Introduction to Computer Science II (non-majors) (3) Introduction to World Wide Web applications and design, including Web scripting languages and HTML editors. Three hours of lecture per week.

An Introduction to programming using C++. Covers fundamental concepts of structured programming and provides a comprehensive introduction to computer programming. Topics include problem solving, software development methodology, data types, control structures, functions, arrays. Co-Requisite: CS-120L.

A laboratory course in computer programming in C++. Provides students with hands-on programming examples and exercises that supplement the concepts discussed in CS 120 lectures. Topics include C++ data types, variables, input/output, selections statements, loops, pointers, arrays, and functions. Co-Requisite: CS 120.

Study of the theory and applications of discrete mathematical structures as a foundation for topics in computer science.

Introduction to the JAVA programming language that covers the use of object oriented programming methodologies such as class inheritance, polymorphism, multi-threading, generics, GUI components, and exceptions. Required for computer science majors and minors. Prerequisite: CS 120. Co-Requisite: CS 140L.

A laboratory course in computer programming in Java. Complements the concepts covered in CS 140. Helps students gain hands-on programming experience though examples and exercises tailored for CS 140 lectures. Topics include data types, variables, input/output, conditional statements, loops, arrays, and functions. Co-Requisite: CS 140.

In depth study of the object oriented programming methodologies using the C++ programming language. Students will work through a number of programming exercises to explore the concepts of structures, pointers, advanced file operations, classes, inheritance, and polymorphism.

Basic concepts of digital computers: Boolean algebra, combinatorial and sequential logic design, arithmetic/logic units, control units, memory units, and input/output units, flip flops, synchronized and asynchronized counters. Required for computer science majors and minors.

Advanced programming techniques and data structures including tables, linked lists, queues and stacks. Abstract data types, recursion, searching and sorting, hashing, binary trees. External storage devices and sequential and direct file organization, file processing techniques.

Introduction to automata and languages, computability and complexity of algorithms. This course covers graph theory, finite state automata, determinism non-determinism, regular expressions, context free and non-context free grammars, algorithm definition, algorithm complexity, class P, class NP algorithms and NP-completeness.

Introduction to the fundamental networking concepts and technologies focusing on both the conceptual and practical skills needed to understand basic networking. Students will gain an understanding of the “layered” approach to networks and examine the OSI and TCP/IP layers in detail to understand their functions and services. It provides an overview to various network devices, network addressing schemes and, finally, the types of media used to carry data across the network.

A comprehensive study of internetworking as well as routing concepts and protocols is presented to develop an understanding of how networks are linked together. An introduction to routers, their role in the network, their main hardware and software components, and the packet forwarding process is included. This course covers the foundations of static and dynamic routing protocols. It provides a detailed study of various Distance Vector as well as Link State protocols and examines their characteristics, operations, limitations, configuration, and troubleshooting techniques.

Introduction to the structure and design of the programming language paradigm, formal specification of syntax, semantics, functional languages, logic languages, parallel languages, data types and interfacing procedures.

Rigorous study of the architecture, applications, programming, and interfacing of current microprocessors, co-processors, and controllers. Hardware and software structures found in modern digital computer systems. Parallel architectures included.

Theory and current practices in database management systems, data organizational models, including hierarchical and networked, with relational and semantic models stressed.

This course covers an introduction to LAN switching and wireless LANs and. in depth examination of the underlying concepts and processes of the common Layer 2 switching protocols and technologies. It provides the necessary knowledge required to implement, verify, and troubleshoot Local Area Networks. It also covers wireless LAN standards and concepts required to design, implement and troubleshoot wireless LANs.

This course is an introduction to the various wide area networks (WANs) access technologies used to connect small-to medium-sized business networks. This course introduces WAN converged applications and quality of service (QoS). It focuses on WAN technologies including PPP, Frame Relay, broadband links, and WAN security concepts. It covers the principles of traffic control and access control lists and describes how to implement IP addressing services for an Enterprise network, including how to configure NAT and DHCP. Finally, students learn how to detect, troubleshoot and correct common Enterprise network implementation issues.

A thorough introduction to logic programming using Prolog. Includes a description of Prolog data objects such as predicates, clauses, facts, and queries and introduces the concepts of goal resolution though unification and negation as failure. Programming techniques using control, meta-logical and extra-logical predicates and arithmetic operations are also studied.

Numerical solution of nonlinear equations, integration, interpolation and data smoothing, systems of linear and nonlinear equations.

This course is a study of the ethical and social issues related to computers and computer networks. It provides an introduction to the legal, social, and ethical issues surrounding information technology and to the societal risks addressed in software testing and reliability standards. Safety and relevant legal cases will be covered.

Introduction to the function, internal data structures, and operations of operating systems and their associated file systems.

This course focuses on the fast emerging field of multimedia authoring and application development. It covers multimedia representation, storage, and communication. It provides students with the basics of integrating audio, video, and textual sources into multimedia objects. Software and hardware issues related to multimedia are studied in this class.

This course provides an introduction to the basic concepts of the network-management architectures and protocols. It covers, in detail, the implementation, operation, security, management and support of complex LAN and WAN networks to develop an understanding of the tools, procedures and standards needed for network administration. Students will learn common network management concepts and protocols such as Structure of Management Information (SMI), Management Information Base (MIB), Simple Network Management Protocol (SNMP), Remote Monitoring (Rmon), and Common Management Information Protocol (CMIP).

This course provides an introduction to wireless and mobile networks and covers the following topics: mobile radio propagation; traffic engineering; cellular concepts; multiple radio access; multiple division techniques; channel allocation; mobile communication systems; existing wireless systems; network protocols; Ad Hoc and sensor networks; and wireless LANs and PANS.

Study of the principles and practices of software engineering. Topics include software quality concepts, process models, and analysis of software requirements, design methodologies, software testing, and software maintenance.

Introduction to the fundamental theories, algorithms and representational structures underlying Artificial Intelligence and practice techniques for programming AI applications using Prolog. General areas covered include search techniques, production systems, planning, learning, and connectionist systems.

Basic concepts of computer graphics, including programming, hardware, display technology, and data structures for both micros and high-performance workstations.

Advanced Topics (3) Presentation of advanced topics in computer science by faculty and students. Three hours of lecture per week. Corequisite: CS 456.

A CS required capstone design course to encourage independent study, project design, and development. Proposal must be submitted and approved during term preceding enrollment.

Complements the concepts introduced in COSC 1345 such as input/output, data types, variables, iterations and selections statements, arrays, and strings. Students are required to enroll in the same semester as the co-requisite course COSC 1345 and will receive the same grade earned for COSC 1345. Two contact hours per week.

Introduction to Computer Science I (non-majors) (3) Study of fundamental concepts of computing: how computers work, what they can do, and how they can be used effectively. Topics covered: spreadsheets, word processing, databases, presentation software, multimedia/graphics software, program design and implementation, and fundamental computing theories. Three hours of lecture per week. Listed in the Texas Common Course Numbering System as COSC 1300.

Study of the Python programming language. Includes procedural and data abstractions, program design, debugging, testing, and documentation. Covers data types, control structures, functions, parameter passing, library functions, arrays and inheritance.

Introductory-level course in statistical learning with a focus on regression and classification methods. Course covers: linear and polynomial regression, logistic regression, cross validation, model selection and regularization methods; tree-based methods, random forests and boosting. Some unsupervised learning methods are introduced, principal component and clustering. Three hours of lecture per week.

Introduction to the fundamental theories, algorithms and representational structures underlying Artificial Intelligence and practice techniques for programming AI applications. General areas covered include search techniques, production systems, planning, learning, and connectionist systems. Three hours of lecture per week.

Concepts of Machine learning, including data preparation, visualization, model building, evaluation and scoring are presented. A modern-day machine learning language will be used to implement all data analysis tasks or statistics code needed to be incorporated. Includes introduction to foundational packages for machine learning that are required for basic data manipulation. Three hours of lecture per week.

Overview of cybersecurity; cybersecurity standards and plan of action; cybersecurity protocol and framework; network security; cybersecurity with mobility; Internet security; algorithms for cryptography and cryptoanalysis in cybersecurity.

Focus will be on descriptive analytical methods, such as clustering: K nearest neighbor, K-means and classification and predictive methods such as decision trees, Bayesian classification and support vector machines. Advanced AI programming language will be used throughout the course. Three hours of lecture per week

This course will focus on data analysis and management techniques that can be applied to massive datasets (Big Data) produced by various sources such as large sensor networks, new data acquisition techniques, simulations, and social networks. The course provides a balanced view of "theory" and "practice" which allows the student to understand, use, and build practical big data analytics systems. The course will focus on techniques used for analyzing very large amounts of data. This course will present several advanced learning methods that are used for solving the big data problems. Three hours of lecture per week

An elective course for students in undergraduate program to take Professional Internships are directly related to the student's program of study and provide learning experiences not available in the classroom setting. Internships provide career-related experience, and workplace competencies that employers value.

Independent study allows the student to explore a topic of interest under the close supervision of a faculty member. The course may include directed readings, applied work, assisting a faculty member with a research project, carrying out an independent research project, or other activities deemed appropriate.

An elective course for students in graduate program to take internship. Advanced Internships are directly related to the graduate student's career development and provide learning experiences not available in the classroom setting. Internships provide career-related experience, and workplace competencies that employers value.