COURSE CURRICULUM

The Master of Science in Data Science (MSDS) program is designed to provide students with advanced technical knowledge and practical skills in data analysis, artificial intelligence, machine learning, and large-scale data engineering. The curriculum combines theoretical foundations, computational techniques, and applied learning experiences to prepare students for careers in data science, analytics, artificial intelligence, and related technology-driven industries. The program requires students to complete core courses in Math for Data Scientists, Introduction to Data Science, Machine Learning, Natural Language Processing, Distributed and Scalable Data Engineering, Deep Learning, and Introduction to Artificial Intelligence. These courses provide comprehensive training in statistical modeling, AI systems, machine learning algorithms, data processing frameworks, and intelligent computational methods used to solve real-world problems. Students also complete either a Data Science Internship or a Capstone Project, which provides practical experience through professional industry work or applied project development. In addition to the required coursework, students select two courses from Special Topics and Electives, with at least one course coming from the Special Topics category. Special Topics courses allow students to explore advanced and emerging areas such as Computer Vision, AI and Cybersecurity, Bayesian Data Analysis, Leadership and Entrepreneurism, and Autonomous Vehicle technologies. Elective courses provide opportunities to gain interdisciplinary expertise in areas such as data visualization, bioinformatics, database systems, cybersecurity, forecasting, optimization, supply chain analytics, and healthcare analytics. Students without sufficient programming experience in Python may be required to complete an additional preparatory course, Introduction to Programming for Data Science, before progressing into advanced coursework. The program also provides recommended course plans for both 30-credit and 33-credit pathways, helping students progress through foundational subjects, advanced AI and machine learning courses, electives, and experiential learning in a structured manner over four semesters. Throughout the program, students develop skills in exploratory data analysis, algorithm design, distributed data pipelines, deep learning, natural language processing, and AI-driven problem solving. Graduates are prepared for careers as data scientists, machine learning engineers, AI specialists, business intelligence analysts, data engineers, and analytics professionals in industries such as technology, healthcare, finance, cybersecurity, research, and business consulting.