COURSE CURRICULUM

The Master of Science in Data Science at the University of North Texas is designed to provide students with advanced knowledge, research experience, and practical skills needed to succeed in the rapidly expanding field of data-driven decision-making. The program is enriched through special lectures hosted by the college and department, where renowned scholars share diverse perspectives and insights into the broader information science field, helping students stay connected to current research and emerging trends. The Department of Information Science is also part of the prestigious iSchools organization, an international consortium of leading information schools dedicated to advancing research and education in information and data sciences, which enhances collaboration and research opportunities for students. On campus, students have access to specialized research facilities such as the Data Innovation Lab, Intelligent Information Access Lab, and Visual Thinking Lab, which support hands-on experimentation and in-depth study across various areas of data science. The program also provides limited travel support for academically strong students, helping them attend conferences, workshops, seminars, and professional meetings where they can network and present their research. Students are encouraged to engage in collaborative learning and professional networking through organizations such as the UNT Data Science Student Organization and events like Data Science Day, which help build industry connections and career awareness. Instruction is offered in flexible formats, including face-to-face, online, and blended learning options, allowing students to balance their studies with professional or personal commitments. The curriculum includes key courses such as principles and techniques of data science, applied machine learning, data visualization for analytics, usability and user experience metrics, contemporary issues in workforce learning and performance, and fundamentals of data analytics, all designed to build a strong foundation in both theoretical understanding and practical application of data science concepts.