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
SWE601 – Software Engineering
CSC602 – Advanced Computer Architecture
AIM610 – Introduction to Artificial Intelligence
CSC603 – Design and Analysis of Algorithms
CSC604 – Distributed Operating System Principles
CYB620 – Network Security
CYB630 – Cybersecurity Risk Management
AIM620 – Machine Learning
DAT620 – Big Data Analytics
CSC630 – Cutting Edge Technologies
CYB611 – Cryptography
CSC699 - Capstone Project
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:
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design efficient and scalable computing solutions using advanced algorithms, data structures, and software engineering principles;
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analyze cybersecurity risks and system vulnerabilities and apply appropriate security controls to protect digital and cloud-based infrastructures;
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develop artificial intelligence and machine learning solutions that address real-world computational and decision-making problems;
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apply data analytics, big data processing, and visualization techniques to extract insights and support evidence-based decision-making;
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implement distributed, parallel, and cloud-based systems that support scalable, reliable, and high-performance computing environments; and
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demonstrate effective communication, teamwork, and project coordination skills in collaborative and professional computing environments.
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.