This course provides a practical introduction to quantitative research methods for doctoral students in business, focusing on how data informs managerial and strategic questions. Students learn core statistical concepts, practice hands-on programming in R and Python, and study real examples from business research. Topics include probability, regression, hypothesis testing, and introductory machine learning for predictive and causal analysis. The course also emphasizes effective communication of quantitative findings through clear interpretation and data visualization. By the end, students will be prepared to evaluate empirical research and begin designing quantitative components of their own projects.

This course provides an introduction to research in the foundational information systems and finance topics. Designed for working professionals beginning their journey into research, it covers the fundamental concepts and methodologies used to investigate research questions in information systems and finance. Students will learn how to formulate research questions, how to use key frameworks in information systems and finance to investigate those research questions, as well as what methods are used in information systems and finance research.

The purpose of this workshop course is to meet with other DBA students and faculty to discuss research ideas, study designs, research challenges, and research progress. Students will present their research at different stages and practice receiving and giving constructive feedback.