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This is a team-based course where students will work on a project to improve a product using data and experimentation. We will cover key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. Students will 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, experimental and non-experimental methods for estimating the impact of product features, as well as management consideration for the delivery of actionable research. The course involves three weekly meetings: two lectures and one lab. Lectures will focus on research methods and will provide examples of research outputs for students to discuss and evaluate. Labs will comprise technical training in data analysis and structured team meetings. Students will work in cross-functional teams of 5-6 with milestones throughout the quarter. The final deliverable will be a presentation that highlights the team's work and delivers actionable recommendations that draw from the team's research. The class will include a mix of students with different backgrounds and skills. Each team will have at least one member with significant experience with data analysis. This course is part of the GSB's new Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will: Learn research-validated foundations, tools, and practices, Apply these tools and learnings to a real project for an external organization, Create value for the organization by providing insights and deliverables, Be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to: Gain practical industry experience and exposure to the organization, its industry, and the space in which it operates, Build relationships in the organization and industry, and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages.