Data Science for Public Policy (Cornell University) trains you to use data analytics and machine learning to solve real public-sector and civic challenges. You’ll combine technical methods with policy, ethics, and communication skills to turn evidence into action.
The Master’s program in Data Science for Public Policy at Cornell University equips students with the essential skills to leverage data analytics and machine learning in addressing complex challenges within the public sector. By integrating technical expertise with policy analysis, ethics, and communication, graduates are prepared to transform data into actionable insights that drive civic decision-making.
The curriculum is designed to provide a robust foundation in both data science and public policy. Core coursework emphasizes the application of analytics in governance and decision-making processes.
Students may also engage in a capstone project, where they apply their analytical skills to real-world policy challenges, ensuring a practical understanding of the field.
While specific GRE, GMAT, or GPA requirements are not outlined for this program, applicants should possess a strong academic background and relevant experience in data science or public policy. Proficiency in English is essential for success in the program.
Non-native English speakers must demonstrate proficiency through standardized tests such as IELTS, with a minimum overall score typically required.
Graduates of the Data Science for Public Policy program are well-equipped for a diverse range of careers. They can pursue opportunities in government agencies, non-profit organizations, think tanks, and private sector firms focused on public sector consulting. The ability to analyze data and inform policy decisions positions graduates as valuable assets in driving social impact and improving governance.
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