An introductory course emphasizing the basic concepts and principles of database systems. Topics include relational, hierarchical, and network approaches to data organization.

This course is designed for IT professionals to learn computer and network security theories and practices that can be used to significantly reduce the security vulnerability of computers on internal networks or the Internet. The course assumes some familiarity with various operating systems and computer networks. Topics include cryptography, program security, operating systems security, database security, network security, security administration, computer ethics, and legal issues.

This course is designed for Security System Administrators and Managers who are responsible for the design, planning and management of security installations in Business and Government Institutions. Topics include Management of Information Security, security planning, security protection (technical and procedural), best practices, risk management, Operations Security, legal issues and certification and accreditation. The course assumes some familiarity with various topics taught in an Introduction of Information Assurance course. Prerequisite: CSC 535.

In depth study of concepts and problem solving techniques of artificial intelligence. Topics include knowledge representation, functional and logic programming, machine learning, natural language understanding, computer vision, robotics, and societal impact.

This course introduces students to game design and development concepts. Topics include the history of games, genres, play elements, story and character development, game play and storyboard design, level and user interface design, and the game design document.

This is a project-oriented course on 3D Game Programming. Students will work in teams to design, implement and test a three-dimensional game with interactivity, game state diagrams, animation, sound, and constraints. Students will also learn the basics of graphic design and animation. Prerequisite: CSC 571.

This course will introduce you to different types of simulation systems and applications. The course will cover an introduction on gaming, military, health, business processes and transportation simulations. You will work as a member of a development team and design simulation system for a specific application area.

Fundamentals of system and network programming methodology, techniques, system calls and library calls. Prerequisite: CSC 564.

Machine learning is a subfield of artificial intelligence that is concerned with the design, analysis, implementation, and applications of programs that learn from experience. It offers some of the most cost-effective approaches to automated knowledge acquisition in emerging data-rich disciplines. This course is about learning to extract statistical structure from data, for making decisions and predictions, as well as for visualization. The course gives in-depth coverage of advanced methods in machine learning, as well as their underlying theory. It emphasizes approaches with practical relevance and discusses a number of recent applications of machine learning, such as recommender systems and malware detection systems. An open research project is a major part of the course.

Computational Science is a rapidly emerging field to foster collaborative research by teams of mathematicians, computer scientists, and scientists, to cover a wide variety of science disciplines. This is a one-semester course to provide students with an overview of applications of computational skills to solve scientific research problems. The computational skills in review include Programming Languages, Algorithms, Database Implementation, Internet technologies, Data Visualization, Statistics, Modeling and simulation, and Operations Research. The course will be team-taught by a group of scientists from Mathematics, Chemistry, Physics, Biology and Engineering departments and Center for Material Research, SciViz Laboratory and the B.E.S.T. Laboratory, and other applied science laboratories to cover a wide-range of expertise.

A one-semester, graduate-level course focuses on the concept of internetworking in general and the TCP/IP internet technology in particular. The course reviews both the architecture of network interconnections and the principles underlying protocols that make interconnected networks function as a single, unified communication system. It also covers how an internet communication system can be used for distributed computation and communication. Prerequisite: CSC 530.

Intrusion detection systems, malicious software (viruses, worms, and other rogue programs), advanced risk analysis methodologies, international standards and computer security models (Bell and LaPadula, Biba, Clark and Wilson), network and distributed security, database security. Prerequisite: CSC 535.

A one-semester, graduate-level course providing an introduction to data science which is experiencing rapid growth in research, development and deployment globally. This course is designed to provide students a solid foundation in data science, covering theories, techniques, and best practices in data science (including data manipulation, statistical analysis, and machine learning). An emphasis on labs and projects will provide students hands-on learning experiences in using popular open-source and industry leading data analytic technologies, tools and solutions, especially those in Python and R. Prerequisite: CSC 625.

Study of historical and modern cryptographic techniques and algorithms. Topics include symmetric and asymmetric key cryptography, one-way functions, secure hash functions, digital signatures, key exchange, authentication, key management, PKI, DES, AES (Rijndael), current topics. Prerequisite: CSC 535.

Fundamental concepts of the algorithms and design principles underlying modern 3D computer graphics, data and scientific visualization.

Analysis of Algorithms & Computer Architecture or Permission of Instructor Study of high performance computing techniques. Includes the study of parallel computer architecture, memory, and I/O. Also, parallel computer algorithms to include shared and distributed memory, parallel computation models, graph algorithms, numerical algorithms, divide-and- conquer. Prerequisite: CSC 625.

An advanced, graduate-level course focusing on study of wireless sensor networks from communications, security, and computing platform viewpoints. Wireless sensor networks are a sensing, computing and communication infrastructure enabling the monitoring and manipulating of the environment.

This course includes the major fields in optical networks, dynamic spectrum access in wireless networks, cognitive radio networks, network coding, and other newly emerged networking technologies. For optical networks, the topics include WDM network elements, routing and wavelength assignment algorithms, blocking probability analysis, virtual/physical topology design, survivability, and IP over WDM. For dynamic spectrum access or cognitive radio networks, the topics include enabling technologies for cognitive radio, channel assignment/selection, routing, security, and spectrum management. This course will also cover network coding and other new ideas.

Focuses on network defense and countermeasures, including firewalls, intrusion detection and prevention systems, virtual private networks. Prerequisite: CSC 530.

The fundamentals of applying biological evolutionary characteristics to optimization of very complex problems.

A one-semester graduate-level course focuses on cloud computing technologies and solutions. It is designed to give students a solid foundation in cloud computing fundamentals. The course covers both the conceptual and practical aspects of cloud computing. Prerequisite: CSC 530.

Introduction to basic concepts and the latest research trends and results in developing secure software. Topics include the best practices in developing secure software within Software Development Lifecycle (SDLC), vulnerability assessment, and code analysis techniques. Prerequisites: CSC 635, 650.

Survey of current topics in Information Assurance.

Supervised independent project designed to give computer science graduate students an opportunity to explore a single topic in a one-to-one learning relationship with a faculty member. This will be the final course in the program.