MS in Data Analytics and Policy Degree Details and Courses

This course introduces students to the fundamentals of applied statistical analysis for policy and politics. Students will learn the building blocks of exploratory data analysis and causal inference, including summary statistics, sampling, measurement, hypothesis testing, linear regression and probability theory. 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. This course introduces the R programming language. Prerequisites: none

Machine learning (ML) and, more broadly, artificial intelligence can now be used to perform complex tasks in data science and social science. This course introduces students to a variety of these machine learning techniques. Students will learn the fundamentals of statistical software used for ML and develop an understanding of statistical and mathematical foundations of ML. Students will implement these techniques using open source tools in R and Python. Further, students will learn how to select an appropriate ML tool depending on the dataset they have and the question to be answered. Prerequisite: 470.681 Probability and Statistics and 470.768 Programming and Data Management (470.768 may be taken concurrently with 470.667).

This course instructs students in various visualization techniques and software, such as R, Tableau, and vector graphics software. 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 present compelling, informative and accurate visualizations. Prerequisite: 470.681 Probability and Statistics and 470.768, Programming and Data Management (470.768 may be taken concurrently with 470.673).

Data-driven decision making in public policy often investigates the causes of problems and the effects of policies. However, causal identification is extremely difficult, and the application of statistical processes can often be misleading. The course builds on the foundation from prerequisite courses and provides a more rigorous and faster-paced survey of statistics, regression modeling, and research design. Specific topics include measures of fit, generalized linear models, interaction terms, model specification, and common econometric tools like instrumental variables or difference in difference designs. The goal of the course is to use quantitative data in an applied manner to address meaningful research questions. Prerequisites: 470.681, Introduction to Data Analytics and Policy

This course introduces students to statistical programming. Computer programming languages are important tools 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. No prerequisite.

This course is for students who are completing their M.S. in Data Analytics and Policy. The course guides students through the process of developing and executing an original data analysis project aimed at addressing an issue related to public policy, politics, or governance. Students will formulate an empirical research question and answer that question using a quantitative analysis that makes an original, scholarly contribution. To complete the project, students will use the skills, tools and knowledge they have acquired throughout the program. Students should take this course in their final term (or penultimate term with permission from their advisor). Prerequisites: 470.681, Introduction to Data Analytics and Policy; 470.768 Programming and Data Management; 470.673 Data Visualization; 470.709 Quantitative Methods for Policy and Political Analysis.

In this course, students will develop expertise with 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. Prerequisites: 470.667, Machine Learning Methods and Applications.

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. Prerequisites: None.

This class applies data analytic skills to the civic context, analyzing problems and datasets involving urban issues and civic technology. Students will develop the statistical and computational skills to complete data-driven analytical projects using data from city agencies, federal census data, and other sources, including NGOs that work with cities and civic technology. We will examine a variety of data sets and research projects, both historical and contemporary, that examine civic technology and urban problems from a quantitative perspective. Prerequisites: 470.667, Machine Learning Methods and Applications OR 470.709, Quantitative Methods for Policy and Political Analysis OR 470.854, Fundamentals of Quantitative Methods (AS.470.667 or AS.470.709 may be taken concurrently).

This course offers a survey of statistical modeling techniques for complex data structures, such as time-series, multilevel, and network data. It covers foundational concepts and practical methods for analyzing dependencies and relationships within these data types. The course also introduces students to computational tools and strategies for efficiently processing and analyzing large-scale datasets. Through a blend of theoretical instruction and hands-on application, students will develop the skills necessary to select, implement, and interpret appropriate models for diverse data challenges in a modern data science context. Prerequisite: 470.709, Quantitative Methods for Policy and Political Analysis

This course is an introduction to the tools used to create chat and analytical artificial intelligence (AI) applications including the ethical considerations associated with AI including bias, transparency, fairness, accuracy and the methods used to measure and assess the results produced by AI. The different forms of AI in the public sector are evaluated using the AI products currently in use at the federal, state, and local level. The application of AI for diverse policy areas including policing, disaster mitigation, and foreign policy and citizens’ responses to interactions with AI in service provision are also covered, along with current and emerging policy guidelines for public sector AI.

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: 470.667, Machine Learning Methods and Applications OR 470.709, Quantitative Methods for Policy and Political Analysis OR 470.854, Fundamentals of Quantitative Methods (AS.470.667 or AS.470.709 may be taken concurrently).

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. Prerequisites: Prerequisites: 470.667, Machine Learning Methods and Applications OR 470.709, Quantitative Methods for Policy and Political Analysis OR 470.854, Fundamentals of Quantitative Methods (AS.470.667 or AS.470.709 may be taken concurrently).

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. Prerequisite: 470.667, Machine Learning Methods and Applications OR 470.709, Quantitative Methods for Policy and Political Analysis (470.667 or 470.709 may be taken concurrently as a co-requisite).

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)

Americans traditionally have viewed the courts as—in the words of a constitutional scholar—"the least dangerous branch of government." They are seen as reflectors, not agents, of change. But in an age of government downsizing, the role of the courts bears renewed examination. Students explore the historical and philosophical roots for the notion that American courts, and whether the lawyers who appear before them, can and should make law and policy, and the alternatives to this function. Students consider prominent areas of public policy that have been shaped by the courts, such as civil rights, family and domestic law, environmental and safety regulation, and the regulation of business and commerce. This course counts towards the Legal Studies Concentration.

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 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.)

Lobbying is a constitutional right guaranteed under the First Amendment. It's also big business in Washington, D.C., as more than $4.2 billion was spent on these efforts during the first six months of 2023 alone. 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 or attend Zoom meetings, 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 a company, non-profit, trade association either reaches out to constituents of a specific U.S. House district or state to involve citizens in their efforts or hire unregistered public affairs firms employing campaign-styled tactics to persuade decision-makers to support their client’s positions. This course counts towards the Concentration in Political Communications.

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.)

This course examines the process of drafting legislation and the consequences of legislative language in the implementation and adjudication of federal policies. Focusing on the various stages of the legislative process, this course considers the expert and political sources of the legislative language in the U.S. Congress and the importance of language in coalition-building for policy passage. Examining the interactions of Congress with the other branches of government, the course also considers how presidents, the executive branch, and the judiciary interpret statutory language.

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.

Congress is the First Branch, “the People’s Branch,” and one of the most powerful legislatures the world has ever known. At this moment in history, however, the people do not assess the institution favorably and political scientists and pundits have declared it the “broken branch.” Is Congress “broken” or merely reflective of our political times? In an era of “unorthodox lawmaking” is a return to “regular order” and “textbook lawmaking” realistic or a fantasy? This course will discuss these questions in the context of the evolving nature of Congress as an institution. The class will examine the institutional development of Congress and explore changes in its representative and legislative functions, as well as constitutional responsibility of holding the “power of the purse.” Congress remains a dynamic institution and it behooves citizens to understand its complexity and centrality to governance in the U.S.

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 a core class for the MA Nonprofit Management degree.

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 about the systematic examination of data from diverse sources to understand complex situations, identify threats or opportunities, and support informed decision-making. Students will examine materials from experts on intelligence analysis, and discuss complex psychological, organizational, and other key issues that relate to intelligence analysis in order to develop the knowledge and skills to analyze information and apply analytical tradecraft to complex problems. They will learn to collect, analyze, and interpret information, and communicate clearly and concisely. Through a peer review process, they will provide constructive feedback on their own analytic writing projects.

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.

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.

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 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. Offered once a year in Spring.

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

A well-designed database is necessary to construct relevant spatial data queries. In this course, students learn the different database designs for stand-alone databases 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: 430.600 Web GIS.

Spatial data quality is a major concern for any GIS. This course examines the nature of errors in spatial data and various aspects of spatial data quality, including positional and thematic accuracy, resolution, precision, completeness and logical consistency. The impacts of errors on the reliability of GIS-based analysis are explored. Various strategies to improve the quality of spatial data are addressed, including the use of standards for spatial data (FGDC, OGC and ISO) and data management tools. Offered once a year. Prerequisite: 430.601 Geographic Information Systems,

This course will familiarize students with applications of Geographic Information Systems (GIS) for infrastructure management. Building, utilizing and sharing reliable asset information and integrating enterprise data will be emphasized, in order to help stakeholders make informed decisions and capitalize on efficiencies of using GIS to support various kinds of facilities and infrastructure. Students will have the opportunity to use GIS applications to do project work in support of facility operations, strategic planning, real estate management, architecture design and construction, sustainability, utilities, buildings and interior space management, drones mapping, among others. Samples will be drawn from large university enterprise with multiple campus locations yet applicable to cities and various other settings. Research and spatial analysis will be conducted using recently acquired GIS orthoimagery, LIDAR and planimetric data for the Johns Hopkins' own Homewood campus. Prerequisite: 430.601 Geographic Information Systems.

The Cartographic Design and Visualization course focuses on the fundamentals of cartography, spatial statistics, thematic mapping techniques, 3D mapping, and web based mapping. Students will gain an inter-disciplinary understanding of cartographic representation and visualization with hands on applications using cutting edge GIS and graphic design software to create purpose tailored maps. Upon successful completion of this course, students will be able to interpret and appropriately communicate spatial data; will have developed a personalized cartographic style; will have created a professional GIS portfolio for current/potential employers; and most importantly will have developed a keen appreciation for maps and spatial awareness! Offered once a year. Prerequisite: 430.601 Geographic Information Systems.

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 twice a year. Prerequisites: 430.606 Programming in GIS. Python programming experience is highly recommended.

Census data is the most often used data in geospatial studies. Census data provide information on the demographic composition of households 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 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 a year. Prerequisite: 430.601 Geographic Information Systems, or permission of the instructor.

This course is designed to provide students with experience in web programming and application development. It focuses on uses of Web APIs for developing rich and interactive web mapping applications. HTML, CSS and several popular JavaScript frameworks, such as Dojo, JQuery and AngularJS, will be covered. Interchange languages (JSON, XML) and responsive design will also be explored. Widgets will be examined to quickly develop solutions, and emphasis will be placed on tasks which provide further functionality. Conceptual and technical documentation, and samples, will be greatly utilized. The course will facilitate heavy engagement with the large and growing community of Web API developers. Offered once a year. Prerequisite: 430.600 Web GIS

Geographic Information Systems (GIS) have become an integral part of understanding the natural hazards in our world and how emergency management agencies respond to events and mitigate the impact of disasters. Furthermore, the advent of Web GIS has helped agencies overcome many challenges previously associated with GIS in Emergency Management. This course is an opportunity to learn about the use of GIS in studying natural hazards and apply cutting edge GIS technology to help emergency management agencies in the field. In today's device-driven world, maps need to work on mobile devices so there will be an emphasis on enabling GIS in the field. You will use Web GIS to deploy maps that assist agencies with their incident command functions: Planning, Operations, Logistics, Command, and Public Information. While the industry focus will be on Emergency Management, the knowledge, skills and abilities you develop will be widely applicable in both public and private sector industries. Offered once a year. Prerequisite: 430.601 Geographic Information Systems or permission of the instructor.

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: 430.606 Programming in GIS. Python programming experience is required.

This compressed format field course will explore current and future techniques of close-range remote sensing utilizing unmanned aerial vehicles (UAVs or drones) for environmental monitoring, urban cybersensing, and infrastructure assessment related to emergency response, leading to FAA Remote Pilot Certification to operate UAVs for commercial, professional, and research purposes. The course will focus on four basic objectives: (1) demonstrating knowledge and operational skills to successfully execute data acquisition using a variety of UAV remote sensing collection devices; (2) applying different methods of data acquisition and processing to identify, cross-validate and interpret data collected from UAV sensors; (3) discussing existing and emerging trends of UAV applications in various academic and professional situations, and (4) synthesizing and extrapolating data from these novel collection techniques to solve real-world problems. Students will act as flight crewmembers and scientific crew on numerous daily missions during the field portion of the course. Prerequisite: 430.600 Web GIS, or 420.603 Environmental Applications of GIS, or introductory GIS course.

This course will teach students about the fundamental data structures and algorithms behind GIS and computer science. These data structures and algorithms are what all complex GIS systems are built upon. The topics presented are a mixture of computer science data structures and computational geometry topics. This course will stress code optimization and runtime analysis of code, teaching students how to program efficiently – just because a set of code works, it does not mean it is optimal. The course will use Python to cover such fundamental concepts and help students become better GIS Professionals. Offered once a year.

This course will leverage geospatial technology to analyze urban spatial problems relevant to contemporary urban planning and design practices. It provides students the opportunity to integrate spatial information and enhance decision making when working with urban environments. Focus is on understanding the business requirements for urban designs, along with use of spatial patterns and big data in smart city planning. Emphasis will also be placed on digital transformation of urban planning to encourage collaboration with community stakeholders and drive efforts towards sustainable cities. Application problems addressed will be within areas of urban planning and design, business decision-making, social, and political and environmental issues, among others. Prerequisites: 430.600 Web GIS