Master of Science in Computer Science (MSCS)
The Master of Science in Computer Science (MSCS) at Glenmore University is a fully online, 36-credit graduate program designed to develop advanced technical skills, innovative thinking, and leadership in software engineering, artificial intelligence, cybersecurity, and data analytics. The program is structured to meet the evolving demands of the global technology sector, preparing graduates for leadership roles in high-tech industries and research organizations.
Students build expertise through a rigorous curriculum that integrates theoretical foundations with hands-on application. The program culminates in a capstone project that challenges students to synthesize knowledge and propose solutions to real-world computing problems.
Program Structure
● Total Credit Hours: 36
● Course Format: 11 Core Courses (3 credit hours each) and 1 Capstone Course (3 credit hours)
● Delivery Method: 100% online
● Program Completion: Typically completed in 12-18 months for full-time students
● Degree Awarded: Master of Science
Core Courses
● SSE501 – Software Engineering
● SSE502 – Advanced Computer Architecture
● SSE503 – Design and Analysis of Algorithms
● OSS504 – Distributed Operating System Principles
● OSS505 – Network Security
● OSS506 – Cybersecurity Risk Management
● AID507 – Machine Learning
● AID508 – Big Data Analytics
● AID509 – Introduction to Artificial Intelligence
● ETC510 – Cutting-Edge Technologies
● ETC511 – Cryptography
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Capstone Course
The program concludes with a 3-credit Capstone course. Students work independently or in small teams to solve a complex, real-world computing challenge. The capstone integrates skills in project design, data analysis, secure systems, and strategic technology deployment.
Program Learning Outcomes
Graduates of the MS in Computer Science program will be able to:
● Apply computational theories, advanced algorithms, and data structures to design, analyze, and optimize software solutions.
● Utilize modern computing frameworks, cloud-based architectures, and distributed systems to develop scalable applications.
● Lead software and IT projects by managing teams, timelines, and technical implementation in cross-functional environments.
● Explore and apply emerging technologies such as AI, machine learning, and big data to drive innovation.
● Implement cybersecurity best practices and cryptographic protocols to maintain secure enterprise systems.
● Leverage machine learning tools and data science methods to develop automation and extract actionable insights.
● Design enterprise-level applications using agile methodologies and user-centered development models.
● Communicate complex technical information effectively to technical and non-technical stakeholders.
● Collaborate in research and knowledge-sharing initiatives across disciplines and industries.
● Integrate ethical frameworks into the design and deployment of computing technologies.
● Demonstrate commitment to lifelong learning and continuous improvement through professional development.
● Evaluate complex computing problems using analytical, systematic, and interdisciplinary approaches.
These outcomes ensure that graduates are prepared to meet the challenges of modern technology environments, contribute to scientific and applied computing advancements, and maintain ethical leadership in the digital economy.