Yeshiva University Katz School of Science and Health
M.S. in Cybersecurity
The Katz School of Science and Health at Yeshiva University gives students the opportunity to pursue their intellectual and professional ambitions and become part of one of U.S. News and World Report’s top 100 universities in the United States.
The Katz School of Science and Health MS programs at Yeshiva University focus on applied sciences and mathematics; technology, data, and design; health sciences; and emerging and expanding professions that are being transformed by technology innovations. Students can earn graduate degrees in Artificial Intelligence, Biotechnology Management and Entrepreneurship, Cybersecurity, Data Analytics and Visualization, Digital Marketing and Media, Mathematics, Occupational Therapy, Physics, Quantitative Economics, Quantitative Finance, and Speech-Language Pathology.
In each of these highly specialized programs, the curriculum is informed by industry practices to provide our students with the tools that will serve them well in their careers. Courses are frequently project-based so that students are evaluated on what they build and do. As a result, students graduate with a portfolio of work that will give them a competitive edge in the job market.
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The Katz School’s MS in Cybersecurity is a STEM-approved, 30-credit master’s degree that is focused on the technology and management competencies for planning, implementing, upgrading, monitoring and auditing cybersecurity protocols and procedures. The degree is aligned with industry certifications: CISSP, CISM, and ISACA-CRISC. You’ll get hands-on experience with threat mitigation, detection and defence, and you’ll have the opportunity to gain practical experience through internships, CPT and STEM OPT.
Artificial Intelligence, M.S.
In the interdisciplinary Artificial Intelligence master’s degree, you’ll design and build AI technologies for a variety of applications, such as finance, biotech, cybersecurity, adtech and law. Working with top faculty and accomplished industry experts, you’ll bridge AI and machine-learning models, such as supervised and unsupervised learning, deep learning and neural networks, and reinforcement learning with engineering best practices, including problem framing, requirements gathering, UI/UX, and software development. In addition, you’ll gain hands-on experience with structured and unstructured data using the latest tools, such as Python, R, Google ML Kit, SQL/NoSQL, and TensorFlow.