COURSE CURRICULUM

The Master’s in Data Engineering at the University of North Texas is designed to prepare students for one of the fastest-growing and most in-demand fields in technology, focusing on the development and management of large-scale data systems. The program is open to students from a wide range of STEM backgrounds, including engineering, computer science, mathematics, and other science-related disciplines, making it accessible to those with strong analytical and technical foundations. Students learn how to design software solutions and manage hardware systems that efficiently collect, store, and process data across both SQL and NoSQL database environments, ensuring they are equipped to handle modern data infrastructures. The curriculum is closely aligned with industry needs, emphasizing not only technical problem-solving but also teamwork, communication, and collaboration skills that are essential in professional data-driven environments. Graduate students often receive financial support through teaching and research assistantships, which provide a monthly stipend and may include in-state tuition rates as well as tuition and fee support, with additional scholarship opportunities available. As part of a Carnegie-ranked Tier One research university, students have opportunities to work alongside faculty engaged in advanced research and innovation. The College of Engineering also provides access to state-of-the-art instructional facilities and laboratories equipped with cutting-edge research tools, enabling hands-on learning and discovery. Career opportunities for graduates are strong, with high demand for roles such as data engineers, data architects, business intelligence architects, machine learning engineers, and data warehouse engineers, reflecting the increasing need for professionals who can manage and interpret complex data systems across industries. The program includes courses such as empirical analysis methods, scientific data visualization, database systems, distributed and parallel databases, natural language processing, and introductory big data and data science, providing a strong foundation in both theoretical and applied aspects of modern data engineering.