Course Descriptions

Core Courses

430.615 Big Data Analytics: Tools and Techniques

The explosion of data collection methods from a vast array of data sources in volumes previously unimaginable has tested the limits of traditional technology, which are not able to scale to the requirements of massive data. Big Data is the field of data studies where the data is identified by very large volumes, high velocity in data generation, and data format variety. This course explores Big Data technologies while utilizing cloud infrastructures. We will discuss the characteristics and architectural challenges surrounding Big Data, and explore geo-visualization techniques of data processed using Big Data Analytics. Students will work in a cloud computing environment to build Hadoop clusters, NoSQL databases, and work with other open source technologies to process data stores like Census data, and Twitter feeds. Offered once a year.

Prerequisite: 430. 600 Web Mapping, 430.601 Geographic Information Systems. Programming experience is highly recommended.

470.660 Program Evaluation

Program Evaluation is the systematic use of empirical information to assess and improve the efficacy of public or non-profit programs and policies. Evaluation is increasingly required by funders and policymakers concerned with accountability and efficient use of public or philanthropic resources. In addition, many governments and organizations have built the logic of evaluation into their work through systems of performance management and monitoring.

This course introduces the student to the literature, theories, and approaches to evaluating organizational programs, policies and procedures. Students will acquire a broad perspective on types of program evaluation, including formative and summative evaluation, process evaluation, monitoring of outputs and outcomes, impact assessment, and cost analysis. Students gain practical experience through exercises and assignments involving the design of a conceptual framework, development of indicators, analysis of quantitative and qualitative evaluation data, and development of an evaluation plan to measure impact. In addition, topics such as experimental, quasi-experimental, and non-experimental study designs are introduced in the context of a variety of settings, including schools, welfare agencies, mental health organizations, criminal justice settings, environmental programs, nonprofit organizations, and corporations.

Prerequisite: 470.709 Quantitative Methods

470.673 Data Visualization

This course instructs students in various visualization techniques and software. Students will learn how to: (1) ask interesting questions about politics, (2) identify data that can be used to answer those questions, (3) collect, clean and document the data, (4) explore and analyze the data with statistical and graphical techniques, (5) create compelling, informative and accurate visualizations and (6) present these visualizations to educated audiences.

Prerequisite: 470.681 Statistics and Political Analysis.

Important Note: For the onsite version, this course REQUIRES that you bring a laptop that supports Chrome to all class meetings. For the online version, students must have a computer that supports Chrome.

470.681 Statistics and Political Analysis

Introduces students to the concepts central to social science research design and methods used to summarize and present quantitative data. Applications using political and public policy data will be featured. Topics covered include research question formulation, cross tabulations, controlled comparisons, hypothesis testing and bivariate regression analysis. In addition, students will learn to use R, a powerful software program that is popular among political consulting firms, think tanks and government agencies.

The course is at the introductory level; there is no prerequisite.

470.694 Big Data Management Systems

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.

Technology Requirements:

  • 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.

470.699 Applied Performance Analytics

Data is 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 data analysis? How do you 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.

The course will culminate in a small group project where each group will apply their knowledge gained from the course to create and submit a budget neutral proposal to improve performance on the common challenges studied in the course. Students will share their recommended solutions and reflect upon lessons learned from the simulation and how they will apply those lessons in the real world.

Students will be required to use Excel in the course; the instructors strongly recommend that students have a working knowledge of Excel. Students should be able to execute all of the following functions in Excel: sorting, filtering, pivoting, and making a chart.

Recommended prerequisite: Statistics and Political Analysis.

470.709 Quantitative Methods

Solutions to both political and policy problems increasingly require an understanding of how to understand and analyze data. Campaigns collect data to identify potential supporters and donors. Government agencies analyze data to evaluate programs. Research organizations use data to support their policy positions. This course will provide you with the knowledge and skills needed to perform a cutting-edge statistical analysis. You will learn how to design and test regression models using Stata, an incredibly powerful and widely-used statistical software package. Other topics include interaction terms, measures of fit, internal and external validity, logistic and probit regression, and translating statistical findings for broad audiences. The focus of the course will be on using statistical methods in an applied manner. We will concentrate on using statistics to answer political and policy questions, not on the underlying mathematical theories.

Recommended prerequisite: 470.681 Statistics and Political Analysis.

470.710 Advanced Quantitative Methods

This course builds upon the concepts taught in Quantitative Methods. Students will learn how to construct and evaluate advanced regression models. Topics include experimental data, instrumental variables, panel data, matching and multiple imputation.

Prerequisite: 470.709 Quantitative Methods.

470.736 Methods of Policy Analytics

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.

Prerequisite: 470.681 Statistics and Political Analysis.

470.743 Data Mining and Predictive Analytics

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 areas such as fraud, healthcare, fundraising, human resources and others. In addition, students will learn to build segmentation models using clustering techniques in an applied manner. Integration with other statistical tools and visualization options will be discussed.

Prerequisites: 470.681 Statistics and Policy Analysis and 470.709 Quantitative Methods.

470.769 Data Science and Public Policy

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, cluster analysis, outlier detection, and text analytics 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 helpful.

Prerequisite: 470.681 Statistics and Political Analysis.

470.862 Capstone for Government Analytics

This course guides students through the process of developing and executing an original data analysis project aimed at addressing a public policy, political or governance challenge.

Prerequisites: Statistics and Political Analysis, Quantitative Methods, Advanced Quantitative Methods.

Elective Courses

470.608 Public Policy Evaluation and the Policy Process

This course is designed to introduce students to the public policy-making 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 review s 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.

470.613 Managing Risk and Performance: Improving Decisionmaking in Government Agencies

The United States has experienced the most significant failure of its financial system since the Great Depression. Differences in governance and management between the survivors and the others are instructive not only for financial firms, but for government agencies and private companies in other sectors of the economy. This course seeks to present learnings that are potentially relevant to government managers and organizations. The basic lesson, of course, is that low probability events with devastating consequences do happen. Nicolas Nassim Taleb (2007) calls such events “black swans.” He argues that they take place much more frequently than people expect. Managers must take the possibility of black swans into account even when times are good; that’s one factor that distinguishes the survivors from the rest. The federal government and private sector have learned this from Katrina, the massive 2010 Gulf oil spill, homeland security events such as September 11, and the Great Recession that emerged from the financial crisis. All of these occurred within a single decade. Students will be expected to produce a research paper on an approved topic relating to (1) a crosscutting theme of governance and risk management at one or more private companies, (2) government regulation and supervision of risk management at one or more private companies, or (3) a cross-cutting theme of governance and risk management at government agencies. Students will be encouraged to make the course an interactive one and to share their personal knowledge of successes and failures of governance and risk management.

470.623 Nonprofit Program Development and Evaluation

A major goal of this course is to help students become more proficient in recognizing, evaluating, and encouraging the kinds of benefits or outcomes intended by our society’s variety of nonprofit and public programs. We will examine what needs and opportunities are addressed by four major types of programs: those serving individuals, those serving communities, those serving networks or systems, and those serving other organizations. Evaluating each requires different lenses and different tools; we will explore the role of culture and context in choosing particular approaches to evaluation. A view of programs as interconnected rather than isolated will be encouraged. A second goal is to help students become more proficient in managing an evaluation process: we will explore purposes and uses of evaluation, the essential elements of an evaluation inquiry, and ways to communicate and use evaluation results. We will explore the variety of quantitative and qualitative criteria that are useful for evaluating progress in an organization’s attainment of its intended outcomes or benefits. Students can expect to become more proficient in discussing issues of nonprofit and public program effectiveness and strategies for improving nonprofit and public program designs.

470.624 Healthcare Analytics and Policy

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.

Prerequisite: 470.681 Statistics and Political Analysis.

470.627 Financial Management and Analysis in the Public Sector

Many Americans believe that there was a time when citizens were free of government controls. But there always have been significant government controls, which in our day we call public policies. This course analyzes major economic policy tools and their advantages and disadvantages. It provides an overview of issues confronting the American economy today including productivity, employment, international trade, and distribution of wealth and incomes. Students explore specific policy tools available to influence economic outcomes, among them monetary and fiscal policy, trade regulation, grantmaking, entitlement spending, and specialized interventions such as health care.

470.631 Economics for Public Decisionmaking

This course provides a basic understanding of macro- and micro-economics. Students will be given a survey of conventional economic theory and asked to think critically about when markets function properly versus when government interventions are necessary to achieve desired outcomes. Students will also learn how to apply economic thinking to a number of public policy areas including taxation, entitlement spending, environmental/energy policy, monetary policy, and economic stimulus.

470.643 Text as Data

Text is not straightforward. In this course, students will develop 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, then proceeds to the key statistical concepts in machine learning and statistics used to analyze text as data. Throughout the course, students develop a research project that culminates in the online display of results from a large-scale textual analysis.

NOTE: At a minimum, students in this course should have some programming experience with R and must have a basic understanding of statistical concepts like distributions, model-based inference, and uncertainty.

Prerequisite: 470.681 Statistics and Political Analysis.

470.645 The Budgetary Process

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.

470.667 Machine Learning and Neural Networks

Machine learning and, more broadly, artificial intelligence, has recently had a series of unprecedented successes in performing tasks such as image recognition and autonomously playing video games at a higher level of accuracy and performance than humans. These successes are driven by accelerated developments in machine learning, notably neural networks.

This course will cover a variety of machine learning algorithms from linear regression to nonlinear neural networks. Students will learn to implement these algorithms and understand 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 the algorithm to generate predictions.

For the onsite version, students are REQUIRED to bring a laptop to class; the laptop should be a PC or Mac laptop (not Chromebook) with 4GB RAM (preferably 8GB) minimum. Please contact the instructor with questions.

Prerequisite: 470.681 Statistics and Political Analysis.

470.669 Math for Data Scientists

This course reviews the mathematical principles that are fundamental to quantitative analysis. The course covers functions, probability theory, integral and derivative calculus and matrix algebra.

470.671 Risk Management in the Public Sector

The demand for robust and resilient risk management practices is increasing in the public sector as organizations continue to struggle with explicitly integrating risks into their executive decision making processes. OMB’s recent revision of A-123 places additional pressure on this imperative. The objective of this course is to introduce students to fundamental risk management and measurement practices and demonstrate their relevance to the government sector. It will help students understand risk management principles and practices and how they might apply to their organization. The goal is to give students a comprehensive view of both the risk management processes and some of the key measurement tools for understanding and mitigating operational, credit, market and enterprise risks exposures.

Prerequisites: 470.681 Statistics and Political Analysis and 470.709 Quantitative Methods. If you’ve taken a different statistics course, check with the instructor prior to enrolling.

Important Note: This course requires that students use Excel (on their personal computer) and purchase @Risk software ($50).

470.674 Advanced Data Visualization

This course advances students’ understanding of modern data visualization by introducing concepts and tools that enable unconstrained graphical representation of information. Students will learn to create any visualization they can imagine with the expressive and powerful D3 library, used by organizations like the New York Times, the Guardian, and the Urban Institute. The course will examine the design and development of dynamic, interactive presentations that allow for personalized and non-linear modes of data exploration. Each student will publish a final project live on the web, employing the same tools and processes used by professional information designers and web developers, including HTML, CSS, Javascript, version control, and rapid application development.

Prerequisite: 470.673 Data Visualization.

Note: This course requires students to bring a laptop to onsite class meetings.

470.675 Measurement for Government Analytics

Many of the questions posed to government and NGO researchers involve trying to systematically analyze hard-to-measure ideas. Was a program successful? How much popular support might there be for a policy that the public knows little about? How democratic is a country? This course will introduce students to the challenges of and strategies for successfully approaching measurement for government analytics. The focus is on the tasks of conceptualization, operationalization, data collection, and data validation for government analytics. Students will learn to both evaluate and use existing data sources for their own research as well as strategies for collecting and assessing original data.

470.688 Political Institutions and the Policy 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.

470.695 Essentials of Public and Private Management

The purpose of the class is to help equip students to operate effectively in both the public and private sectors. The class will cover three major topics: (1) an overview of managing public and private organizations, with special attention to their differing missions, capabilities, and environments, (2) a survey of important relationships between the public and private sectors, and (3) the need for improved coordination between the public and private sectors to achieve important public purposes. Students will be encouraged to make the course an interactive one and to share their personal knowledge in the context of the issues discussed. Students will be expected to complete a significant paper on a relevant topic approved by the instructor.

470.700 Cloud Computing in the Public Sector

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 computer 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.

470.703 Urban Data Analytics

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, which 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, and analyzing it, culminating in a research paper.

Prerequisite: 470.681 Statistics and Political Analysis

470.708 Unleashing Open Data with Python

Learning the basics of the computing language, Python, empowers people to retrieve and analyze data in new ways. During the course, students with no prior coding experience will learn how to gather and analyze data in ways that are not possible without the assistance of programming. After covering the fundamentals of syntax and logical thinking, students learn how to read, create and edit files. Then, building on that knowledge, students interact with online resources through web scraping and APIs. Finally, students will use the data they collected to create their own analysis and publish their research to a website. The class equips students to add programming components to their future work, giving them an advantage in a competitive workplace.

470.731 Privacy in a Data-driven Society

This course will address 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. Our cases will range from 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”.

470.738 Civic Technology and Smart Cities

Civic technology is an emerging field that combines the work of those in and out of government for government innovation. 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. Topics covered in the course include open data platforms and policies, algorithms employed in civic tech, and the civic tech organization ecosystem. Smart city technology is a distinct but related field that involves the management of city infrastructure and services with the goal of improving the quality of life of citizens through the use of information and communication technology (ICT). Topics include smart infrastructure, connected technologies, and sustainability. Students will use R to build dashboards, open data portals, and maps. Students will use information to identify a community need and design an application that addresses the need. Some familiarity with the R programming language and the RStudio environment is helpful.

Prerequisite: 470.681 Statistics and Political Analysis.

470.758 Data-driven Campaigns and Elections

This course focuses on the central role that data is playing in campaigns and elections in America. Data is increasingly becoming central to decision-making, strategizing, and forecasting in modern American campaigns. Voter rolls, consumer data, and public opinion polls are the tools of campaign strategists, policy analysts, and social scientists. Relying on Big Data, campaigns identify potential supporters and funnel resources accordingly. Pollsters keep close track of public opinion throughout the course of campaigns to predict election results and voting behavior. Political scientists and other analysts use such data to answer important questions about political behavior and American democracy. The course surveys the theoretical and empirical literature in American politics to study how campaigns and political organizations are using field experiments, microtargeting, and public opinion polling to tackle the challenges of getting out the vote from potential partisans and of increasing registration and voting rates from likely supporters. Other topics covered include voting behavior and turnout, public opinion, partisanship, and campaign financing. Students will gain a rich understanding of how data is becoming a key component of the electoral process and an understanding of the literature in campaigns and elections in America.

470.763 Database Management Systems

This course provides students with a strong foundation in database architecture and database management systems. The principles and methodologies of database design and techniques for database application development are evaluated. The current trends in modern database technologies such as Relational Database Management Systems (RDBMS), NoSQL Databases Cloud Databases, and Graph Databases are examined.

470.764 Survey Methodology

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, IRB, 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.

470.766 Economic Growth: The Politics of Development in Asia, Africa and Beyond

What makes some countries grow while others do not? What accounts for successful economic development versus stagnation? As these questions become ever more relevant in an increasingly globalized world, this course offers an introduction to the topic. The class will provide an overview of the main classic and current theories of economic development. It will then go on to explore specific current issues in development, including: development aid, role of international organizations, sustainable development, corruption, institution building and regime type. Specific case studies will be examined including China and India, the East Asian “tigers,” development failures in Africa and mixed outcomes in Latin America.

470.768 Programming and Data Management

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 bit with 470.681 Statistics and Political Analysis, but this course focuses much more heavily on the fundamentals of programming.

470.772 Practical Applications of Artificial Intelligence

Artificial Intelligence and Data Science are transformational technologies that hold the promise of improving lives and our society at large. While the hype around AI is growing, its adoption is anything but straightforward. The successful application of AI to lower risk, understand customers better and automate decision making requires a deep understanding of the right use cases where AI can lead to breakthrough innovations.

This course will provide students with an opportunity to investigate multiple AI use cases and evaluate their merit. While no coding in R or Python is required, students will develop use cases, develop reference architectures and implementation strategies for nine industry verticals (including healthcare, energy, transportation, smart cities). The course culminates in the development of a lab-to-market strategy for a student-selected use case.

470.779 Computational Modeling for Policy and Security Analysis

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.

470.793 The Influence of Public Opinion on Public Policy and American Democracy

This course discusses the theories and methods used to design and implement public opinion surveys in areas of public policy and politics. Topics include sampling design and margin of error; question design (wording and order); measurement of concepts (reliability and validity); measurement error (e.g., social desirability bias, recall problems); interviewing methodology; cross-sectional vs. panel surveys; and theories of survey response.

470.798 Financial Management and Analysis in Nonprofits

The basic tools for financial management and analysis are covered in this course with a focus on those aspects that will: 1) provide needed skills to students planning careers in public and nonprofit organizations and 2) provide those working for government with tools to evaluate nonprofit and private sector organizations with which they interact. Topics include legal and audit requirements for financial reporting, disclosure laws, and state and federal registration requirements. The course will also address interpreting financial statements and assessing and managing for financial health. These basic management tools are necessary not only for basic financial management but also for creating the financial component of a Request For Proposal (RFP) from a US funding source and for those striving for organizational sustainability through social enterprise or earned income ventures in general.

430.600 Web GIS

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.

430.601 Geographic Information Systems (GIS)

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.

430.602 Remote Sensing: Earth Observing Systems and Applications

This course introduces remote sensing as an important technology to further our understanding of Earth’s land, atmospheric, and oceanic processes. Students study remote sensing science, techniques, and satellite technologies to become familiar with the types of information that can be obtained and how this information can be applied in the natural and social sciences. Applications include assessment of land cover and land use, mapping and analysis of natural resources, weather and climate studies, pollution detection and monitoring, disaster monitoring, and identification of oceanographic features.

430.603 Geospatial Data Modeling

This course moves beyond the fundamentals of GIS to explore the constraints surrounding data modeling as well as the methods to model spatial data. Students review current research in the field, learn relevant modeling techniques, and utilize advanced software tools for analysis. The course focuses on various kinds of spatial data, how it is collected, handled, processed, and analyzed through G2IS technologies. As the term progresses, students deal extensively with different types of data presentations and the manipulation of those data in GIS models. Students develop a significant GIS project over the course of the semester and present their findings at the end.

430.604 Spatial Analytics

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. Students will also use spatial statistics to address the distributional and locational aspects of spatial data within a variety of situations. Examples and assignments are drawn from many GIS applications, such as business, urban planning, public safety, public health, transportation and natural sciences. Offered twice a year.

430.605 Development and Management of GIS Projects

This course introduces students to project, program, and portfolio management standards, which will guide them on how to successfully manage GIS projects. Students will learn how to apply core project management principles and guidelines to real project scenarios. The course will impart knowledge and skills for managing GIS projects throughout their entire lifecycle, while addressing technical, ethical, and institutional problems. Students will explore key issues in organizational management, including earned value management, resource planning, and communications. During the course, students will learn how to determine the return on investment of a GIS project, create a comprehensive schedule and budget, as well as determining risk management, quality control, and contract management skills in support of your GIS project.

430.606 Programming in GIS

This course introduces students to various customization methods for GIS using Python and Application Programming Interfaces (APIs). Students will learn how to develop tools and automate workflows using Python scripts in the ArcGIS for Desktop interface as well as develop web mapping applications using APIs and ArcGIS for Server. Cloud computing will be introduced as Infrastructure-as-a-Service for a development platform model.

Prerequisite: Geographic Information Systems (GIS).

430.607 Spatial Databases and Data Interoperability

A well-designed geodatabase is necessary to construct relevant spatial data queries. In this course, students learn the different geodatabase designs for stand-alone geodatabases and enterprise database systems. This course examines the requirements for a GIS Decision Support System by focusing on the design of the data schema, identifying the necessary data elements and their formats, and exploring data Interoperability as a designed constituent of a database. Data management routines for maintaining the spatial integrity will also be introduced. Offered once a year.

Prerequisites: Geographic Information Systems (GIS), Geospatial Data Modeling.

430.608 GIS and Spatial Decision Support Systems

GIS can be a very effective tool to assist in making decisions for a wide range of applications at the local, regional and global scale. This course will examine the use of GIS as a spatial decision support system for systematic policy analysis and scenario modeling. Case-studies will be used from the areas of agriculture, conservation planning, homeland security, land use planning, natural disasters, transportation, urban planning and water resources. Offered once a year.

Prerequisites: Geographic Information Systems (GIS), Spatial Analysis with GIS.

430.613 Advanced Topics in Remote Sensing

This course explores the various remote sensing platforms, collection systems, processing methods, and classification approaches to remotely sensed data. Discussion of image adjustment techniques, relative orientation, and geo-referencing methods are compared. Topics include hyperspectral imaging, spectral analysis, and image filtering. Offered once in two years.

Prerequisites: Geographic Information Systems (GIS), Remote Sensing: Earth Observing Systems and Applications.

430.617 Demographics Modeling

Census data is the most often used data in geospatial studies. Census data provide information on the demographic composition of household all the way through state and national population trends. Census data also serve the data layers that form the basis of most mapping applications. In this course, students will learn how to work with Census data in a GIS by understanding the vast amounts of data collected in support of the decadal Census, how to discover and read the various tables that associate with the raw Census data, and how to create custom data layers for demographic models in Economics, Housing, and Population Studies. Offered once in two years.

Prerequisite: Geographic Information Systems (GIS).

430.627 Artificial Intelligence and Machine Learning in Geospatial Technology

The transformational impact of artificial intelligence and machine learning in geospatial data science is profound. This course presents a hands-on approach of applying automated modeling and predictive analytics to solve problems. Smart capabilities are powered by machine learning and GeoAI through the use of correlations of pattern detection to build predictive models and classify outcomes for data never seen before. Use cases from various sectors focusing on prediction and optimization, finding patterns and correlations, advanced object detection and automatic feature extraction, are examined. Offered once a year.

Prerequisites: 430.600 Web GIS. Python programming experience is required

State-specific Information for Online Programs

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