This Area of Concentration is optional, yet the successful completion of these requirements will allow this concentration to be noted on your official transcript.

Concentration Courses as Electives

You have the opportunity to heavily customize your MS in Data Analytics and Policy degree because most of the courses listed below can satisfy the Elective Courses requirement. If a course is identified with *NOTE then that course cannot be counted as an elective outside of this concentration without prior academic adviser approval.

Area of Concentration Courses

A minimum of four courses are required to earn this Area of Concentration within the MS in Data Analytics and Policy degree.

This course is designed to introduce students to the public policymaking process, to the basics of policy analysis, and to the substance of some of today’s major policy debates. The first half of the course focuses on establishing a framework in which to analyze public policy formulation within the United States. The class also reviews the tools for developing and implementing policy. The second half of the course turns to policy analysis of some critical contemporary issues. Building on earlier readings, we will study current debates in economic/tax policy, education, health care, social security, and national security. (Core requirement for the MA in Public Management. Elective option for Government. Analytics students)

This course examines the role of race and ethnicity in U.S. national politics focusing on political development, political behavior, and public policy. Treated as both a persistent “dilemma” and as central to U.S. national identity, race and representation questions have been pivotal in American political development from the Founding to the present. Tracing that development over time, this course focuses, too, on how race-based differences manifest in differences in voting, public opinion, and other behavioral aspects of politics as well as the ways that racial attitudes have been embedded in public policies and reinforced by their implementation.

This course covers the ways in which analytics are being used in the healthcare industry. Topics include data collection opportunities created by the ACA and other laws, the use of analytics to prevent fraud, the use of predictive modeling based on medical records, the insurance industry's increasing use of data and the ethical issues raised by these practices. Prerequisites: none required (470.681 Probability and Statistics recommended)

Artificial intelligence is rapidly improving for well-defined tasks and narrow intelligence. But will AI ever have human-like general intelligence? This course is designed to answer this complex question by giving students a working knowledge of the underlying principles and mechanisms of human behavior and cognition. Key topics to be addressed include vision, audition, language, emotion, memory, creativity, and consciousness. We will use current and future advancements in big data and AI as a backdrop for critical and creative analysis.

Lobbying is a Constitutional right guaranteed under the First Amendment. It's also big business in Washington, DC, as more than $4 billion was spent on these efforts in 2015. In fact, for many, the term “lobbying” conjures up an image of a shady character passing a cash-filled envelope to an elected official.

The stereotype of lobbyists as greedy predators of the political system detracts from the efforts made by the tens of thousands of people, from lobbyists and concerned citizens alike, who come to Washington every year to exercise their “Right to Petition” the government to make it more responsive and accountable to the people.

This applied course provides students with a practical understanding of how to lobby Congress and the Executive Branch. The course also teaches students about “advocacy” efforts where unregistered public affairs firms employ campaign-styled tactics to persuade decision-makers to support their client’s positions.

With the passage of the Foundations for Evidence-Based Policymaking Act, all federal agencies are now required to make data accessible to the public and to implement specific plans for developing statistical evidence to inform policymaking. This course will examine the ways in which evidence and expertise are now being used for policy development and assessment. Specific topics will include cost-benefit analysis, cost effectiveness analysis, contingent valuation, forecasting and the communication of statistical evidence. In addition, the course will explore the interplay between political decisionmakers, experts and citizens in the evidence-based policymaking process.

Bridging the divide between political science theories of policymaking and the actual workings of the policy process in the institutions of national government, this course examines the individual contributions of each of the legislative, executive, and judicial branches of government as well as the interactions and struggles between those branches. How do these various institutions set the policy agenda, develop and deliberate policy alternatives, make authoritative policy decisions, and implement those decisions? In what ways are the interactions between these institutions best considered conflict or cooperation? Also, how do outside actors and institutions -- the media, interest groups, public opinion, parties and campaigns -- affect policymaking in these various institutional settings? Drawing on the Constitutional design and historical development of these institutions as well as contemporary practice, this course examines the purposes, processes, and outcomes of policymaking from an institutional perspective.

Data are everywhere, and many elected officials and government managers understand they need it. But how can they use it to solve problems and shape policy? What is the best way to make decisions based on a data analysis? How can they communicate those decisions, and the rationale behind them, to employees, citizens, and stakeholders? This course will provide students with an experiential learning opportunity based on real-world scenarios. Students will each take on a role (mayor, police commissioner, human capital director, budget director, public works director, public health director) and participate in a simulated public policy scenario. Working in small groups, students will apply a practical performance analytics process to develop solutions to address governmental challenges. Students will begin by studying foundational concepts and techniques of data collection, analytics, and decision support. They will also learn how to navigate multiple interests, asymmetrical information, and competing political agendas as they make difficult decisions about resource allocation and public policy. Along the way, they will learn how to turn insights into action by effectively communicating the results of analysis to busy executives and decision makers at all levels of the organization. Prerequisites: none required (470.681 Probability and Statistics recommended)

This class applies data analytic skills to the urban context, analyzing urban problems and datasets. Students will develop the statistical skills to complete data-driven analytical projects using data from city agencies, federal census data, and other sources, including NGOs that work with cities. We will examine a variety of data sets and research projects both historical and contemporary that examine urban problems from a quantitative perspective. Over the course of the term, each student will work on a real-world urban data problem, developing the project from start to finish, including identifying the issue, developing the research project, gathering data, analyzing the data, and producing a finished research paper. Prerequisite: 470.681 Probability and Statistics

This course explores technological and data-driven solutions for policy challenges. This includes developments within government, such as the new types of leadership provided by Chief Innovation or Chief Data Officers, the trend toward digitalization of services, and the movement toward open data. It also covers innovation by citizens through the civictechnology movement. Civic tech initiatives have been used to extend and improve services, increase efficiency, design applications for citizen engagement, and improve communication across a variety of policy domains. The course also covers the concept of smart cities and how it can be understood as both new applications of technology (such as sensors and smart infrastructure), and the strategic use of data. For the course project, students will evaluate a policy initiative using city open data, policy research, an analysis of political culture within which the initiative would be implemented, and the technology that could be used for the initiative. Some familiarity with R programming language and theRStudio environment is helpful. Prerequisite (one of the following): 470.768 Programming and Data Management; or experience with statistical programming and instructor permission.

The course examines how terrorist groups finance their operations. It also explores current policy approaches to curb financial support to terrorists through the application of U.S. and international sanctions, in particular how multilateral fora, such as the United Nations and the Financial Action Task Force, disrupt and deter terrorist financing. At the completion of this course, students will have a better understanding of the key tools, including law enforcement, diplomacy, and intelligence, that are used to counter terrorists’ financial networks and activities. Through this course, students will develop proficiency in a series of analytic methods used to study terrorist financing and counter financing. Students will use structured analytic tools such as weighted ranking methods, scenario trees, causal flow diagramming, hypothesis testing, and utility analysis, as well as game theory and logic to form analytic judgments. Prior coursework or professional experience in intelligence, (counter) terrorism, or finance recommended.

Analytics inform the decision-making process, strategizing, and forecasting of modern American campaigns. This course focuses on the role that analytics play in campaigns and elections in America. Campaign strategists, policy analysts, and social scientists leverage data from voter rolls, consumption and public opinion polls to make better choices. This course surveys the theoretical and empirical literature in American electoral politics to examine how campaigns and political organizations are using field experiments, microtargeting, and public opinion polling to tackle the challenges of getting out the vote and increasing registration and voting rates. Other topics covered include voting behavior, public opinion, partisanship, and campaign finance. Students will gain a rich understanding of how analytics has become a key component of the electoral process. Students will also gain experience analyzing data through simulations and data analysis exercises. Prerequisites: none required (470.681 Probability and Statistics recommended)

Data science is a methodology for extracting insights from data. This course is an introduction to the concepts and tools that are used in data science with an emphasis on their application to public policy questions. The course covers some advanced data mining and machine learning processes including classification and decision trees, random forests, cluster analysis, and outlier detection, while also providing you with training in the basics of data management and data exploration. All of the work in the course will be conducted to prepare you to proficiently conduct predictive analytics in a real-world setting. Some familiarity with R programming language and the RStudio environment is necessary. Prerequisite: 470.681 Probability and Statistics

This course will introduce computational modeling and demonstrate how it is used in the policy and national security realms. Specifically, the course will focus on agent-based modeling, which is a commonly-used approach to build computer models to better understand proposed policies and political behavior. Agent-based models consist of a number of diverse "agents,'’ which can be individuals, groups, firms, states, etc. These agents behave according to behavioral rules determined by the researcher. The interactions with each other and their environment at the micro-level can produce emergent patterns at the macro-level. These models have been used to understand a diverse range of policy issues including voting behavior, international conflict, segregation, health policy, economic markets, ethnic conflict, and a variety of other policy issues. The course will consist of two parts: First, we will examine the theoretical perspective of computational modeling. Second, you will be introduced to a software platform that is commonly used to develop computational, and, in particular agent-based modeling. No prerequisite

Washington, D.C. is the laboratory for anyone studying American government and politics or analyzing the policy making process here. DC Lab: Politics, Policy, and Analytics will give any graduate student in one of the programs of the JHU Center for Advanced Governmental Studies the opportunity to bring theory and practice together through an intensive week of lectures, seminars, and site visits in the nation’s capital. Sessions will include guest speakers from JHU faculty, think tank scholars, and agency officials. The goal is to experience Hopkins in Washington and assess what is observed to better inform each student’s studies of the political process. No prerequisite

Intelligence analysis is fundamentally about understanding and communicating to decision makers what is known, not known, and surmised, as it can best be determined. Students will read seminal texts on intelligence analysis, discuss the complex cognitive, psychological, organizational, ethical, and legal issues surrounding intelligence analysis now and in the past, and apply analytic methodologies to real-world problems.

This course describes and assesses the role played by intelligence in the formation of national security policy, with a particular emphasis on the relationship between intelligence and policy. It is important to understand that this relationship pervades the entire intelligence process, from requirements through collection, analysis and also operations. Policy makers are broadly defined to include the Executive Branch, Congress and, on occasion, the courts. The course reviews the steps of the intelligence process in detail, assessing the roles played by policy makers and intelligence officers. This course is designed to give students an appreciation and understanding of the role played by intelligence in support of policy, as well as the stresses and strains that exist between intelligence and policy, and within intelligence itself.

If a course is identified with *NOTE then that course cannot be counted as an elective outside of this concentration without prior academic adviser approval.


Students should be aware of state-specific information for online programs. For more information, please contact an admissions representative.

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