Three foreign language credits
- Subject
- CSC
- Credits (min)
- 3
- Credits (max)
- 3
- Credit unit
- Credit Hours
- Type
- course
- Edition
- 2026-2027
- Catalog
- 2026-2027 Undergraduate Catalog
- Source
- uncfsu.catalog.acalog.com
79 courses with the subject CSC, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Three foreign language credits
Lecture Hours: 3 Lab Hours: 0 This course is an introduction to FORTRAN, a problem-oriented computer language for use in scientific and mathematical problem solving.
Lecture Hours: 3 Lab Hours: 0 This course will introduce students to computing using Visual Basic with emphasis on business applications. The topics for the course will include Visual Basic programming, computer concepts in relation to management, the use of an Integrated Development Environment (IDE), flowcharts, algorithms, decision making, control structures, modules, windows programming, procedures and arrays.
Lecture Hours: 3 Lab Hours: 0 This course explores topics of computer science for non-technical majors. The course covers the fundamental issues of networking, HTML, data representations, computers, algorithms, and programming. Students receive a solid grounding in the central concepts as well as in important uses of computing and information technology.
or Any BICH, BIOL, BTCH, CHEM, FORS, MATH, MATS, PHYS, or STAT courses at the 200-level or above. Total Credit Hours: 120 Chemistry with a concentration in Materials Science, B.S. This degree option satisfies the requirements for certification by the American Chemical Society (ACS) and represents the minimum undergraduate preparation recommended by the ACS for professional chemists. This chemistry curriculum prepares students for careers as professional chemists and provides an excellent basis for further graduate and professional areas of study. Admission Requirements for the Bachelor of Science in Chemistry Students must have a grade of C or higher in CHEM 161 and a grade point average of 2.0 or better to be admitted to the program. Admission Criteria to Pre-Chemistry: 352
This course introduces the fundamentals of machine learning (ML) with a focus on applications in social sciences. Students will learn key concepts, methods, and tools for analyzing and interpreting data, enabling them to apply machine learning techniques to real-world social science problems. Key ML algorithms like learned decision trees, logistic regression, artificial neural networks will be introduced in this course. Students will carry out ML experiments with real datasets. The hands-on experiments will use low code software like Orange Data Mining, Weka, KNIME and Blockly-DS to increase student understanding of the typical elements of the ML workflow. Equipped with this understanding, the concepts of the Python programming language and supporting tools like programming notebooks and ML libraries in Python will be introduced so students are exposed to how ML software is developed in the industry.
Lecture Hours: 3 Lab Hours: 0 This course is the first course of a two-semester sequence that provides an overview of programming methodology and program writing skills. Topics include basic concepts of computer systems, problem solving and algorithm development, program structures, data types, program development, method and style, coding, debugging, testing, and documentation.
CSC 200 or higher MATH 142 or higher ISBA 300 or higher STAT 300 or higher Free Electives: 12 Credits Total Credit Hours: 120 Engineering 3+2 Dual Degree Program (Mathematics and Computer
Lecture Hours: 3 Lab Hours: 0 This course provides an overview of cyber ethics and its relation cybercrimes. Topics discussed include the ethical framework, ethical hacking, privacy, surveillance, piracy, cyber trespassing, cyber pornography, cyber deception, and cyber thief. The course also covers the investigation of cybercrimes, as well as a discussion of the relation among algorithms, big data, and policing.
764
Lecture Hours: 3 Lab Hours: 0 This course focuses on object oriented programming and software development strategies in C/C++ programming language. Material includes syntax and semantics of C/C++, memory management, file processing, and network programming. Directed projects in C/C++ are an integral part of the course.
Lecture Hours: 3 Lab Hours: 0 This course introduces IoT systems, architectures, standards & protocols, and IoT applications. Topics include IoT network architecture and design, smart objects, wireless communications, network and application protocols, big data and machine learning, and IoT security and applications. Students will learn how to develop and implement IoT technologies, solutions, and applications using electronics, microcontrollers, and single board computers.
Lecture Hours: 3 Lab Hours: 0 This course focuses on object-oriented programming and software development strategies. Material includes syntax and semantics of Java, file processing, and network programming. Directed projects in Java are an integral part of the course.
or
Lecture Hours: 3 Lab Hours: 0 This course introduces the basic concepts and methods of symbolic programming. Symbolic programming involves the construction and analysis of complex symbolic expressions that can be used to represent different types of information. This course also introduces functional programming and logic programming as two widely used paradigms for symbolic computation. Course topics include recursion, list processing, tree processing, backtracking, unification and resolution.
Lecture Hours: 3 Lab Hours: 0 This course involves how to develop and program graphical user-interfaces (GUI) using current programming toolkits and GUI tools.
Three credits of CSC electives at 200 level or above Total Credit Hours: 17
Lecture Hours: 2 Lab Hours: 0 This course is designed to cover contemporary topics of current interest and demands in cybersecurity. It may be used up to three times for a maximum of six credits. It may not be used more than once in the same semester.
Lecture Hours: 3 Lab Hours: 0 The course will provide students with comprehensive understanding of the client and enterprise technologies. This course consists of two modules. The first module, the client module, covers a range of computing devices in the product portfolio of a high-tech industry, the Pre-Boot environment, diagnostics, troubleshooting, the Common Industry Standard Client Technology, process of disassembly and reassembly. The second module, the Enterprise module, covers servers, networking, enterprise storage, and common industry standard Enterprise technology.
Lecture Hours: 3 Lab Hours: 0 This course introduces the architecture, structure, functions, components, and models of the Internet and other computer networks. It uses the OSI and TCP layered models to examine the nature and roles of protocols and services at the application, network, data link, and physical layers. The principles and structure of IP addressing and the fundamentals of Ethernet concepts, media, and operations are introduced to provide a foundation for the curriculum. Students build simple LAN topologies by applying basic principles of cabling, performing basic configurations of networks devices such as routers and switches, and implementing IP addressing schemes.
operation of routers, and explains the principles of routing and routing protocols. The course explains how to configure a switch for basic functionality and how to implement VLANs, VTP, and inter-VLAN routing in a converged network. Students analyze, configure, verify, and troubleshoot the primary routing protocols RIPv1, RIPv2, EIGRP, and OSPF. Students complete a basic procedural lab, followed by basic configuration, implementation, and troubleshooting labs in each chapter. Students learn how to select devices for each layer and develop the knowledge and skills necessary to implement a WLAN in a small-to-medium network.
Lecture Hours: 3 Lab Hours: 0 This course studies the security and threat models of mobile and web applications. Topics will include: mobile device platforms; Mobile Device Management (MDM) command injections including SQL injection and Format Strings and Buffer Overflows; communication protocols including non-secure and secure HTTP protocols - TLS/ SSL; digital signatures, certificates, and public-key infrastructure; OWASP Top 10 models; Windows, Android, and iOS security models.
Lecture Hours: 3 Lab Hours: 0 This course will provide in-depth coverage of threat models and malwares and virtual machines software systems. Topics will include: exploitation techniques including persistent and non-persistent threats and reverse social network engineering; password cracking including dictionary attacks; VMWare and malwares; countermeasures such as Firewalls, secure SSL, password salting, and sandboxing; HTML5; code review using static and behavioral analyses; Intrusion Detection Systems.
Lecture Hours: 3 Lab Hours: 0 747
Lecture Hours: 3 Lab Hours: 0 The course is a continuation of CSC 201 which covers many aspects of computer architecture and implementation. Topics covered include pipelining, instruction-level parallelism; advanced cache and memory-hierarchy design issues; design issues for shared memory multiprocessors; storage systems and design of input/output systems; architectural implications for networks and distributed systems. In this course a rigorous quantitative approach is taken to examine different system design tradeoffs.
Lecture Hours: 3 Lab Hours: 0 This course covers fundamental concepts of databases and spatial databases. Data models, building and querying databases, optimizing databases and interfaces for databases, will be discussed with the emphasis on spatial data. More advanced topics such as spatiotemporal databases and spatial reasoning will be also covered. Computer exercises will be done using a spatial database management system. The course will focus on the actual use of the material in the real-world settings with concentration on geospatial big data.
Lecture Hours: 3 Lab Hours: 0 This course focuses on computer techniques used to translate certain known computational algorithms into computer programs and on practice in use of existing mathematical library routines. Topics include linear systems of equations, curve fitting and interpolation algorithms for differentiation, solution of non-linear equations, solution of ordinary differential equations, and elementary discussion of errors. 748
Total Credit Hours: 18 399
Lecture Hours: 3 Lab Hours: 0 This course provides a comprehensive introduction to the design and analysis of computer algorithms. Design techniques include divide-and-conquer, the greedy approach, and dynamic programming. Algorithm analysis topics include asymptotic notation, recurrence relations, time-space complexity, Turing machines, and NP-completeness. Algorithms for sorting, searching, hashing, and advanced data structures are examined.
Lecture Hours: 3 Lab Hours: 0 This course considers the principal programming language concepts and shows how they are dealt with in 749
Lecture Hours: 3 Lab Hours: 0 This course emphasizes the concepts and structures necessary to design and implement a database management system. It will acquaint the students with current literature on the subject and give them an opportunity to use a database management system. Topics include database concepts, hierarchical, network and relational data models, data normalization, data description languages, query facilities, file organization, file security, data integrity, and reliability.
Lecture Hours: 3 Lab Hours: 0 This course covers basic theoretical principles embodied in formal languages, automata, computability, and computational complexity. Topics include finite automata, pushdown automata, non-determinism, regular expressions, context-free grammars, Turing machines, Gödel numbering, Church's thesis, the halting problem, unsolvability, and computational complexity.
Lecture Hours: 3 Lab Hours: 0 This course covers software development on a specific platform such as Unix. Topics include general utilities, shell programming, file processing and manipulation, text patterns, software management facilities, linkers, loaders, debuggers, compilers, editors, filters, and macro processing.
Lecture Hours: 2 Lab Hours: 0 750
357 rn credit if requirement is waived.
Lecture Hours: 3 Lab Hours: 0 This course introduces simulation and modeling of systems with concentration on discrete stochastic systems. Topics include modeling and simulation techniques, Monte Carlo methods, queuing models, and computer simulation languages such as GPSS, and SIMSCRIPT. A simulation project is developed, completed, and presented by each student as a member of a project team.
Lecture Hours: 3 Lab Hours: 0 This course describes the architecture, components, and operations of routers and switches in a small network. Students learn how to configure a router and a switch for basic functionality. By the end of this course, students will be able to configure and troubleshoot routers and switches and resolve common issues with virtual LANs and inter-VLAN routing in both IPv4 and IPv6 networks. 751
Lecture Hours: 3 Lab Hours: 0 This course describes the architecture, components, and operations of routers and switches in larger and more complex networks. Students learn how to configure routers and switches for advanced functionality. By the end of this course, students will be able to configure and troubleshoot routers and switches and resolve common issues with OSPF, EIGRP, and STP in both IPv4 and IPv6 networks. Students will also develop the knowledge and skills needed to implement a WLAN in a small-to-medium network.
Lecture Hours: 3 Lab Hours: 0 This course discusses the WAN technologies and network services required by converged applications in a complex network. The course enables students to understand the selection criteria of network devices and WAN technologies to meet network requirements. Students learn how to configure and troubleshoot network devices and resolve common issues with data link protocols. Students will also develop the knowledge and skills needed to implement virtual private network (VPN) operations in a complex network.
Lecture Hours: 3 Lab Hours: 0 This course is designed to cover contemporary topics of current interest and demands in computer and 752
Lecture Hours: 1 Lab Hours: 0 This course provides intensive training in programming towards solving particular problems in a timely fashion. The skills obtained will be very useful in the workplace as well as programming competitions. Topics include: mathematical/logical skills for problem solving; online programming references; restricted programming environments; and collaboration for problem solving.
Lecture Hours: 1 Lab Hours: 0 This course discusses the impact of computers on society including people, business, and government. Topics include historical and social issues, security, privacy, professional responsibilities, risks and liability, and intellectual property.
Lecture Hours: 3 Lab Hours: 0 This course provides students with the practical knowledge of methods for analyzing geospatial data, which in the age of big data is produced with increasingly high variety, volume and velocity. This course will cover applications of data analytic techniques to geospatial applications such as remote sensing; networking and computing models such as virtualization and cloud computing, structured and unstructured geospatial data, machine learning algorithms for clustering and classification of data, discovery of patterns through data mining, and visualization of geospatial data. The course will also cover concepts of spatial data mining to learn about techniques to find spatial patterns.
Lecture Hours: 3 Lab Hours: 0 753
This course is a continuation of CSC 310. Topics covered include interactive techniques in matrix algebra, numerical solutions of nonlinear systems of equations, initial-value problems for ordinary differential equations, boundary-value problems for ordinary differential equations, and numerical solutions to partial-differential equations.
This course considers the principal programming language concepts and shows how they are dealt with in the design and implementation of traditional imperative languages, functional languages, logic languages, and object- oriented languages. Topics include history, virtual machines, representation of data types, sequence control, data control, data sharing, data type checking, run-time storage management, and distributed and parallel programming constructs. Prerequisites: CSC 201, CSC 220, MATH 250, CSC 206, and CSC 207.
Lecture Hours: 3 Lab Hours: 0 This course is designed to develop an understanding of the organization and architecture of computer systems at the register-transfer and programming levels of system description. Major concept areas of operating systems principles 722
Lecture Hours: 3 Lab Hours: 0 This course provides for the study of advanced software techniques, especially focusing on operating systems. It presents materials that will enable the student to design, use, and analyze current and future operating systems. Topics include I/O programming, interrupt programming, memory management, processor management, device management, design and implementation of operating systems.
or
Lecture Hours: 3 Lab Hours: 0 This course introduces hardware and software components of graphics systems, and graphic application programming. Programs to deepen understanding of interactive graphics, picture plotting and input handling in an integrated manner will be written. Topics include geometrical transformation, three- dimensional concepts, windowing, clipping, segmentation, logical interaction input methods, raster algorithms, algorithms for hidden surface and hidden line removal, and shading and color.
This course is devoted to a graphics project in which the student will treat fundamental techniques for representing three-dimensional surfaces in making realistic pictures of three-dimensional objects with the help of raster graphics techniques. Prerequisite: CSC 451.
This course introduces simulation and modeling of systems with concentration on discrete stochastic systems. Topics include modeling and simulation techniques, Monte Carlo methods, queuing models, and computer simulation languages such as GPSS, and SIMSCRIPT. A simulation project is developed, completed, and presented by each student as a member of a project team. Prerequisites: Proficiency in a programming language and STAT 301.
Lecture Hours: 3 Lab Hours: 0 This course introduces students to an intensive study of writing large programs, program design and programming style, and object-oriented development techniques in an attempt to manage the complexity of large software systems. Topics include principles of software engineering, object-oriented development, systems development, programming support environments, and software life cycles.
Lecture Hours: 3 Lab Hours: 0 This course introduces the fundamental theory and common practices of software security throughout the software development life cycle. It covers analysis and design principles of building secure software, as well as implementation techniques and tools to identify and eliminate potential software security threats. It emphasizes the importance of development processes and methodologies and programming practices that lead to secure software. The course also introduces risk analysis and management and software security testing. 755
Lecture Hours: 3 Lab Hours: 0 This course gives the students basic knowledge about parallel processing. Topics include hardware architecture of parallel machines; software environment that enables parallel computing; performance analysis of parallel algorithms; techniques for developing parallel algorithms; and case studies on typical parallel algorithms.
Lecture Hours: 3 Lab Hours: 0 A course on user-interface technology and human-computer interaction issues including user productivity, system habitability, abstraction barriers, and human factors. Topics include command languages, hierarchical menus, direct manipulation (graphical user interfaces), multimedia interfaces, multimodal interaction, and user interface management systems.
Lecture Hours: 3 Lab Hours: 0 This course is the second of two Capstone Project courses. This course is based on using skills and 756
Lecture Hours: 3 Lab Hours: 0 This course presents a formal approach to state-of-the-art techniques in geospatial data analytics and provides means for students to apply the techniques. An integral part of the course is the involvement of students working in teams in the organization, management, and development of a large project. Upon completion of this course, students will be able to comprehend the key knowledge of geospatial data analytics and allow for the integration and reflection on previous knowledge. Students will demonstrate their advanced abilities and skills in solving geospatial data analytics problems and will be able to use current techniques, skills, and tools to analyze, design, and implement computing systems for real-world cases.
Three credits from CSC 200 or higher 376 credit if requirement is waived.
science not covered in other computer courses, such as software, hardware utilization, programming languages, numerical methods, syntactic descriptions, symbolic functions, and manipulations, with course requirements including one or more of the following: readings in the literature and research on computer science, introductory research projects, major computer programming projects, seminars, or new course development. Prerequisite: CSC 101 or consent of the instructor.
Lecture Hours: 3 Lab Hours: 0 This course is designed to provide a combination of theoretical knowledge and practical, hands-on experience in solving real-world problems in data science through the application of machine learning and data mining. Topics include classification algorithms such as decision tree induction and support vector machines; cluster analysis with the k-means algorithm and hierarchical methods; outlier detection; mining of complex data types such as graphs and networks. This course also includes the recent and emerging area of deep learning, where it discusses architectures like convolutional and recurrent neural networks that have been designed to solve different classes of problems in computer vision, natural language processing and other areas. The course uses Python as the primary language, R as a secondary language, and introduces software tools that have been standardized for industrial applications of data science.
Lecture Hours: 3 Lab Hours: 0 This course is for student to understand big data and its impact on the society and business. The students will gain insight by doing hands-on exercises with tools and systems in big data. Students will learn the 184
Source: Fayetteville State University's catalog, linked per course · table learning_unit · CourseShelf publish 61