COURSE CURRICULUM

The Master’s in Applied Data Science is designed to provide students with comprehensive training in the full data lifecycle, equipping them with the skills and knowledge needed for advanced roles in data-driven industries. The program requires a total of 31 graduate-level units and builds on a foundation of quantitative and computational skills. Students begin with four required courses totaling 12 units, which cover the fundamentals of the field, including the Foundations of Data Science, Theory of Algorithms and Their Analysis, Big Data Management, and Data Mining. These courses provide a strong theoretical and practical base in managing, processing, and analyzing large datasets, as well as understanding the underlying principles of computational algorithms and statistical methods. In addition to the core courses, students must complete five elective courses totaling 15 units, allowing them to tailor their studies to specific areas of interest or career goals. Electives include options such as Mathematical Statistics, Computational Intelligence, Machine Learning, Database Systems, Data Visualization, Artificial Intelligence, and Distributed Database Management Systems. These courses deepen students’ expertise in advanced analytics, algorithmic methods, machine learning techniques, artificial intelligence applications, and the design and management of complex databases. The program culminates in a 4-unit capstone project, either CSE 6880 or MATH 6880, which requires students to integrate and apply their learning to a real-world or research-based data science problem. This project emphasizes practical application, problem-solving, and the development of a professional portfolio demonstrating mastery of data collection, cleaning, integration, analysis, and visualization techniques. Students are expected to maintain a minimum GPA of 3.0 in all courses, and the program must be completed within seven years. Overall, the Master’s in Applied Data Science prepares students to enter diverse professional settings, including technology companies, research institutions, government agencies, healthcare analytics, finance, and consulting, with strong technical skills and the ability to address complex data challenges using contemporary methods in data science, machine learning, and artificial intelligence.