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Course Criteria
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3.00 Credits
This course is an elective course to provide the students subtle skills in cyber security. This course will have 24 hands-on labs. They cover common network attacks, applications of information security concepts, hands-on security assessments of wired and wireless networks, web applications and intrusions, countermeasures to attacks, lifecycle of incident response, real work case studies, several pen test tools, mobile hacking, mobile forensics tools Santoku and Drozer. Variable.Credit, three hours.
Prerequisite:
CSCI 263 OR MTSC 313
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3.00 Credits
Students entering this course are expected to be familiar with high-level procedural language such as Java or C/C++, and a scripting language such as Python. Students must have the mathematical maturity to be able to model and implement mathematical expressions in software.The main topics of this course include: the representation, manipulation, visualization, analysis, and presentation of data.
Prerequisite:
CSCI 263 OR INFO 280 OR ENGR 220
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3.00 Credits
Introduction of the most important and basic concepts, methods, and tools used in bioinformatics such as bioinformatics databases, sequence and structure alignment, protein structure prediction, protein folding, protein-protein interaction.
Prerequisite:
CSCI 340
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3.00 Credits
This course is designed to cover a variety of important topics related to application of stochastic methods in computer science. The course includes theoretical principles necessary to understand use of stochastic methods, including notions of probability, distributions and statistical estimation and testing. The course emphasizes the practical aspects of stochastic methods in fields such as networking and pattern recognition. Mathematical details are covered to a minimal extent, needed to support the main ideas of the introduced algorithms. The students will be provided with hands-on experience in programming stochastic techniques in languages such as C/C++ and Matlab, and overview of statistical software such as SAS and SPSS.
Prerequisite:
MTSC 251 AND MTSC 213
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3.00 Credits
The goals of this course are to provide introductions to event driven programming, game engine scripting, interactivity, animation, sound, resource management, constraints, networking capabilities, artificial intelligence and physics for games, and game development tools.The premise for this course is that students learn by doing. Students are expected to design, implement, test and debug game programs.
Prerequisite:
CSCI 211
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3.00 Credits
This course introduces programming concepts in rendering of graphics primitives, shading, lighting, geometric transformations, clipping, depth, ray tracing, texture mapping and ant aliasing, interaction, perspective, and stereo viewing.
Prerequisite:
CSCI 211 AND MTSC 313
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3.00 Credits
This course is a formal comparative study of programming languages and concentrates on syntactic and semantic issues in the design and implementation of a programming language. Topics include regular expressions, Backus-Naur Form, grammars, parse trees, lexical analysis, parsing, overview of families of programming languages, introduction to functional languages, scopes, variables, types, selection statements, iterative statements, overview of object-oriented programming, trade-offs in the design and implementation of languages.Prerequisite: CSCI-211. Credits, three hours.
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3.00 Credits
The purpose of this course is to introduce students to fundamentals of parallel computing. The course provides an overview of parallel programming models and architectures, as well as the principles of parallel algorithm design and analysis.
Prerequisite:
CSCI 211 AND CSCI 380
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3.00 Credits
Students entering this course are expected to be familiar with the basic concepts of operating systems and computer networks.The knowledge of digital forensics has become essential in securing today's network-centric computing environment. This course will give the students both the fundamental knowledge and hands-on practice on digital forensics. The added exposure to forensics will enhance the marketability of our students and serve the students who carry the skills and knowledge forward into their future careers.Upon completing this course, the students are expected to understand the basics of digital forensics, to be well-trained as next-generation computer crime investigators, and to be prepared for active professional development at the forefront of these areas.
Prerequisite:
CSCI 320 AND CSCI 330 AND INFO 340
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3.00 Credits
The purpose of this course is to introduce students to fundamentals of data mining (DM) and knowledge discovery in databases (KDD). In addition to covering such topics as data types and other characteristics, data quality and pre-processing, basic statistical data analysis, frequent patterns and associations, classification and prediction, and cluster analysis, special emphasis will be placed on integration of database technologies with algorithms for efficient and non-trivial querying.
Prerequisite:
CSCI 340 AND CSCI 370
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