M.A. in Applied Policy Analysis Curriculum
Students will take courses on data analysis, data management, data visualization, economics, human behavior, and policy evaluation. Students in this program will be required to take a slate of core courses centered around various aspects of data analysis including regression analysis, behavioral analysis, ethics, data management and visualization, and applied economic theory. Once students have completed the majority of their core courses, they will be able to shift their focus to specific policy areas that may be of interest to them. The policy areas students can focus on will be education policy, health policy, environmental policy, and social/public policy. The aim of these policy-centered courses is to allow students to contemplate and devise potential data-oriented solutions that affect a variety of populations as it relates to the specific content area.
The aim of the program is to be data application-focused. The pairing of the core courses with the applied nature of the elective courses in specific content areas allows for those who enter the program to apply the data analysis tools learned to a wide variety of policy areas. Further, the elective courses are focused on specific societal issues that require a nuanced use of the data analysis toolkit that students will learn from other courses in the program. Overall, the program offers an interdisciplinary blend of data analysis, economics, and policy courses that will give students a diverse set of skills that can be applied in numerous industries.
The Master of Arts in Applied Policy Analysis requires 36 credits, with 27 of them coming from the following courses that all students must complete:
Required Courses
Introduction to Data Analysis (3 credits)
Economics for Policy Analysis (3 credits)
Data Ethics (3 credits)
Advanced Data Analysis (3 credits)
Time Series Data Methods (3 credits)
Behavioral Analysis (3 credits)
Data Management and Visualization (3 credits)
Applied Policy Analysis (3 credits)
Capstone for Applied Policy Analysis (3 credits)
Students must take 9 credits from the following list of graduate courses:
Elective Courses
Education Policy (3 credits)
Health Policy (3 credits)
Environmental Policy (3 credits)
Social and Public Policy (3 credits)
Core Courses Description
Introduction to Data Analysis (3 credits).
This course introduces students to the essential tools needed for policy analysis—statistics, hypothesis testing, probability, data management, data visualization, and regression analysis.
Advanced Data Analysis (3 credits).
This course is a continuation of Introduction to Data Analysis, in which students explore causal inference. This course explores more advanced methods of analysis in order to understand how to infer true causal relationships using both experimental and quasi-experimental methods. Additionally, students learn how to differentiate between correlation and causation. (Prerequisite: Introduction to Data Analysis)
Data Ethics (3 credits).
This course will explore ethical issues centered around the use of data. There is an abundance of data in the world and how that data is used is important when answering complex real-world issues. This course will discuss the responsibilities of researchers whose goal is to disseminate information gleaned from proper data analysis to a wide audience.
Time Series Data Methods (3 credits).
This course focuses on methods of analysis for data that is tracked over various periods
of time—time series. The application of such methods are useful for identifying underlying
trends that inform movements of variables, including past and future behavior.
(Prerequisite: Introduction to Data Analysis)
Data Management and Visualization(3 credits).
This course will introduce students to data gathering and visualization processes.
Real world data can be messy. All data tells a story, and how that story is told depends
on how the data is collected, tested, and presented. Being able to properly handle
data in various formats and being able to decipher good data from bad data is critically
important for any policy analyst. Students will learn how to gather data from various
sources to answer real-world policy questions and present their findings in written
and presentation form.
(Prerequisite: Introduction to Data Analysis)
Economics for Policy Analysis (3 credits).
This course explores the application of microeconomic theory to real-world policy and social issues. This includes topics such as resource allocation, incentives, opportunity cost, market failure, government intervention, uncertainty, consumer and firm behavior, and constrained optimization.
Behavioral Analysis (3 credits).
Policies are enacted in many ways in order to influence the choices people make. This
course will explore human behavior and incentives as it relates to policy-related
issues, including discussions about education policy, health care policy, and environmental
policy among other topics. This course will draw from insights from economics, psychology,
biology, sociology, and anthropology.
(Prerequisite: Economics for Policy Analysis)
Applied Policy Analysis (3 credits).
This course will give students the chance to apply their policy knowledge to real
world examples. Students will evaluate policy across various policy areas using the
toolkit they have developed in previous courses. Students will be given prompts regarding
current policy questions and will be required to critically analyze their efficacy
in both written and presentation form to a wide audience consisting of those from
both technical and non-technical backgrounds.
(Prerequisites: Introduction to Data Analysis, : Advanced Data Analysis)
Capstone for Applied Policy Analysis (3 credits).
This course will require students to conduct original research focused on current
policy issues. Students will choose a policy area and come up with a strategy for
analyzing a particular question within that policy realm and present the results in
written and oral form. This course is intended for students to showcase all of the
tools they have learned in previous classes.
(Prerequisites: Introduction to Data Analysis, : Advanced Data Analysis, : Applied
Policy Analysis)
Electives
Education Policy (3 credits).
This course will explore and discuss current issues surrounding education-related
policy. Students will delve into various topic areas as chosen by the instructor.
Topics may include educational inequality and opportunity, school readiness, education
and the law, instruction policy, higher education policy, and school finance. This
course will involve discussion, data collection and analysis, and written and oral
presentation on topic areas covered.
(Prerequisites: Introduction to Data Analysis, : Advanced Data Analysis)
Health Policy (3 credits).
This course will cover various topics related to health policy. The topics discussed
will be chosen by the instructor. The course will be centered around current issues
surrounding health policy such as health care equity, access to health care, insurance,
retirement and aging, population dynamics, and access to medical data. This course
will involve discussion, data collection and analysis, and written and oral presentation
on topic areas covered.
(Prerequisites: Introduction to Data Analysis, : Advanced Data Analysis)
Environmental Policy (3 credits).
This course will explore issues surrounding environmental-related policy. The topics
discussed will be chosen by the instructor. Topics may include environmental protection,
natural resource use, energy policy, transportation policy, and national and international
climate policy. This course will involve discussion, data collection and analysis,
and written and oral presentation on topic areas covered.
(Prerequisites: Introduction to Data Analysis, : Advanced Data Analysis)
Social and Public Policy (3 credits).
This course will explore topics centered around social and public policy. The topics
discussed will be chosen by the instructor. Topics may include minimum wage policy,
housing policy, social welfare policy, and criminal justice policy. This course will
involve discussion, data collection and analysis, and written and oral presentation
on topic areas covered.
(Prerequisites: Introduction to Data Analysis, : Advanced Data Analysis)