Politics and Ethics of Data Analytics in the Public Sector
Master ethical data analysis in government, exploring frameworks and real-world case studies for evidence-based public policy.
Master ethical data analysis in government, exploring frameworks and real-world case studies for evidence-based public policy.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analytics in the Public Sector with R Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Explore ethical challenges in public sector data analytics
Understand frameworks and codes of ethics for data analysis
Analyze population bias and machine learning ethics
Conduct policy options analysis with real-world data
Create data visualizations for policy recommendations
Skills you'll gain
This course includes:
2.9 Hours PreRecorded video
3 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This comprehensive course explores the ethical dimensions of data analytics in public sector decision-making. Students examine common ethical challenges in data analytics, randomized controlled trials, and machine learning applications. The curriculum covers frameworks for ethical decision-making, professional guidelines, and real-world case studies including population bias and predictive model analysis. The course culminates in a capstone project analyzing authentic data for policy recommendations, providing practical experience in evidence-based public sector analysis.
The Power and Politics of Data
Module 1 · 4 Hours to complete
Professional Ethics for Data Analysts
Module 2 · 4 Hours to complete
Bringing it All Together in a Case Study
Module 3 · 5 Hours to complete
Fee Structure
Instructors
Associate Professor at the University of Michigan
Christopher Brooks is an Associate Professor in the School of Information at the University of Michigan, where he specializes in designing tools to enhance teaching and learning experiences in higher education. His research focuses on the application of learning analytics within human-computer interaction, utilizing methods from educational data mining, machine learning, and information visualization. Brooks has published extensively in these areas and is actively involved in directing the Educational Technology Collective, which includes postdoctoral scholars and students collaborating on innovative projects. He teaches various courses related to applied data science and has contributed to online education platforms such as Coursera. His work aims to leverage data to improve educational outcomes and foster better learning environments.
Expert in Health Policy and Social Demography
Paula Lantz is the James B. Hudak Professor of Health Policy at the Gerald R. Ford School of Public Policy and a University Professor of Diversity and Social Transformation at the University of Michigan. As a social demographer and social epidemiologist, her research focuses on how public policy can improve population health and reduce health disparities. Currently, Dr. Lantz is investigating critical issues such as abortion policy, housing policy, and the exacerbation of social and health inequities due to COVID-19. She has been recognized as an elected member of both the National Academy of Social Insurance and the National Academy of Medicine.Dr. Lantz teaches various courses that equip students with analytical skills in public policy, including "Assisting Public Sector Decision Makers With Policy Analysis" and "Politics and Ethics of Data Analytics in the Public Sector." With advanced degrees in sociology and epidemiology, she combines rigorous academic research with practical policy implications, aiming to foster social change through informed decision-making.
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4.9 course rating
11 ratings
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