Master of Science in Computer Science
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Major Core Courses
CST 7080 | Cloud System Design (3 Credits)
This course provides students with the knowledge and skills needed to design and implement cloud-based solutions for complex business systems. Topics covered include cloud deployment models, virtualization, containerization, and cloud architecture.
CST 7090 | Advanced Network Design and Deployment (3 Credits)
The Advanced Network, Design, and Deployment class is a graduate-level course exploring advanced network design and deployment principles and techniques. This course focuses on advanced topics such as network architecture, design, security, and management. Students will learn about the latest trends and best practices in network design and deployment, including emerging technologies, software-defined networking, cloud computing, and virtualization. Through hands-on assignments and projects, students will gain practical experience in designing and deplying enterprise-level networks, configuring network security, and managing network operations. Upon completing this course, students will deeply understand advanced network design and deploy large-scale, high-performance networks in real-world environments. This course is ideal for students who want to specialize in network design and deployment or pursue careers in network engineering, network architecture, or network operations.
CST 7100 | Scalable Computer Architecture (3 Credits)
This course thoroughly studies computer architecture principles and techniques for building scalable and high-performance computing systems. The course covers various topics such as parallelism, memory hierarchy, interconnects, and techniques for designing scalable computer systems that can handle large-scale workloads. The course also covers emerging trends and challenges in scalable computing, such as big data analytics, machine learning, and cloud computing.
CST 7300 | Advanced Systems Analysis and Design (3 Credits)
In the Advanced Systems Analysis and Design course, student will delve into the complexities of the systems development life cycle, mastering advanced methodologies and techniques to design, analyze, and implement complex information systems. Building on foundational principles, the course aims to equip students with the hands-on experience and theoretical knowledge to tackle real-world projects. Using tools and frameworks, students will use case studies, collaborative projects, and simulations to design and analyze system architectures, workflows, and interfaces. By the end of this comprehensive course, students will be proficient in advanced system analysis techniques, design methodologies, and the latest industry best practices, making them well-suited for leadership roles in systems development projects.
CST 7500 | IT/IS Project Management (3 Credits)
The IT/IS Project Management course is designed to equip students with the skills and methodologies to effectively plan, execute, and oversee information technology and systems projects. Drawing from established project management frameworks such as Agile and Waterfall, the course emphasizes a holistic approach, covering key areas, including scope definition, timeline scheduling, resource allocation, risk assessment, and stakeholder communication. Through theoretical instruction and practical case studies, students will learn how to manage complex projects in fast-paced, technology-driven environments, ensuring alignment with organizational goals and compliance with indsutry standards. By the end of the course, students will be well-versed in the tools and techniques of IT/IS project management, ready to lead projects to successful completion.
CST 7600 | Ethics in Computer Science (3 Credits)
This course explores ethical principles, legal frameworks, and social responsibilities in computer science. Students will examine the impact of technology on society, evaluate the ethical implications of emerging technologies, and learn to apply decision-making frameworks to address ethical challenges. Topics include data privacy, artificial intelligence ethics, bias in algorithms, intellectual property, and professional conduct. The course emphasizes critical thinking and responsible computing practices.
CST 7010 | Advanced Computer Science Topics (3 Credits)
This course delves into cutting-edge topics and emerging trends in computer science, including artificial intelligence, machine learning, computer vision, natural language processing, big data, and cloud computing. Students will gain an understanding of advanced algorithms, techniques for solving complex computational problems, and modern tools and technologies used in the industry. The course also covers the critical analysis of research papers and the practical application of knowledge to real-world problems.
AI and Machine Learning Specialization
CST 7020 | Programming Language Logic (3 Credits)
This fundamental course focuses on programming language logic, including the syntax and semantics of programming languages, the principles of programming language design, and program correctness. Students will learn to reason about programs, write programs that meet certain specifications, and formally verify program correctness. Topics covered include propositional and predicate logic, operational semantics, type theory, and program verification.
CST 7030 | Analysis of Algorithms and Computation (3 Credits)
Students will delve into the foundational principles of algorithmic problem-solving and computational efficiency. The course explores a range of algorithms, from sorting and searching to graph traversal and dynamic programming while emphasizing analytical techniques for evaluating their time and space complexities. Students will gain hands-on experience designing, implemening, and critically assessing algorithms for various problems. The course introduces students to advanced topics such as NP-completeness and randomized algorithms using theoretical and practical approaches. Ideal for computer science majors and professional interested in enhancing their programming skills, this course equips students with the computational thinking and analytical capabilities essential for solving complex problems in diverse domains.
CST 7040 | Fundamentals of AI and Machine Learning (3 Credits)
This course introduces students to the theory and practice of machine learning, covering the basic concepts and techniques of supervised and unsupervised learning, including decision trees, neural networks, clustering, and regression. Students will learn how to apply these techniques to real-world problems in various fields, such as natural language processing, computer vision, and data mining. The course also covers ethical considerations and the potential societal impacts of machine learning.
CST 7050 | Neural Machine Learning and Data Mining (3 Credits)
This class provides an in-depth understanding of the concepts, techniques, and algorithms used in modern neural machine learning and data mining applications. Topics covered include deep learning, convolutional neural networks, recurrent neural networks, natural language processing, and data mining algorithms. Students will also gain experience working with tools and frameworks such as TensorFlow, Keras, and PyTorch.
CST 7060 | Data Science for Business Intelligence (3 Credits)
This course covers the fundamentals of data analysis and how to use data to drive business intelligence. Students will learn how to use data mining techniques, statistical analysis, and data visualization tools to identify trends, patterns, and insights that can help organizations make better decisions. The course also covers data cleaning, preprocessing, and integration.
Cybersecurity Specialization
CST 7070 | Information Insurance and Cybersecurity (3 Credits)
This course provides an in-depth study of cryptography and computer security's theoretical and practical aspects. Topics covered include classical and modern cryptographic systems, encryption and decryption techniques, authentication, secure communication protocols, digital signatures, access control, security models and policies, and vulnerability assessment and management. Students will also explore emerging trends in cryptography and computer security.
CST 7110 | Cyber Forensics and Incident Response (3 Credits)
Cyber Forensics and Incident Response course is designed to provide students with advanced knowledge and skills in digital forensics and incident response. Students will learn to investigate cyber incidents, identify evidence, and preserve digital artifacts. They will also learn to analyze digital evidence and report their findings clearly and concisely. Topics covered in the course include digital forensics methodologies, forensic acquisition and analysis of electronic data, network forensics, mobile device forensics, malware analysis, incident reponse procedures, and legal and ethical considerations in cyber investigations. Students will also be able to work with industry-standard tools and technologies used in digital forensics and incident response. Upon completing this course, students will be equipped with the necessary knowledge and skills to pursue careers in the rapidly growing field of cybersecurity, with a focus on digital forensics and incident response.
CST 7203 | Cybersecurity Risk Management and Assessment (3 Credits)
This advanced course in Cybersecurity Risk Management and Assessment provides students with comprehensive knowledge and practical skills to identify, analyze, and mitigate cybersecuirty risks in complex organizational environments. The course covers key concepts of risk management frameworks, threat modeling, vulnerability assessment, and risk mitigation strategies. Students will learn to develop and implement effective cybersecurity policies, conduct risk assessments, and create incident response plans. The course emphasizes the application of risk management principles to real-world scenarios, preparing students to making informed decisions to protect critical assets and information systems.
CST 7220 | Cybersecuirty for the Internet of Things, Artificial Intelligence, Cryptocurrency (3 Credits)
This course explores the cybersecurity aspects of Internet of Things (IoT) devices, artificial intelligence, (AI) systems, and cryptocurrency technologies. Students will gain a deep understanding of the security vulnerabilities, threats, and best practices associated with these interconnected domains. The curriculum covers IoT architecture and protocols, AI-driven security solutions, blockchain technology, and cryptocurrency security measures. Through hands-on projects and case studies. students will develop practical skills in securing IoT ecosystems, implementing AI-based threat detection, and safeguarding cryptocurrency transactions.
CST 7240 | Cloud Security: Advanced Concepts and Strategies (3 Credits)
This graduate-level course provides an in-depth exploration of cloud security principles, challenges, and solutions in modern computing environments. Students will develop a comprehensive understanding of risk management, secure cloud architecture, DevSecOps practices, and security assessment techniques specific to cloud computing.
PLUS
*12 Terms of Executive Practicum courses equivalent to 6 hours.*
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