Data Science and Scientific Computing Track Core Course

Format: The course is divided into three 4/5-week segments in which students work in interdisciplinary groups under the supervision of a DSSC faculty member. During each segment, students first learn the necessary background and tools, and are then coached by faculty to tackle a specific DSSC problem or data set in pairs. Evaluation is based on homework and written or oral reports at the end of each segment. Topics: • Segment 1: Basic inspection of real data (G. Tkacik) • Segment 2: Numerical computation and optimization (M. Mondelli) • Segment 3: Data generation, evaluation, presentation – project work (M. Robinson) Goals: • Provide hands-on experience and scientific insight into different DSSC problems and methodologies • Learn about evaluation criteria for good models in different fields • Build a community of computational / data students by project work • Practice the following skills: handling data, extracting knowledge from data, creating models, running numerical simulations, identifying and understanding sources of error, working in mixed background teams, visualize data, written and oral communication