COURSE CURRICULUM

The Master of Science in Advanced Data Analytics at the University of North Texas is a flexible and industry-focused 30-hour graduate program designed to prepare students for success in an increasingly data-driven economy. The program offers both accelerated and traditional pathways, with all core courses delivered in 8-week accelerated sessions throughout the fall, spring, and summer terms. Students can begin the program at multiple entry points each year, including August and October in the fall, January and March in the spring, and June in the summer, providing maximum flexibility for working professionals and full-time students. One of the key advantages of the program is the accelerated online format, which allows students to complete their degree in as little as 12 months. For those who prefer a traditional classroom experience, courses are also available in a 16-week format at both the UNT Frisco campus and the main Denton campus during fall and spring semesters. The program includes a structured curriculum of 18 hours of core coursework and 12 hours of concentration coursework, with specialization options in Digital Retailing, Health Data Analytics, Management, and Statistics. This structure enables students to tailor their education to specific career goals while maintaining a strong foundation in analytics principles. The curriculum emphasizes the application of big data analytics, statistics, machine learning, and data visualization to real-world business problems, ensuring students can translate complex datasets into actionable insights. Graduates are prepared for in-demand roles across industries where organizations rely on data to assess risk, improve efficiency, and increase profitability. Core courses such as Data Analytics I, Data Mining, Big Data Analytics, Large Data Visualization, Analytics Capstone, and Introduction to Data Analytics provide both technical depth and practical experience, culminating in the ability to design, execute, and communicate data-driven solutions effectively.