MASTER OF SCIENCE IN
APPLIED COMPUTER SCIENCE
General Requirements for degree Program
The Department of Mathematics and Computer Science offers a Master of Science (Thesis or Non-thesis option) in Applied Computer Science. This program provides advanced understanding, knowledge and research opportunities related to computer science that prepare the students for applying their skills in business, industry, academia, government agencies, consultancy, etc. The primary emphasis is reflective of current theoretical and applied computing in multi-disciplinary applications.
The graduate program in Applied Computer Science (APSC) culminates in a Master of Science (M.S) degree. It is designed to meet the needs of students such as:
- Those who are already in the workforce and wish to update or improve their knowledge of current computer science (many of these students will have completed their undergraduate or graduate work in fields other than computer science); and
- Those who have recently completed an undergraduate degree program and wish to enhance their interests, understanding and research opportunities in Computer Science and its application in multi-disciplines or with specific focus.
Admission to this program adheres to the general criteria for admission to the UMES Graduate School. The general GRE is also required but, in view of the wide variety of fields from which students come, the Computer Science subject-matter GRE is not employed in admission consideration for Applied Computer Science.
Regular admission: Students with undergraduate (in computer science related areas) GPA s of at least 2.5 on a 4.0 scale and
Provisional Admission: those students with weaker preparation in Computer Science may be offered Provisional Admission. This status is usually changed to Regular Admission as soon as the student completes prerequisite requirements and/or achieves a 3.0 or better cumulative GPA. Students whose computing background is weak may be directed to take undergraduate computer science or mathematics courses as a condition for entering the graduate program. Courses required for Regular Admission must be completed as early as possible, generally within the first year.
In addition to the University of Maryland Eastern Shore s general admission requirements, applicants must have:
- A bachelor s degree in Computer Science or Information Sciences or a bachelor s degree and specified background course determined by the Department Graduate Committee
- An undergraduate GPA of at least 2.5 on a 4.0 scale for Provisional admission, which may also be conditional on some course requirements (of computer science or mathematics) to be met, determined by the Departmental Graduate Committee.
Admission to the program is determined by the Departmental Graduate Committee.
Course Requirements for Graduation
Student must earn a minimum of thirty (30) credit hours with Thesis option or thirty three (33) with Non-thesis option to graduate from the program, not including any Provisional Admission course requirements. All courses that are to count towards graduation must be passed with a minimum grade of B , and students must also pass at least five of these courses with a grade of A .
The time limit for completing the M.S. degree is five years from the first enrollment in the graduate program. This includes any Provisional Admission course requirements to be met. Any exception to the time limit must be approved by the UMES Graduate School.
In the Thesis option, the student is required to take a minimum of eight graduate level courses (24 credits) and six credits for Thesis (CSDP699). The thesis must be supervised by a member of the graduate faculty member as a thesis advisor and the initial thesis proposal must be defended with an oral presentation (see below) and approved by student s thesis committee (three members including advisor). The thesis must be submitted to the department in a bound form after its defense orally which will take place after the thesis research is completed. A student is required to submit at least one journal/conference paper from his/her thesis work before its defense.
In the Non-thesis option, the student is required to take a minimum of ten graduate level courses (30 credits) and a 3-credit hour research project (CSDP698) that must be approved by the project advisor. A copy of the resulting scholarly paper (if any) must be submitted to the department. A Student is advised to do scholarly activity out of his/her project work.
MS in Applied Computer Science Computer Science Thesis/Project Policy
Cooperative Education Program Internship
Students seeking internship in industry are required to take CSDP 698 (Master s project) or CSDP 699 (Master s thesis), and all required lecture courses. Students must register for the appropriate cooperative education credit to undertake the internship. The internship should provide learning experience in computer applications useful to strengthen the Master s project or Master s thesis.
Frank C Lin Award:
Requirements: Graduate student is
Not a US Citizen
Majoring in Applied Computer Science
A minimum of 3.0 GPA (please attach a copy of your transcript)
Must demonstrate student leadership ability
Computing Resources: The Department has a Sun Lab consisting of 21 Sun Blade 150 workstations and Sun V1280 server and two Computer Laboratories consisting of high-end Pentium computers. Users have access to a wide variety of Windows and UNIX Microcomputers, plus special purpose facilities for graphics. These computer facilities and several other campus wide computer facilities are available for all students.
Students in both the undergraduate and graduate Computer Science courses benefit from the wide variety of computing resources made available at the University of Maryland Eastern Shore as a member of the University System of Maryland. Both Unix-based and Windows-based systems provide a rich computing environment both for majors and for students in service courses.
Library Facilities:Library facilities are extensive and are supplemented each year. Opportunities exist for student participation in faculty research projects. While computer laboratory facilities are open and available all day and evening, most graduate courses are scheduled in the early evening so that those working during the day can participate.
MS in Applied Computer Science
I Core computer science courses:
CSDP 600 Advanced programming languages, 3 credits
Topics include (not limited to); Advanced topics in programming language theory, design and implementation, in depth understanding of data types, binding, scope and extent, abstraction, extensibility and control mechanisms, formal semantics and program verification, alternative programming language paradigms
CSDP 601 Analysis and design of algorithms, 3 credits
Topics include (not limited to); NP completeness and approximation algorithms, design techniques for efficient algorithms such as amortized analysis, dynamic programming and greedy algorithms. Computational geometry, graph algorithms, primality and other number-theoretic algorithms, specialized data structure techniques such as augmenting data structures, combinational graph reduction and functional repetition
CSDP 602 Database Management System, 3 credits
Topics include (not limited to); A study of the theoretical foundations of database management systems. Design and implementation of alternatives for various database models, including, but not limited to, hierarchical network and relational models, comparison of the reliability, security and integrity of various database systems. Implementation of simple database system.
CSDP 603 Advanced Operating system, 3 credits
Topics include (not limited to); Structure and functions of operating system, inter-process communication techniques, high-level concurrent programming, virtual memory system, basic queuing theory, security, distributed system, design and implementation of operating systems.
CSDP 605 Software engineering, 3 credits
Topics include (not limited to); A formal study of the software development process, lifecycle models, requirements definition specifications, design, implementation, validation, verification, maintenance and reuse, team work on a project.
II Elective courses:
Select any three for Thesis option and five for Non-thesis option
CSDP 606 Artificial Intelligence (AI), 3 credits
Topics include (not limited to); Principles of knowledge-based search techniques, automatic deduction, knowledge representation using predicate logic, semantic networks, connectionist networks, frames, rules, applications in problem solving, expert systems, game playing, vision, natural language understanding, learning, robotics, LISP/PROLOG programming
CSDP610 Parallel Computing, 3 credits
Topics include (not limited to); Motivation for parallel computation and survey of different models. Fundamental techniques used in parallel algorithms. Implementation on parallel machines and simulation on clusters of workstations. Distributed computed versus parallel computing model for distributed computing, examples of distributed programming environment.
CSDP 611 Theory of computation, 3 credits
Topics include (not limited to); Grammars, automata, Turing machines, decidability and complexity, language hierarchies, normal forms, NP completeness and reducibility with applications from various areas of computer science
CSDP 612 Advanced Software Engg:
Topics include (not limited to); A formal study of selected aspects of contemporary software development methodology, definition of user requirement, formal specification of solutions, design and implementation techniques, validation and testing, verification, maintenance and reuse.
CSDP 613 Computer security, 3 credits
Topics include (not limited to); Computers and network security, Public-key and private-key cryptography, authentication, digital signatures, key exchanges, key management, certification authorities, distributed trust models, file system security, mail system security, and web security, intruders, Trojan-horse, viruses, covert channels, projects involve the use of available security tools.
CSDP 614 Trusted Computing Technology, 3 credits
Topics include (not limited to); Introduction to modular arithmetic, RSA, El-Gamal, Diffie-Hellman and Blue-Blum-Shub public key cryptosystems, authentication and digital signatures, anonymity protocols, protocol failures
CSDP 615 Bioinformatics, 3 credits
Topics include (not limited to); The primary objective of this course is to expose students to the computational methods and software tools often used in bioinformatics research. Bioinformatics is a new research field where computational models and methods are developed to analyze and interpret biological data and systems. Major topics in this course include sequence alignment and analysis, gene structure and prediction, motif recognition, structure modeling and prediction for RNA and protein sequences, protein identification in proteomics, and haplotyping and phylogenetic trees. The applications of machine learning and data mining algorithms in bioinformatics will also be introduced and studied.
CSDP 616 Data mining security, 3 credits
Topics include (not limited to); Introduction to principles and implementation techniques for information security and privacy in data mining. . It deals with techniques used to automatically search large volumes of data for patterns to detect security breaches, fraud, tampering, etc. using tools such as classification, association rule mining, and clustering. Techniques will be compared and application areas for the techniques will be addressed. Other topics include security vulnerabilities and privacy breaches, design of defensive countermeasure and privacy-preserving mechanisms.
CSDP 618 Advanced Graphics, 3 credits
Topics include (not limited to); Introduce to advanced raster graphics architecture, advanced geometric and raster algorithms, advanced 3D modeling and rendering techniques, advanced animation, virtual reality techniques, advanced image transformation such as image warping and morphing techniques, and the advanced applications such as motion capture in movie industry, existing and emerging standard for 3D model storage and compression, image synthesis and scientific visualization.
CSDP 619 Artificial neural networks, 3 credits
Topics include (not limited to); Introductions to various aspects of artificial neural networks, with emphasis on elements of design of trainable systems. Topics include linear and nonlinear neurons, multi-layer networks, back-propagation algorithms, unsupervised learning algorithms, Hopfield networks, and advanced neural network architectures and training algorithms.
CSDP 620 Secured E-commerce, 3 credits
Topics include (not limited to); Introduced to the concepts and issues of electronic commerce. Topics include comparison of e-commerce procedures, payment mechanisms, applications in different industry sectors, security, the challenges of starting and maintaining an electronic business site, as well as a comparison with traditional business practices. Students create an e-commerce Web site using such tools as Dreamweaver.
CSDP 651 Computer Architecture, 3 credits
Topics include (not limited to); Structure of computer system using processors, memories, input/output devices as building blocks, computer system instruction set design and implementation including memory hierarchies, microprogramming, pipelining and multiprocessors, issues and tradeoffs involved in the design of computer system architectures with respect to the design of instruction sets, applications of hardware description language (HDL) in the design of computer systems.
CSDP 668 Advanced topics in databases, 3 credits
Topics include (not limited to); Parallel and distributed database system architectures, distributed database design, client/server database systems, selected topics from new development in extended relational databases, multimedia databases, information retrieval systems, object-oriented databases, temporal databases
CSDP 604 Computers Methods in Statistics, 3 credits
Topics include (not limited to); principles and applications of probability and statistics needed in graduate studies in various academic areas and to the computer realization of these methods. Review of basic statistical principles.
Pre-requisite: one semester of calculus
NOTE: The department is committed to offer CSDP 604 (Computers in Statistics) as an elective course on demand.
CSDP 697 Special Topic course, 3 credits
CSDP 698 Master s Project, 3 credits
CSDP 699 Master s Thesis, 6 credits in at least two semesters, 3 credits each
For further information on this program,
Graduate Program Coordinator (APCS)
Dept. of Mathematics and Computer Science
University of Maryland Eastern Shore
Princess Anne, MD 21853