The objective of this course is to develop the skills needed to turn data and AI technologies into competitive advantage. Students build a rigorous foundation in predictive modeling and machine learning, which serves as the basis for understanding the capabilities and limitations of modern AI technologies, including AI agents, and for knowing when to trust their outputs and when to override them. Particular attention is given to the actionable insights that can be derived from data and the practical pitfalls of data-driven approaches. The course covers statistical modeling, machine learning, and experimental design, with applications spanning advertising, eCommerce, finance, healthcare, marketing, and revenue management. Students work hands-on with real datasets using Python and AI technologies, learning to formulate business-relevant questions and solve them through data analysis. A central theme is integrating technical capability with the domain expertise and business judgment that determine whether data-driven decisions actually work in practice. Students are expected to integrate these topics with their existing proficiency in mathematical notation, algebra, probability, and basic statistics.
3 units · GSB Letter Graded
The objective of this course is to develop the skills needed to turn data and AI technologies into competitive advantage. Students build a rigorous foundation in predictive modeling and machine learning, which serves as the basis for understanding the capabilities and limitations of modern AI technologies, including AI agents, and for knowing when to trust their outputs and when to override them. Particular attention is given to the actionable insights that can be derived from data and the practical pitfalls of data-driven approaches. The course covers statistical modeling, machine learning, and experimental design, with applications spanning advertising, eCommerce, finance, healthcare, marketing, and revenue management. Students work hands-on with real datasets using Python and AI technologies, learning to formulate business-relevant questions and solve them through data analysis. A central theme is integrating technical capability with the domain expertise and business judgment that determine whether data-driven decisions actually work in practice. Students are expected to integrate these topics with their existing proficiency in mathematical notation, algebra, probability, and basic statistics.
Offered in Winter 2027 at Stanford University.