Data Science Research Methods

Catalog description

This course prepares doctoral students to design, execute, and document rigorous research that contributes to the body of knowledge in data science. Unlike general research methods, this course specifically addresses the unique challenges of computational research, including algorithmic benchmarking, experimental design for large-scale datasets, and the nuances of reproducible data-driven discovery. Students will explore diverse research paradigms, ranging from experimental and quasi-experimental designs to observational studies and simulation-based research. The curriculum emphasizes the selection of appropriate methodologies to validate predictive models and the application of rigorous evaluation frameworks. By the end of the course, students will be able to formulate high-impact research questions, identify appropriate data sources, and establish the technical and theoretical validity of their findings for peer-reviewed dissemination and strategic business application.