Registration for the Fall 2022 semester is open. Please see the Academic Calendar for the full schedule.
Certificate Requirements and Courses
This 15-credit Post-Baccalaureate certificate is composed of 2 Required Core Courses and 3 Elective Courses.
Core Courses - Required
Complete these 2 courses:
This course introduces students to the fundamentals of statistical analysis as well as the R programming language and RStudio environment. Students will learn the building blocks of descriptive and causal inference, including summary statistics, survey sampling, measurement, hypothesis testing, linear regression and probability theory. Students will also learn how to create data visualizations in R, including times series plots, scatter plots and bar graphs. In addition, students will focus on interpreting statistical findings and presenting results in a compelling manner. By the end of the course, students will be able to conduct a statistical analysis to answer a meaningful policy question and will be prepared to take more advanced methods courses. Prerequisites: none
Solutions to policy challenges increasingly require an understanding of how to analyze, present, and interpret data. Government agencies use data to evaluate programs and proposed policy initiatives. Private companies use data to inform their strategic decision making. Advocacy organizations use data to support their positions. This course will provide you with the knowledge and skills needed to perform a sophisticated data analysis. You will learn how to design and test regression models using R/RStudio, an incredibly powerful and open-source statistical software package. Specific topics covered in the course include measures of fit, logistic and probit regression, panel data, instruments, and translating statistical findings for broad audiences. The focus of the course is on using statistics in an applied manner to address meaningful research questions.
Prerequisite: 470.681 Probability and Statistics
Select 3 electives from the courses listed below.
Alternatively, and with the approval of your academic adviser, up to two of your elective courses may be chosen from selected master's programs within the Advanced Academic Programs division, including: Applied Economics, Environmental Sciences and Policy, Global Security Studies, and Government.
Web GIS is an important foundation course in which students will become familiar with the current platforms available for delivering Web GIS and sharing geographic content over the web. Professionals in various industries often have to make information readily available and with current developments this has become easier than ever. The class offers a fundamental understanding of creating and designing web maps and web apps using various approaches and platforms. Capabilities such as editing, geoprocessing, geocoding, image analysis, 3D, mobile and real-time GIS in a web environment will be examined. Cloud-based and on premises infrastructure to deliver Web GIS will be utilized.
Offered twice a year.
In this introductory course, students become familiar with the concepts and gain the experience necessary to appreciate the utility of Geographic Information Systems in decision-making. Topics covered include the fundamentals of data structures, georeferencing, data classification, querying, cartography, and basic spatial data analysis. The course provides an overview of the capabilities of GIS software and applications of GIS. Class time is divided between lectures and GIS exercises that reinforce critical concepts. Students must complete a term project as part of the course. Offered every semester. Elective option for Govt. Analytics students.
This course introduces theory and practical application of statistical methods in spatial analysis. Statistical fundamentals will be introduced to expose students to descriptive and inferential methods in spatial statistics. Geostatistical fundamentals will also be covered to introduce methods (in particular, kriging) for modelling spatial and spatio-temporal phenomena. This course will provide working knowledge of theory and practice in spatial statistics and Geostatistics, and will serve as a primer to more advanced courses in spatial statistics and machine learning. Theoretical knowledge will be supplemented with real-world use cases through in-class projects and assignments. Throughout the course, students will be exposed to open-source statistics libraries in R, no previous programming knowledge will be assumed. Offered twice a year.
This course introduces students to using various techniques for solving spatial problems. The course teaches a proven process one can utilize to address common inquiries related to understanding spatial relationships and patterns. Traditional analytical methods such as suitability analysis, network analysis, geostatistical analysis, spatial interpolation, etc. are examined, along with recent data science and analytics methodologies that help us extract knowledge and insights from data. Examples and assignments are drawn from many applications, such as business, urban planning, public safety, public health, transportation and natural sciences. Offered twice a year. Elective option for Govt. Analytics students.
In this course students will learn how to automate workflows and develop tools using Python as a fundamental language for geospatial technology. The course will first cover introductory python basics, then move into geospatial concepts. It will teach students how to automate simple and complex GIS tasks and functionality, thus simplifying workflows and increasing efficiency. Focus will be placed on following proper coding techniques and patterns. The course will introduce students to Python, ArcPy, Python API, Pandas, Numpy, Jupyter, and Markdown to name a few. Offered twice a year. Prerequisites: 430.600 Web GIS
In the wake of the financial crisis, bank bailouts, and stimulus plans, the relationship between American economic power and national security is especially salient. In this course, students investigate core topics in international political economy, analyzing the security implications of each. Topics include trade relations, international finance, monetary relations, poverty, and development. (Core course for the MA in Global Security Studies. Recommended elective for MA in Public Management)
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 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)
This course focuses on financial aspects of public sector organizations and institutions. The objectives of this course include helping students (1) learn the basics of public sector accounting and the construction of their financial reports, (2) become more intelligent users of the financial statements of public sector organizations such as sovereign, state, and municipal institutions, and (3) better understand the factors that affect the financial condition and financial performance of such entities.
More specifically, the course focuses on (1) the financial reporting concepts and standards that are applicable to public sector organizations; (2) ratios and other summary indicators used by analysts to evaluate the financial condition and financial performance of public sector and nonprofit organizations; (3) the analysis and interpretation of financial statements of selected public sector organizations; (4) fundamental finance principles; and 5) basic principles of budget formulation.
Economic thinking provides an important set of tools for almost every aspect of public policymaking. This course aims to offer students a basic understanding of economics and its importance in public policymaking. The first half of the course will offer students an understanding of microeconomic and macroeconomic theory, including a discussion of when markets can work to achieve policy goals and when “market failures” call for government intervention. The second half of the class will use these economic tools and theories in order to survey several specific policy areas, including health policy, tax policy, and the national debt. (Core course for the MA in Public Management This course counts toward the Economic Security concentration (GSS). Elective option for Government Analytics students.)
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.
In this course students will develop expertise in using the tools necessary to collect, analyze, and visualize large amounts of text. The course begins with a hands-on introduction to the programming concepts necessary to collect and process textual data. The course then proceeds to cover key statistical concepts in machine learning and statistics that are used to analyze text as data. Throughout the course, students will develop a research project that culminates in the display of results from a large-scale textual analysis. Prerequisite: 470.681 Probability and Statistics.
The federal budget process is an enormously complex mixture of administrative routines and mechanisms designed to bias decisions, avoid blame, or reduce conflict. This course explores the structures of federal budgeting in terms of its varied goals and in the context of the wider governing process. The course will review the budgetary process in both the executive and congressional branching, as well as the interaction of those two systems. In order to gain understanding of the difficult policy choices and political pressures policymakers face, students will be asked to do a simulation of a budget process within the executive branch. The role of entitlements, scoring issues, and tax policy will be examined in the context of the debate over budget policy. The course will start with a short primer on finance theory. (Recommended elective for MA in Public Management. Elective option for Government Analytics students.)
Machine learning and, more broadly, artificial intelligence, can now be used to perform complex tasks such as image recognition, fraud detection, traffic prediction and product recommendations. These successes are driven by developments in machine learning, such as the use of neural networks. This course introduces students to a variety of machine learning techniques using Python. Students will first learn the fundamentals of the Python programming language and will then implement machine learning algorithms and develop an understanding of how they work. Further, students will learn how to select and implement an appropriate algorithm depending on the type of dataset they have, and will be able to use a machine learning algorithm to generate predictions.
Prerequisite: 470.681 Probability and Statistics
Risk management has always been in the vanguard of data analytics because risk measurement is a critical element in calculating risk/return tradeoffs. This course will examine both qualitative and quantitative analytical methods commonly used in risk management. Qualitative tools include impact/likelihood analysis; event and fault trees; threats, vulnerability, and consequences (TVC); and failure mode and effects analysis (FMEA). However, a key lesson in risk management is that what gets measured gets managed. As a result, a major part of the course will focus on quantitative tools, including modeling and stochastic simulations. We will use the @Risk software to build realistic risk models, including one in assessing project management risks. The objective of the course is to equip students with practical tools they can apply in risk-based decision making.
Prerequisites: 470.681 Probability and Statistics; working knowledge of Excel
This course instructs students in various visualization techniques and software, including R, Tableau, and vector graphics software (e.g., Adobe Illustrator, Inkscape). Students will learn how to ask interesting questions about politics; identify data that can be used to answer those questions; collect, clean and document the data; explore and analyze the data with statistical and graphical techniques; and create compelling, informative and accurate visualizations and present these visualizations to educated audiences. Prerequisite: 470.681 Probability and Statistics
This course introduces students to big data management systems such as the Hadoop system, MongoDB, Amazon AWS, and Microsoft Azure. The course covers the basics of the Apache Hadoop platform and Hadoop ecosystem; the Hadoop distributed file system (HDFS); MapReduce; common big data tools such as Pig (a procedural data processing language for Hadoop parallel computation), Hive (a declarative SQL-like language to handle Hadoop jobs), HBase (the most popular NoSQL database), and YARN. MongoDB is a popular NoSQL database that handles documents in a free schema design, which gives the developer great flexibility to store and use data. We cover aspects of the cloud computing model with respect to virtualization, multitenancy, privacy, security, and cloud data management.
Prerequisite: 470.763 Database Management Systems
A 64-bit computer with a chip that supports virtualization (set via BIOS)
Windows Operating System 7, 8, or 10
At least 8 Gb of Physical RAM
Oracle VirtualBox version 4.2 (free)
Please be in touch with the instructor with questions about the technology requirements.
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 course provides insights into how to utilize shared cloud computing resources through a service provider. These resources can be storage space, software as a service, or compute servers. This is a hands-on course in which students will access a variety of cloud services and work with different cloud providers such as Apple, Microsoft, Google, and Amazon. Students will set up virtual servers, work with cloud file storage, learn about a variety of cloud collaboration options, and much more. This practical course will help students make the transition to working in the cloud from any device, anywhere, anytime. All areas of the public sector, such as education, healthcare and law enforcement, increasingly use cloud computing both to deliver information to clients and share information within and across agencies.
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
Learning the basics of Python empowers analysts to retrieve and leverage data in new ways. After covering the fundamentals of syntax, students learn how to read, create and edit data files using Python. Building on that knowledge, students interact with online resources through bulk data APIs and web scraping. Finally, students will use the data they collect to develop an original analysis. Prerequisite: 470.681 Probability and Statistics
This course addresses the legal, policy and cultural issues that challenge the government and its citizens in the increasingly complex technical environment of privacy. We will examine the challenges in balancing the need for information and data against the evolving landscape of individual privacy rights. The course will examine privacy at all levels: by analyzing the shifting views of individual privacy by citizens as well as the technological challenges in both protecting and analyzing personal information for government use. Using case studies and hypotheticals, we will discuss the issue of transparency in the government use and retention of data. The cases will range from Facebook to healthcare.gov to sunshine laws to national security uses of information. We will trace the development of legal and policy measures relevant to privacy concerns and envision future solutions needed in an era of great technological innovation including the use of big data.
In a democracy, the views of citizens are intended to guide lawmakers as they shape public policy. This makes public opinion a central component in the study of democratic politics. In this course, we will investigate the psychological and sociological origins, structure, measurement, and consequences of public opinion. We will investigate the content of what people think on a variety of salient topics from immigration, income inequality, taxes, to the 2020 elections. However, the main purpose of this class is to move beyond the what and examine the why. Why do Americans think what they do about politics? The course will draw from theories in political science and political psychology to examine the organizing structures of political beliefs including identity, self-interest, socialization, personality, values and morality. In turn, the course will examine how these various sources of public opinion impact voting behavior and policy preferences.
Data analytics are an essential part of program and policy evaluation. Policymakers increasingly rely upon analytics when making critical policy decisions. In this course, students will conduct a variety of policy focused data analyses using R. Students will utilize a variety of descriptive and inferential data analysis techniques to inform the design and execution of a policy. Students will utilize data-driven analysis to produce policy memoranda in a variety of domains relevant to today’s practitioners. A good understanding of basic economics and statistics, and an understanding of American government institutions and programs, will be necessary for a student to participate effectively in the class discussions and complete the assignments. Please contact the instructor with any questions.
Prerequisite: 470.681 Statistics and Political Analysis
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.
Prerequisites: one of the following: 470.681 Probability and Statistics or 470.768 Programming and Data Management
Many government agencies engage in data mining to detect unforeseen patterns and advanced analytics (such as classification techniques) to predict future outcomes. In this course, students will utilize IBM SPSS Modeler to investigate patterns and derive predictions in policy areas such as fraud, healthcare, fundraising, human resource and others. In addition, students will build segmentation models using clustering techniques in an applied manner. Integration with other statistical tools and visualization options will also be discussed.
Prerequisite: 470.681 Probability and Statistics; Recommended: 470.709 Quantitative Methods
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)
This course provides students with a strong foundation in database architecture and database management systems. Students will evaluate the principles and methodologies of database design and techniques for database application development. Students will also examine the current trends in modern database technologies such as Relational Database Management Systems (RDBMS), NoSQL Databases Cloud Databases, and Graph Databases.
This course is a comprehensive examination of all aspects of designing questionnaires, conducting survey research, and analyzing survey data. The class will cover question construction, measurement, sampling, weighting, response quality, scale and index construction, IRBs, ethics, integrity and quality control, modes of data collection (including telephone, mail, face to face and focus groups), post collection processing and quantitative analysis of data (including chi-square and ANOVA), as well as report writing fundamentals. The class culminates by fielding a survey of student created questions and writing an executive summary of the survey with a paper discussing the research findings.
Prerequisite: 470.681 Probability and Statistics
This course introduces students to the R programming language. The R language is one of the most popular tools used today for performing data analytics, statistics, machine learning, data visualization, and much more. By the end of this course, students will understand fundamental programming concepts that apply to all programming languages. These concepts include variables, functions, loops, data structures, and data types. The course will also cover the use of these tools to solve challenging data problems that students may encounter in their academic or professional careers.
Note: The course overlaps a small amount with 470.681 Probability and Statistics, but this course focuses much more heavily on the fundamentals of programming.
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 address the practical applications of artificial intelligence particularly in the realms of policy and governance. AI and data science are transformational technologies that hold the promise of improving lives and society at large. While excitement about AI and its applications is growing, its adoption is anything but straightforward. The successful application of AI to lower risk, better understand customers and automate decision making requires a deep knowledge of the right use cases where AI can lead to breakthrough innovations.
This course will provide students with the opportunity to investigate multiple AI use cases and evaluate their merit. In addition, students will select a specific use case, develop reference architecture and determine an appropriate implementation strategy. The course will culminate in the development and delivery of a lab-to-market strategy for their selected use case.
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
From the perspective of a nonprofit leader, this course provides a solid foundation in understanding key financial tools such as audits, financial statements, budgets and tax documents. Using these tools, students will analyze and assess the financial transparency, accountability, and health of various national and international organizations, determine the financial strengths and weaknesses within those organizations, learn how to use that information in the decision-making process, and finally, practice making informed recommendations to organizational leadership. This course is not designed to make students financial experts or practitioners. Instead, it is designed to enlighten students on key financial management concepts that improve their ability to be informed leaders, participants, and donors in the nonprofit sector. Students will also explore the responsibilities and consequences of international nonprofits engaging in activities in the US, as well as implications for US nonprofits operating abroad. This is an elective course for the Certificate in Nonprofit Management.
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 nationâ€™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 studentâ€™s studies of the political process. Special Note: This course will require one week of residency in Washington, D.C. for the week of May 12-17, 2019.All travel and accommodations (food and lodging) in DC will be covered by the student. In addition to the course tuition, a $300 course fee will be charged to help cover costs for course incidentals.
Social media is now present globally in everyday life, and in conflicts. With its reach, social media has also become an increasingly meaningful information source for scholars, advocacy groups, intelligence agencies, and others who are interested in shaping public discourse. This course introduces students to social media as part of present day open source information gathering, and how to plan collection and conduct analysis of information from social media. The course covers the operations security considerations, monitoring real time events, verification of online material, basics of social network analysis, and how to work with imagery sourced from social media, including geolocation of imagery. Automation and the limits of it in different phases of the process, and future developments in social media exploitation will also be discussed. During the course, students will conduct a hands-on investigation using social media data.
The course will cover the art of communicating geospatial intelligence in writing, photographs or images, and mapping. It will address the challenges of communicating technical information and intelligence from satellites, aircraft, and drones, into text, combinations of text, graphics, maps, and data base,. The students will perform their own analysis, and convert their intelligence discoveries into data bases, reporting, analysis, briefings, and video-based presentations.
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.