DEPARTMENT OF COMPUTER SCIENCE

The graduate program in Computer Science prepares students for professional employment or further study at the doctoral level. Computer Science may be selected as the major for the Master of Science degree. Computer Science may also be selected as a minor for MS and MA degrees in related areas.

The Computer Science program’s curriculum utilizes state-of–the-art software, software development methodologies, project management techniques, and hardware. Emphasis is placed on preparing student for an environment where change is the norm. Research areas include software engineering, networking, programming languages, language translators, artificial intelligence, database, parallel processing, real time systems, digital forensics, security, data mining and the application of theory to practical problems in industry. The quality of Computer Science graduates is widely recognized by industry. Graduates are employed within the state of Texas, the United States, and many foreign countries.

The Master’s program reflects a diverse student body with applicants from Texas, the United States, and a host of foreign countries. Computer companies where SHSU Computer Science graduates have been employed by IBM, Microsoft, Dell, HP, and Texas Instruments, Internet, and communication companies. Many graduates also find positions in energy related fields. Firms employing SHSU graduates include major oil, transportation/distribution, and waste disposal companies.

ADMISSION REQUIREMENTS

Students seeking admission to the graduate program in Computer Science must submit the Graduate Studies Application for Admission with the one-time application fee to the Office of Graduate Studies, and official transcripts of all college-level work (including the transcript that shows the date the undergraduate degree was conferred). In addition the following are required:

1. A recommended minimum Verbal + Quantitative GRE score of 1000. For a final admissions decision, however, GRE scores do not constitute the sole criterion for consideration of the applicant, nor do GRE scores constitute the primary criterion to end consideration of an applicant.
2. At least two letters of recommendation that address qualifi cations for graduate study.
3. International students must submit scores on the TOEFL.

Graduate study in Computer Science is accessible both to students who have completed undergraduate Computer Science majors or minors and to those with baccalaureate degrees in related fi elds with the equivalent of a Computer Science minor in formal course work or professional experience. As a minimum, candidates are expected to present a background comparable to that provided in CS 164, 165, 334, 362, 431, 482, MTH 299, and MTH 379 as described in the undergraduate catalog of Sam Houston State University. Applicants with less preparation will be required to complete additional stem work as part of the graduate program. This requirement covers the stem work courses that are taken at Sam Houston State University as well.

Degree Plans

There is a thesis and non-thesis option available for a Master of Science degree. The graduate advisor will review each applicant’s background and assist in developing individual study plans including any required stem work. The selection of a minor and the choice of electives must be approved by the Computer Science Department Chair to ensure the student’s plan is a properly balanced program.

Master of Science, 36 hours without thesis and 39 hours with thesis.

Common Requirements:

1. A 15-hour core consisting of CS 531, 536, 564, 566, and 584
2. An oral examination over core courses
3. Six semester hours of approved Computer Science courses
4. One of the following:

A. CS 561 (a practicum project, and oral presentation of the project)
B. CS 698 and CS 699 ( research and thesis)

Additional Requirements:

The degree program may include a 12-semester hour minor in a field approved by the Chair of the Computer Science Department, or 12 additional semester hours of approved Computer Science courses. The 12-semester hour minor in an approved field may only be chosen if the student’s plan of study provides for a minimum of 48 semester hours (total graduate and undergraduate) in Computer Science or the student has extensive professional experience in Computer science.

Other Scholarly Requirements

A committee is assigned to each student at the time the student registers for either CS 561 (the programming practicum) or CS 698 (the thesis). Committee appointments are made by the Chair of the Computer Science Department based upon recommendation from the Computer Science Graduate Advisor. The committee consists of graduate faculty from the Computer Science Department and possibly one from the minor area, if applicable. The oral comprehensive examination, required by the University of all Master’s degree candidates, as well as the CS 561 project presentation or the CS 698 thesis defense, will be administered by this committee. Students must be enrolled the semester in which they take comprehensive examinations. Once enrolled in a thesis class, a student must be continually enrolled until graduation.

CS 531 OPERATING SYSTEMS. A comprehensive study of computer operating systems. Topics include: computer architecture, concurrent processes, multi-threaded systems, scheduling, memory management, I/O management, fi le systems, networking and the client/server model, distributed systems, and computer security. Prerequisites: CS 362 and 431. Credit 3.

CS 532 PARALLEL COMPUTING. This course is a study of large-scale parallel processing systems. The central themes are theoretical models, machine architecture, computer algorithms, and programming languages that model, support, describe and implement parallel processing. Prerequisite: CS 584. Credit 3.

CS 533 MICROCOMPUTER INTERFACING. This course emphasizes real-time and fault-tolerant computing systems. Topics include interrupt processing, real-time programming and scheduling, fault-tolerant architectures and systems, and robotic programming. Extensive programming will be done. Prerequisite: CS 333. Credit 3.

CS 536 SOFTWARE ENGINEERING. This course emphasizes strategies, techniques, and methodologies that deal with the complexity in developing large-scale information systems. Methods for Software engineering methodologies, conventional as well as object-oriented, are discussed. Software measurement and management are discussed. Formal mechanisms for system specifi cation, software development, and project management are introduced. Prerequisite: CS 437. Credit 3.

CS 538 COMPUTER GRAPHICS. A study of modern Computer Graphics programming techniques. Topics include: representations, transformations, and analysis of 2-dimensional and 3-dimensional objects; techniques for hidden surface/edge removal, illumination and shading, volume rendering, animation, and image data compression; and practical experience in graphics software libraries and applications. Prerequisite: CS438. Credit 3.

CS 544 DATA MINING AND KNOWLEDGE DISCOVERY. An introduction into Data Mining and Knowledge Discovery. Topics include discussion of variety of mining techniques. Mining of complex data such as multimedia database, text database, and world-wide-web will be introduced. The applications and trends in data mining will also be discussed. Prerequisite: CS566. Credit 3.

CS 560 SPECIAL TOPICS. Topics and courses are selected to suit individual needs of students. The course may be repeated for additional credit. Prerequisite: Consent of graduate advisor. Credit 3.

CS 561 PROGRAMMING PRACTICUM. The practicum provides the student an opportunity to develop their programming and analytical skills by applying concepts and techniques learned in organized classes to real world projects under the supervision of faculty and/or supervisory Computer professionals. Prerequisite: Eighteen hours of Computer Science graduate level course work. Student must register for this course every semester the practicum is in progress but only three hours of practicum will apply to the student’s degree plan. Credit 3.

CS 562 COMPUTER ARCHITECTURE AND ORGANIZATION. An introduction into Computer Architecture and Organization. Topics include computer evolution and performance issues, the computer systems including system buses, internal and external memory, input/output, and operating system support, CPU issues including computer arithmetic, instruction sets, addressing modes, RISC and superscalar organization, control unit issues, microprogramming, and parallel organization. Prerequisites: CS 333 and CS 431. Credit 3.

CS 563 NETWORKS AND DATA COMMUNICATIONS. An introduction to the basic techniques for interconnecting computers and peripherals for decentralized Computer. Network components, digital communications, interconnection architectures, communications protocols for geographic and local area networks and interprocess communications are covered. Prerequisite: CS 463. Credit 3.

CS 564 PROGRAMMING LANGUAGES. A comprehensive study of computer programming languages. Topics include: language design principles, formal grammars, procedural operating environment, language standardization, and language support for parallel and distributed programming. Language paradigms to be discussed will include procedural programming, logical programming, functional programming, and object-oriented programming. Prerequisite: CS 482. Credit 3.

CS 566 DATABASE SYSTEMS. A survey of contemporary topics in database systems. Topics include: relational database theory, database design issues, cryptography, security integrity issues, data recovery, concurrency problems, optimization, distributed database systems, the client/server model, object-oriented databases, stenography, data compression, data warehouse, data mining, logic/knowledge based systems, and other related topics. Prerequisite: CS 334. Credit 3.

CS 582 ARTIFICIAL INTELLIGENCE. A survey of topics in artificial intelligence. Topics include: history of AI, knowledge representation, knowledge acquisition, search techniques, control strategies, and AI languages. Applications include natural language processing, neural nets, and expert systems. Prerequisite: CS 362. Credit 3.

CS 583 NEURAL NETWORKS. An introduction into Neural Networks. Topics include discussion of variety of standard neural networks, with architecture, training algorithm, and applications; and development of neural network expert systems. Prerequisite: CS 362. Credit 3.

CS 584 DATA STRUCTURES. A number of important concepts and algorithms, with emphasis on correctness and effi ciency, are reviewed. The advanced treatment of sorting, searching, hashing, and dynamic storage management is provided. Advanced data structures, such as advanced tree structures, graphs, and networks, are introduced. Applications to distributed file structures, database management systems, internet/intronetworks are covered. Prerequisite: CS 362. Credit 3.

CS 593 MODELING THEORY. This course is a study of the use of analytical models as aids in the formulation and resolution of system alternatives. Emphasis is on problem defi nition, formulation and resolution using appropriate mathematical methodologies and analysis software packages. Prerequisites: MTH 379 and CS 477. Credit: 3.

CS 694 NUMERICAL ANALYSIS. Topics include solutions of equations, approximation and interpolation, numerical differentiation and integration, the fast Fourier transform, and numerical simulation. Also listed as MTH 694. Prerequisite: MTH/CS 394. Credit 3.

CS 698, 699 THESIS. Credit 3 hours for each course.