Molecular, health, and Environmental Data Science

Undergraduate

Data Science introduces the application of computational and statistical techniques to data analysis, with a focus on real-world problem-solving. Students in the course will explore key tools and frameworks for data analysis, with hands-on projects in domains such as healthcare, forensics, chemistry, and environmental sciences, environmental compliance regulations. This course is distinguished by its emphasis on practical implementation, the handling of diverse data types and structures, and the integration of machine learning and visualization tools for data-driven scientific decision-making. Topics covered include data acquisition and cleaning, exploratory data analysis, statistical modeling, supervised and unsupervised learning, feature engineering, and model evaluation. (Prerequisites: Semester 5 standing or above in a Science program). (3 lecture hours per week, 3 lab hours per week.)