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.
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)