Master of Science (M.S.) Major in Computer Science (Data Science Concentration Non-thesis Option)

CS 5100. Advanced Computer Science Internship.

This course provides advanced training supervised by computer scientists in internship programs approved by the department. Course cannot be counted toward any graduate degree, is open only to majors in the Department of Computer Science. May be repeated once. This course does not earn graduate degree credit. Prerequisite: Instructor approval.

CS 5199B. Thesis.

This course represents a student’s continuing thesis enrollments. The student continues to enroll in this course until the thesis is submitted for binding.

CS 5299B. Thesis.

This course represents a student’s continuing thesis enrollments. The student continues to enroll in this course until the thesis is submitted for binding.

CS 5300. Professional Development of Graduate Assistants.

This course is designed to develop and enhance the professional and technical skills of graduate teaching and instructional assistants. Topics covered may include, but are not limited to, teaching skills, technical skills, ethical and legal issues, and laboratory management. This course does not earn graduate degree credit.

CS 5301. Programming Practicum.

This course provides an intensive review of programming through data structures. Topics include syntax, semantics, problem-solving, and algorithm development. Credit for this course cannot be applied to a graduate degree.

CS 5302. Foundations of Data Structures and Algorithm Design.

This course serves as a foundation course for computer science master's degree students who need reinforcement of fundamental concepts covered by CS 3358. This course does not earn graduate degree credit.

CS 5303. Foundations of Computer Architecture.

This foundation course for CS master's degree students who need CS 3339 concept reinforcement covers fundamental hardware components. Topics include ALUs, single and multiple cycle datapath and control, RISC vs. CISC, pipelining, caches, I/O, virtual memory, and related performance issues. It may be repeated once and is non-graduate degree credit. Prerequisite: Instructor Approval.

CS 5305. Foundations of Operating Systems.

This course serves as a foundation course for computer science master's students who need reinforcement of fundamental concepts covered by CS 4328. Topics include the principles of operating systems, central processing unit scheduling algorithms, memory management, cooperating sequential processes, and device management. Credit for this course cannot be applied to a graduate degree.

CS 5306. Advanced Operating Systems.

This course provides a study of modern operating systems, including network, distributed, and real-time systems.

CS 5310. Network and Communication Systems.

This course provides a study of network and communication systems. Students will be required to perform verification and implementation of protocols.

CS 5316. Data Mining.

This course covers fundamental concepts and techniques, plus recent developments in data mining and information retrieval. It provides relevant research training and practice opportunities. May not be taken for credit if the student has received credit for CS 4315.

CS 5318. Principles of Programming Languages.

This course focuses on the principles of programming languages. Topics covered include programming paradigms, concepts of programming languages, formal syntax and semantics, and language implementation issues.

CS 5326. Advanced Studies in Human Factors of Computer Science.

This course provides a professional-level presentation of techniques and research findings related to human-computer interactions.

CS 5329. Algorithm Design and Analysis.

This course provides an introduction to algorithm design and analysis, computational complexity, and NP-completeness theory.

CS 5331. Crafting Compilers.

Overview of the internal structure of modern compilers. Research on compilation techniques. Topics include lexical scanning, parsing techniques, static type checking, code generation, dataflow analysis, storage management, and execution environments.

CS 5332. Data Base Theory and Design.

This course covers computer system organization for the management of data. Topics include data models, data model theory, optimization and normalization, integrity constraints, query languages, and intelligent database systems.

CS 5334. Advanced Internet Information Processing.

This course integrates popular scripting and database programming languages to provide advanced information processing for Internet applications that demand database support and sophisticated, application-specific information processing. Prerequisite: CS 5332 with a grade of "C" or better.

CS 5338. Formal Languages.

This course covers advanced topics in automata theory, grammars, Turing machines, decidability, and algorithmic complexity. A strong background in both data structures and discrete mathematics is required.

CS 5341. Advanced Network Programming.

Study of advanced concepts and programming skills in computer networks such as advanced TCP/IP, API, multicasting and broadcasting, reliable communications, advanced I/O functions and options. Prerequisite: CS 5310 with a grade of "C" or better.

CS 5343. Wireless Communications and Networks.

This course covers the fundamental aspects of wireless communications and wireless/mobile networks, introduction of wireless/mobile networking Application Programming Interfaces (APIs). Prerequisite: CS 5310 with a grade of "C" or better.

CS 5346. Advanced Artificial Intelligence.

This course covers knowledge representation, knowledge engineering, parallel and distributed artificial intelligence (AI), heuristic searches, machine learning and intelligent databases, and implementation of systems in high-level AI languages.

CS 5351. Parallel Processing.

This course provides an introduction to the design and analysis of parallel algorithms, parallel architectures, and computers.

CS 5352. Distributed Computing.

This course provides studies in advanced topics in distributed systems: concurrency control and failure recovery, management of replicated data, distributed consensus and fault tolerance, remote procedure calls, naming, and security.

CS 5369J. Advanced Human Computer Interaction.

This course will cover state of the art human computer interaction topics such as perceptual compression, eye-gaze, and brain computer interfaces with emphasis on the human visual system, eye-tracking, and electroencephalography.

CS 5369L. Machine Learning and Applications.

Provides broad introduction to machine learning, including learning theory, and recent topics like support vector machines and feature selection. Covers basic ideas, intuition, and understanding behind modern machine learning methods. Discusses applications like face recognition, text recognition, biometrics, bioinformatics, and multimedia retrieval.

CS 5369Q. Recommender Systems.

This course covers various concepts of recommender systems, including personalization algorithms, evaluation tools, and user experiences. Discussion of how recommender systems are deployed in business applications, design of new recommender experiences, and how to conduct and evaluate research in recommender systems. Cannot take for credit if already took CS 4379Q.

CS 5369Y. Green Computing.

Reducing mobile device, cloud computing platform, and supercomputer energy consumption is a paramount, daunting problem. This course covers state-of-the-art green computing research, including energy-efficient hardware and software design, power-aware resource management and storage solutions, green data centers and mobile computing. Cannot be taken for credit if received CS 4379Y credit.

CS 5369Z. Distributed Ledger Systems and Blockchains: Theory and Applications.

This course covers fundamental concepts underlying the design, implementation, research, and applications of Distributed Ledger Technology (DLT) systems (e.g., blockchains). It introduces implementations, applications, and performance evaluation of DLT systems. Additionally, through homework projects, the students will be introduced to current research on DLT systems and perform independent study and small-scale research on selected topics. Course topics include cryptography encryption, security, anonymity, cryptographic data structures, DLT performance evaluation, DLT applications, and current DLT research.

CS 5375. Multimedia Computing.

This course provides a study of the digital representation and processing of the three principal multimedia data types: image, audio, and video. Standards, storage media, and compression techniques for the three data types are covered.

CS 5378. Advanced Computer Security.

This course covers various aspects of producing secure computer information systems that provide guaranteed controlled sharing. Emphasis is on software models and design, including discovery and prevention of computing systems security vulnerabilities. Current systems and methods are examined and critiqued.

CS 5388. Advanced Computer Graphics.

This course covers the algorithms and data structures used in representing and processing visual data.

CS 5389. Graphical User Interfaces.

This course covers both abstract and practical treatments of using graphics to implement interactive computer/human interfaces. It includes a survey of the major GUI standards and tools.

CS 5391. Survey of Software Engineering.

The course covers the software life cycle, emphasizing system analysis and design, including a survey of methodologies based on data flows and objects. The course includes a professional ethics component.

CS 5392. Formal Methods in Software Engineering.

The use of design and specification languages in producing software systems. Emphasis is placed on proving correctness of designs and implementations.

CS 5393. Software Quality.

The latter half of the software life cycle is discussed. Topics include testing, performance evaluation, and software metrics. Appropriate software tools are studied and used.

CS 5394. Advanced Software Engineering Project.

Students produce a software project of significant size in a team environment. All aspects of the software engineering course sequence are integrated and put into practice.

CS 5395. Independent Study in Advanced Computer Science.

Open to graduate students on an independent basis by arrangement with the faculty member concerned. Course is not repeatable for credit. Prerequisite: CS 3358 with a grade of "C" or better.

CS 5396. Advanced Software Engineering Processes and Methods.

The essentials of software engineering processes, methods, and tools for the evolutionary design of complex interactive software are discussed. Overviews of other topics like quality concepts, SEI CMM, information technology, and network technology are covered. Student completes a literature survey of the latest software engineering analysis and design processes, methods, and tools.

CS 5399A. Thesis.

This course represents a student’s initial thesis enrollment. No thesis credit is awarded until the student has completed the thesis in CS 5399B.

CS 5399B. Thesis.

This course represents a student’s continuing thesis enrollment. The student continues to enroll in this course until the thesis is submitted for binding.

CS 5599B. Thesis.

This course represents a student’s continuing thesis enrollments. The student continues to enroll in this course until the thesis is submitted for binding.

CS 5999B. Thesis.

This course represents a student’s continuing thesis enrollments. The student continues to enroll in this course until the thesis is submitted for binding.