Student Sharing Studying DASC gave me so many new experiences. The unique teaching materials provided a challenging but memorable learning experience throughout my time at HKUST. I learned and upgraded my programming skills through project-based courses by self-initiated research as well as the feedback provided by the professors who taught the courses. Program Overview Data Analytics and Artificial Intelligence in Science is a four-year science degree for students who want to combine scientific thinking with programming, statistics, machine learning, visualization and practical AI. Rather than treating analytics in isolation, the curriculum applies it to data-intensive scientific fields and gives every student exposure to social analytics. You will build a rigorous foundation, choose a scientific track, and connect methods with meaningful questions through hands-on coursework and projects. Program Highlights This program is structured in such a way that students will be equipped to move confidently from raw scientific data to reproducible analysis, clear visualization, machine-learning models and AIassisted scientific inquiry. This program also distinguishes itself from most other Data Science and AI programs in that students are not only trained in analytical skills. The curriculum develops both technical competence and the ability to explain evidence clearly across disciplines. The ultimate purpose is to bring these techniques into practice in a data-intensive scientific field. At the start of Year 3, students shall select one of the following study tracks to refine and apply their skills. • Applied Biosciences Track - Cell biology, genetics, bioinformatics, and data science for biology and medicine. • Environmental Science Track - Earth and ocean systems, coastal monitoring, ecology, sustainability, and ocean data analytics. • Information Science Track - SQL, modern and information physics, quantum information, and information-science applications. • Molecular Science and Cheminformatics Track - Organic and biomolecular chemistry, cheminformatics, AI for chemistry, and machine learning for chemical data. The courses within each track are not designed to produce experts in the respective fields, but to immerse students in the context of data-intensive scientific domains. DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE IN SCIENCE (DASC) 13
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