Data-Driven Impact

Data-Driven Impact fosters cross-disciplinary learning and collaboration to build effective teams working on a data project with real-world implications.

This course covers key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. Students have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. Topics include designing research and experiments; data analysis; and experimental and non-experimental methods for estimating the impact of product features, as well as management consideration for the delivery of actionable research.

Project Examples


The Economics of Technology Professor

Who Should Register?

Data-Driven Impact (ALP 301) is available to the following students:

  • Students interested in learning more about how to work in a multidisciplinary team to use data and statistics to inform business decisions and create social impact
  • Second-year MBA and all MSx students at Stanford GSB
  • Other Stanford graduate students with a good understanding of financial or data analytic techniques, and an interest in impact or other mission-focused enterprises and investments (application required)