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CSCI A225: Web Development
3.00 Credits
University of South Carolina-Aiken
(Prereq: CSCI A145 with a grade of C or better). This course is an introduction to web development. The course will explore prevailing technologies in three main components of web application: client, server, and data. It will also provide practical experiences with large-scale interactive website development.
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CSCI A240: Intro to Software Engineering
3.00 Credits
University of South Carolina-Aiken
(Prereq: Grade of C or better in CSCI A146) This course introduces basic knowledge about software engineering, including principles and techniques of software design, software construction, software testing, and software maintenance.
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CSCI A255: Intro. to Information Security
3.00 Credits
University of South Carolina-Aiken
(Prereq: CSCI A125 with a grade of C or better). This course is an introduction to basic security concepts and principles of information security. Topics will include history of information security; overview of system security, software security, and network security; and security management.
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CSCI A275: Physics Engine Integration
3.00 Credits
University of South Carolina-Aiken
(Prereq: CSCI A146 with a grade of C or better) This course is an introduction course to gaming physics engines. It provides fundamental techniques for students to integrate and use commercial 3D physics engines in game development. Topics focus on virtual simulation of physics systems, such as gravity, collision, fabric movement, wind, etc.
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CSCI A285: Introduction to Cryptography
3.00 Credits
University of South Carolina-Aiken
(Prereq: grade of C or better in CSCI A146, CSCI A255, MATH A122, and MATH A174) This course introduces fundamental topics in cryptography, including symmetric cryptography, historical ciphers, the data encryption standard, the advanced encryption standard, and asymmetric cryptography. It also covers topics in number theory for public-key cryptography, the RSA cryptosystem, and the RSA digital signature scheme.
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CSCI A320: Object-Oriented Programming
3.00 Credits
University of South Carolina-Aiken
(Prereq: Grade of C or better in CSCI A145) An in-depth study using the object-oriented programming paradigm. Topics include: abstraction, encapsulation, information hiding, classes, inheritance, polymorphism, and the object-oriented programming applications for 2-D graphics, user interface design events, exceptions, multithreading, multimedia, and animations.
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CSCI A325: System-level Programming
3.00 Credits
University of South Carolina-Aiken
(Prereq: CSCI A220) The course is designed to help students with no prior exposure to system programming. System programming refers to writing code that prioritizes operating system support for programmers. The course will cover basic concepts of system programming, system programming language(s), and application of those language(s) to system-level problems. The course will focus on programming constructs that are closely aligned with the architecture of a digital computer: shells and scripting; system-level programming in the C/C++ language; and software development tools and techniques.
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CSCI A330: Programming Lang Structures
3.00 Credits
University of South Carolina-Aiken
(Prereq: Grade of C or better in CSCI A220) Formal specification syntax and semantics; structure of algorithms; list processing and string manipulation languages; statement types, control structures, and interfacing procedures.
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CSCI A340: Mobile Application Development
3.00 Credits
University of South Carolina-Aiken
(Prereq: Grade of C or better in CSCI A146 or consent of the Department) The study of the basic concepts and programming skills of mobile computing. Topics include: user interfaces, layouts, events and event handling, graphics, imaages, animations, multimedia, 2D game development, data persistence, and networking.
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CSCI A345: Data Mining & Machine Learning
3.00 Credits
University of South Carolina-Aiken
(Prereq: CSCI A165, MATH A344, CSCI A220, and STAT A509 with a grade of C or better) This course teaches the basic concepts in data mining and machine learning, algorithms on classification, clustering, regression, and the implementations of those algorithms.
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