Data Analytics & Leadership for Social Innovation

Managerial leaders across public, nonprofit, and for-profit sectors have access to large-scale datasets to address complex societal issues. This increasing expansion of data and digital ecosystems has influenced the scope and breadth of analytic approaches required for social innovation. An informed data-driven decision-making will provide managerial leaders with insights on how to target programs, cooperate with various stakeholders, and allocate resources appropriately. The purpose of this cross-disciplinary data science course is to equip students with the basics of programming and its applications to leadership for social innovation. The class utilizes an open-source language R software. The course covers the fundamentals of programming, including merging data sources, wrangling processes to clean datasets, and analyzing datasets.

In the first part of the course, we will cover theories and impact practices that can be applied to address complex societal challenges. For theories of the social sector, we take a close look at the lenses focusing on the role of the market, the state, and institutional environment in advancing social innovations through managerial leadership. For impact practices in the social sector, we pay attention to private action for public good, such as social entrepreneurship, ESG, and corporate philanthropy. In the second part of the course, we learn analytic techniques and use publicly available data sources targeted to advancing managerial leadership practices in social impact scaling. We finish the course with a discussion of ethical challenges in data leadership for social innovation.

Data Analytics & Leadership for Social Innovation

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