A broad multidisciplinary examination of the use and impacts of data, including fundamental principles and algorithms, tools for data analysis, visualization, and machine learning, policy issues, and societal considerations. Specific topics include: data provenance (where data comes from and how it's processed), the role and value of data in analytics and decision-making, data and algorithmic fairness, data privacy, the concentration of data as power, and issues of data governance and regulation, including transparency and due process. In addition to case studies, conceptual frameworks, theoretical underpinnings, and algorithms, the course provides practical experience through hands-on work where students use tools to explore issues from class on real data.
3 units · Letter or Credit/No Credit
A broad multidisciplinary examination of the use and impacts of data, including fundamental principles and algorithms, tools for data analysis, visualization, and machine learning, policy issues, and societal considerations. Specific topics include: data provenance (where data comes from and how it's processed), the role and value of data in analytics and decision-making, data and algorithmic fairness, data privacy, the concentration of data as power, and issues of data governance and regulation, including transparency and due process. In addition to case studies, conceptual frameworks, theoretical underpinnings, and algorithms, the course provides practical experience through hands-on work where students use tools to explore issues from class on real data.
Offered in Spring 2027 at Stanford University.