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The Master of Science in Computer Science (MSCS) program is designed to appeal to a broad range of individuals. The program balances theory with practice, offers an extensive set of traditional and state-of-the-art courses, and provides the necessary flexibility to accommodate students with various backgrounds, including computer professionals who want to expand their understanding of Computer Science, as well as individuals whose undergraduate degrees are not in Computer Science but wish to broaden their knowledge in computing. The program also provides the background necessary to continue the study of Computer Science at the doctoral level. Students may choose a thesis option, which requires two semesters of study under the direction of a professor in which the student gains an understanding of an area of current research and contributes to it.
- Apply object-oriented programming in desktop and mobile applications
- Employ IDE and managerial tools in real world applications
- Analyze current and future trends in computer science and adapt them appropriately to changing business needs
- Illustrate effective communication and collaboration skills with stakeholders
- Demonstrate understandings of privacy, security, forensics and copyright issues in professional and social environments
- Understand various data structures and developing effective algorithms
- Understand, develop and apply database management concepts and tools
- Understand and design computer and network architecture and analyze different architectural models
Students choose an area and develop specialized knowledge in that area. Additionally, students work toward a project or research paper in their specialization, aimed at developing specific career-skills that they will use as they continue on into their professional trajectory. The following are offered as specializations in this program.
MS in Computer Science in Cybersecurity
The objective of this specialization is to equip the students with in-depth knowledge skills that will enable them to identify, develop, and implement effective and efficient defense mechanisms to secure organization networks and information resources to support organizational goals.
- Chief Information Security Officer
- IT Security Consultant
- Digital Forensic Analyst
These are mini-qualifications that demonstrate skills, knowledge, and/or experience in a given subject area or capability. The following are potential microcredentials in this area:
- Chief Information Security Officer (CISO): Chief information security officer job emphasis on overseeing organizations network and data security, ensuring the security of an organization’s network and electronic documents, learning about new technology and look for ways to upgrade their organization’s computer systems, determining short- and long-term personnel needs for their department, planning and direct the work of other IT professionals, including computer systems analysts, software developers, information security analysts, and computer support specialists
- IT Security Consultant: (ITSC): IT security consultant emphasis on securing IT, mentor staff and make sure the company meets its regulatory requirements, know how hackers work so the security consultant.
- Digital Forensic Analyst: (DFA): Digital Forensic Analyst emphasis in looking to apply forensic skills and mindset to a critical mission assessing hardware and software platforms for potential security vulnerabilities, be familiar with commercial and open-source digital forensics tools, and think like the adversary and devise concise mitigation strategies to counter any and all threats in an effort to protect the platform and its users.
MS in Computer Science in Intelligent Systems
The objective of this specialization is to equip students with in-depth knowledge skills that will enable them to apply artificial intelligence (AI), machine learning and intelligent systems techniques to solve real-world problems.
- Artificial Intelligence/Machine Learning Engineer
- AI Specialist
- AWS Machine Learning Engineer
They are mini-qualifications that demonstrate skills, knowledge, and/or experience in a given subject area or capability.
- Artificial Intelligence/Machine Learning Engineer (ALMLE): Demonstrated experience applying supervised and unsupervised techniques to structured data experience with at least one language for implementing AI/ML algorithms (e.g., Python, R). AIMLE leads software engineering teams in the architecture and implementation of solutions to solve challenging collaboration and social computing problems in domains such as healthcare, intelligence, and defense. AIMLE should be familiar with data management best practices and with DevOps patterns and practices.
- AI Specialist (AISP): AISP work emphasize on analyzing massive data sets, leveraging modern DL frameworks to develop computer vision and Natural Language Processing (NLP) models. AISP uses statistical methods and leverage knowledge of large data sets to characterize uncertainty through statistical methods and analyze an Enterprise Analytics Platform (EAP) while working with a cross-functional team of data scientists, software engineers, and testers.
- AWS Machine Learning Engineer (AWSMLE): AWSMLE work emphasizes on researching, innovating, and creating Prototypes and POCs for AI Platforms, tools & technologies to build models, deploy models into Production and monitor their performance. AWSMLE designs and develops software to extract, clean and manipulate large datasets both structured and unstructured. You will build and test the effectiveness and accuracy of Supervised and Unsupervised AI models including Image Analytics and NLP.