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Course Criteria
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0.00 - 1.00 Credits
Explores a range of topics related to academic processes and professional development for master level students. Topics may include: strategies for successful graduate study, advisor/committee strategies, topic selection, the research experience, writing skills, time/stress management strategies, conference presentations, publishing, job searches, professional organizations and networking. Guest speakers include various faculty members, students and alumni.
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0.00 Credits
Vertically Integrated Project Labs provide students an opportunity to participate in a faculty led project with students from across degree levels in an applied, real time project implementation.
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1.00 - 12.00 Credits
No course description available.
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3.00 Credits
Advanced training in multivariate regression for public policy analysis and social science research. This course is open to doctoral and master's students. The course will primarily cover linear modeling with the Ordinary Least Squares (OLS) estimator, including panel data analysis, regression discontinuity, and estimation with endogenous covariates. In addition, the course will introduce students to estimating nonlinear models with categorical dependent variables using Maximum Likelihood Estimation (MLE).
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3.00 Credits
Introduction to major concepts of survey research, and prepares students in the fundamental skill areas necessary to design, conduct and evaluate complex survey research studies. The specific areas covered include survey research design, questionnaire construction, scientific sampling, survey management, quality control, and analytical concerns.
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3.00 Credits
As societal data has become increasingly accessible, there has been a rapid growth in the use of data for evidence-based decision making in public policy. This course introduces tools and techniques from the recently established field of Data Science to extract insight and understanding from policy-relevant data, focusing on how data is accessed, handled, analyzed, and visualized. Topics include: an introduction to Python and R, working with public data, data structures and geospatial information, basic statistics, and data visualization.
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3.00 Credits
Data-driven policy decisions are an increasingly important aspect of civic operations, opening up new avenues for decision makers to enhance public welfare and quality of life. Policy-relevant information is extracted from data via the application of computational algorithms known as machine learning (ML). This course surveys common ML techniques in data analyses that impact policy and decision making, focusing on qualitative and quantitative understanding of ML models as well as their real-world application, model bias, and data ethics.
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3.00 Credits
Focuses on concepts, issues and techniques related to research design, data acquisition and data analysis in the fields of urban affairs and public policy.
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3.00 Credits
Provides skills and examines issues in use of qualitative methods in context of program evaluation. Qualitative design, sampling approaches, data collection, and analysis included. RESTRICTIONS: Enrollment limited to 15.
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3.00 Credits
This course in advanced public policy and administration provides students with an in-depth application of public policy and public administration theories in empirical scholarship. This course begins with current empirical literature in public administration and public policy from top journals and traces the core concepts and research questions in these works back to foundational approaches in these fields. A central component of this course is the exploration of a critical set of questions and topics as understood and explored in both the fields of public policy and public administration such as government-nonprofit relations, bureaucracy and bureaucratic politics, performance management, human resources/people management, collaboration and collaborative governance, institutional design and governance, social equity and outcomes, politics and policy, and methods and tools for policy analysis. PREREQ: UAPP822, UAPP830.
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