M.S. in Data Analytics and Visualization Curriculum Making the World Smarter, Safer and Healthier Attend an Info Session Contact Us Artificial Intelligence Biotechnology Computer Science Cybersecurity Data Analytics and Visualization Digital Marketing and Media Mathematics Occupational Therapy Physician Assistant Physics Speech-Language Pathology Overview YU’s flexible 30-credit master’s degree in data analytics and visualization prepares you to succeed in cutting-edge business analytics, BI, data analytics and data visualization careers. As part of the coursework, you'll complete an industry-oriented final project, and you'll be encouraged to gain hands-on experience by working in real companies through internships and independent study. You'll develop a work-ready portfolio of projects and deepen your understanding of the core principles, patterns and practices of data science and visualization. The M.S. can be completed full time in just 15 months or part time at a pace that makes sense for you. Review degree requirements below and download course descriptions here. Degree Requirements To earn the M.S. in Data Analytics and Visualization, you must complete 21 credits of required foundational courses, including a capstone project as well as nine credits of elective courses, which may include an internship. Courses are 3 credits, unless otherwise noted. Core Requirements (21 credits) DAV 5000 Business Modeling and Data Analysis DAV 5100 Structured Data Management DAV 5200 Visual Design and Storytelling DAV 5300 Computational Math and Statistics DAV 5400 Analytics Programming MAN 5580 Project Management DAV 6500 Capstone Electives (9 credits) AI Product Studio DAV 6000 Talent Analytics DAV 6050 Data-Driven Organizations DAV 6100 Information Architectures DAV 6150 Data Science DAV 6200 Data Product Design DAV 6300 Special Topics (1-3 credits) DAV 6400 Internship (1-3 credits) DAV 6450 Independent Study (1-3 credits) Note: Recent special topics courses include Recommendation Systems, Automated Machine Learning, Cloud Computing and Experimental Optimization. Qualified Data Analytics and Visualization students often take electives in other graduate programs, including marketing and artificial intelligence. Electives offerings will vary each semester, so some choices will not be available for a particular cohort.
