Graduate programs at CUSP offer a unique, interdisciplinary and cutting edge approach that links data science, statistics and analytics, and mathematics with complex urban systems, urban management, and policy. The curriculum addresses the necessary technical skills and critical problem solving frameworks in addition to providing research opportunities and real-world experiences through internships and practicums that enable students to be successful in a wide range of career trajectories. CUSP students understand how to work with data at all stages of the data lifecycle from acquisition to visualization. Furthermore, they gain knowledge about cities by using robust and live data in their class projects, applied research activities, and partnering with companies and NYC agencies addressing existing urban challenges.

The 30-credit program is designed to offer students flexibility to design their curriculum to fit their personal and professional interests. Students may seek deep training in data science and informatics as applied to various domains related to cities, or focus more on learning how to utilize analytics and data-driven decision-making techniques to inform urban operations and policy decisions.


To introduce students to the field of Urban Informatics and help prepare students for their graduate studies, NYU CUSP offers preparatory resources at the beginning of their degree. These resources help students learn the skills necessary for a successful academic career, discover resources at CUSP and NYU, and connect with other students.

Free Online Programs

There are many free online courses that our students take to augment or refresh their technical skills in these topics before applying. We suggest the following free online courses:

NYU Bridge to Tandon Program: a STEM Preparatory Course

Created for individuals with non-engineering backgrounds, the program provides you the tools needed to be admitted into select graduate-level programs at the School of Engineering.

A Bridge to NYU Tandon is a uniquely designed program to give individuals lacking a STEM background a gateway into the Applied Urban Science and Informatics graduate programs. Whether you are transitioning your career or looking to refresh your knowledge, this course will ensure a seamless start into your graduate career.

Learn more and apply here.

Urban Computing Skills Lab

To help prepare for the specific types of urban computing and data analysis students will engage in at CUSP, CUSP offers a free online prep course to admitted students. The UCSL is designed to walk students through some urban science analyses and tasks. The prep course can be completed at any pace, and also more than once.

New Student Orientation

CUSP’s New Student Orientation is an intensive 4-day program that includes a number of speakers, workshops, academic boot camps, and events that introduce students to CUSP, the field of urban informatics, and NYU resources.


Core Classes (18 Credits)

CUSP students take 6 required core classes and a non-credit lab to form the foundation of their Master of Science in Applied Urban Science and Informatics.

In the fall, these courses include Principles of Urban Informatics; Civic Analytics & Urban Intelligence; a choice between Urban Spatial Analytics, Urban Decision Models, OR Innovative City Governance; an elective; and a non-credit lab.

In the spring, CUSP students begin their 6 credit (two course) capstone with Urban Science Intensive I, as well as take Applied Data Science and two electives.

In the summer, CUSP students conclude their capstone with Urban Science Intensive II, and take an elective.

This course is the introduction to data and analytics strategies, tactics, tools that cities deploy in order to bring resolution to a wide variety of complex and important challenges and concerns. This will include detailed accounting of the use of data acquisition and management, integration, and analytics through the thorough investigation of case studies. In this course, the student will learn the major concepts, tools, and techniques for what informatics can do for cities.  It includes background in how data management, visualization, and data science, have been successfully and unsuccessfully used in each case study.After this class you should be able to formulate a question relevant to Urban Science, find an appropriate data to answer the question, prepare and analyze the data, get an answer, to whichever confidence level, and communicate your answer, and your confidence level in the answer.

Skills Learned: Sourcing, analyzing, and presenting data; open data analysis; GIS data and analysis; Arc GIS; machine learning; working with urban data

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Cities are increasingly data-rich environments, and data-driven approaches to operations, policy, and planning are beginning to emerge as a way to address global social challenges of sustainability, resilience, social equity, and quality of life. Understanding the various types of urban data and data sources – structured and unstructured, from land use records to social media and video – and how to manage, integrate, and analyze these data are critical skills to improve the functioning of urban systems, more effectively design and evaluate policy intervention, and support evidenced-based urban planning and design. While the marketing rhetoric around Smart Cities is replete with unfulfilled promises, and the persistent use (and mis-use) of the term Big Data has generated confusion and distrust around potential applications. Despite this, the reality remains that disruptive shifts in ubiquitous data collection (including mobile devices, GPS, social media, and synoptic video) and the ability to store, manage, and analyze massive datasets require students to have new capabilities that respond to these innovations.

This course introduces students to computational approaches to urban challenges through the lens of city operations, public policy, and urban planning. Students are exposed to a range of analytical techniques and methods from the perspective of urban decision-making. Issues of city governance, structure, and history are presented to understand how to identify and assess urban problems, collect and organize appropriate data, utilize suitable analytical approaches, and ultimately produce results that recognize the constraints faced by city agencies and policymakers. This is not an easy task, and requires an understanding of urban social and political dynamics and a significant appreciation of data governance, privacy, and ethics. Specific attention is given to domain areas of energy and building efficiency, transportation, public health and emergency response, waste, water, and social connectivity and resilience, as well as the deployment of urban technology at the neighborhood scale. The role of civic engagement and community participation in the context of open data and citizen science is explored, as well as the evolving relationship between, and influence of, informatics on urban governance. Top-down and bottom-up models of innovative service delivery are discussed and debated in the context of public decision-making. Case studies and best practice examples from U.S. and global cities are used extensively, with a particular focus on New York City.

Skills Learned: Data collection, basics of urban policy, urban operations

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This course will introduce you to urban governance and its current innovation trends. Urban governance comprises of the various forces, institutions, and movements that guide economic, politic, social and physical development, the distribution of resources, social interactions, and other aspects of daily life in cities. Public-sector innovation is indispensable to solve the complex urban challenges we are facing and can bring significant improvements in the services that the government has a responsibility to provide, including those delivered by third parties. Following a Discovery-Design-Delivery approach, students will learn the complex nature of cities, different strategies to solve public problems, how urban administration works and how public policies are crafted, how we can promote urban governance innovation, why collaboration is a must and which are the best tactics to promote effective public-private partnerships and networks, how we can support public engagement at all stages of the policymaking cycle, how to promote effective communications using current technology available, ethical issues that may arise when applying analytics to policy problems, how we can connect artificial and collective intelligence, and different approaches to measuring organizational performance. This course will help students to become public entrepreneurs that know how to effectively deliver data and innovation projects into an urban environment.

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Please note: students choose between Urban Spatial Analytics (CUSP-GX 7002) and Urban Decision Models (CUSP-GX 7004) during their fall semester.

In this course, students will learn how spatial analysis can support the exploration of geographical properties, patterns, and phenomena in urban context. The course will cover the foundations of spatial analysis in the spatial sciences, examining in particular how spatial science influences data collection, data modeling, data analysis, and data interpretation. The course will explore the derivation of core spatial statistics and geostatistics that are routinely used in geographical analysis. The course will also examine the use of spatial analysis in supporting spatial modeling. Students are expected to have undertaken prior coursework in Geographic Information Systems (GIS). Labs will focus on how to run spatial analysis methods from GIS platforms. Example sets in the course will focus on urban applications.

Syllabus available upon request; please email

Please note: students choose between Urban Spatial Analytics (CUSP-GX 7002) and Urban Decision Models (CUSP-GX 7004) during their fall semester.

This course provides an introduction to computer-based optimization and simulation models for decision-making for government officials and policy makers. The emphasis is on models that are widely used in diverse functional areas, including every day operations such as waste collection, policing and transportation to policy making on environment/climate change to sheltering the homeless. Applications will include resource allocation, workforce planning, revenue management, asset-liability management (public sector finance models), environmental policy modeling, pension and bonding planning, and political campaign management, among others. The aim of the course is to help students become intelligent consumers of these methods. To this end, the course will cover the basic elements of modeling — how to formulate a model and how to use and interpret the information a model produces. The course will attempt to instill a critical viewpoint towards decision models, recognizing that they are powerful but limited tools.

Skills Learned: Basic elements of computer-based modeling, advanced Excel 

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This class will teach you to recognize where and understand why ethical issues can arise when applying analytics to urban problems. You will consider issues across the lifecycle of projects that aim to improve city life through data-driven decision-making, starting with collection and moving through the management, sharing, and analysis of data. You will learn how to parse the unique privacy implications of persistent monitoring of activities in putatively public space, the introduction of sensors and other forms of instrumented measurement into the built environment, the repurposing of government data for uses not anticipated at the time of collection, and the kind of analytic techniques that turn these data into actionable insights. The class will also teach you how to assess whether these result in fairly rendered decisions and how to evaluate the desirability of their consequences (from the perspective of various stakeholders). Finally, the class will force you to consider what ethical obligations you may have to those who figure in your research, as well as those to whom the lessons are later applied. You will learn to think critically about how to plan, execute, and evaluate a project with these concerns in mind, and how to cope with novel challenges for which there are often no easy answers or established solutions. To do so, you will develop fluency in the key technical, ethical, policy, and legal terms and concepts that are relevant to a normative assessment of these novel analytic techniques. You will learn about some of the common approaches and tools for mitigating or managing the ethical concerns that these tend to provoke. And by exposing you to a variety of policy documents, the class will help you understand the current regulatory environment and anticipate future developments.

This course introduces students to the theory, principles and applications of mathematical and computer modeling of data as applied to cities. It will be based on two unified themes: foundations for predictive analytics and decision-making followed by applications in data science. The 1st half of the course will cover predictive modeling using a wide array of examples, including predictive modeling, an advanced treatment of regression, visualization and graphics, and automated analysis for high dimensional data. The second half will introduce students to applications in data science such as analytics of images and video as well as subjective data processing and analysis.

Skills Learned: Python, basic regression analysis, clustering, classification, deep learning, and network science

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The Urban Science Intensive (USI I) is part of a two-semester capstone sequence that is the experiential learning focus of the program. USI I takes place over 14 weeks in the Spring semester and prepares students for delivering Capstone Projects in the summer. The core of the course is team-based work on a real-world urban problem, combining problem identification and evaluation, data collection and analysis, data visualization and communication, and finally, solution formulation and testing. This project-based course begins with the Social Impact Project, where students are introduced and immersed in problem definition and project delivery skills. The course also lays the foundation for the Capstone Projects, where students work on integrated teams with Agency and Industry Partners, immersed in the public aspects of the project. * *The Urban Science Intensive I course introduces students to their projects and the Agency and Industry mentors involved and develops team-building; students meet with various officials at the relevant agencies and industry partners, tour relevant projects and facilities, and begin to engage the community; student teams define the problem and craft a strategy to identify solutions, inventory available and needed datasets, and explore possibilities for new instrumentation and citizen engagement to support project objectives. This course involves a combination of lectures, student team project work, in-class group work, site visits, and guest speakers.

Student teams engage in projects through the integration and analysis of data, definition and testing of possible solutions, identification of implementation strategies and constraints, and recommendation of a preferred solution and implementation plan. Student teams are challenged to utilize urban informatics within the real-world constraints of city operations and development, while cognizant of political, policy, and financial considerations and issues of data privacy, validity, and transparency. In so doing, student teams will be tasked with creating innovative and replicable solutions to pressing urban problems. The end product of the Intensive sequence is intended to be the result of the integration of multiple skill sets from each student’s area of specialization in domain, discipline, and entrepreneurial/organizational leadership focus.

Electives (12 Credits)

As a student in CUSP’s graduate programs, you will further customize your education with specialized CUSP electives in data science, domain applications, and civic analytics. Students take 4 elective offerings (12 credits) throughout the program.

Students may take up to two (6 credits) of non-CUSP data science or domain application electives from other schools across NYU, including but not limited to the Courant Institute of Mathematical Sciences, Stern School of Business, Wagner School of Public Service, and Tisch School of the Arts.

NYU CUSP’s elective choices include:

This course describes how to unleash the power of people and technology to successfully innovate in urban environments. Learn how to overcome the difficulties faced by many organizations trying to do new things, or trying to do things in a new way. These difficulties include failed, late, and over-budget initiatives, difficult to use systems, private data used without permission, and cyber security breaches that undermine public confidence.

Government and industry leaders need to create and sustain high performance cultures of innovation to meet the needs of cities’ diverse populations. Success depends on their ability to use technology cost effectively to make the desired changes. These can be incremental improvements or expensive, high-risk megaprojects that aim to fundamentally change a method of service delivery.

Course discussions will describe the technologies, leadership, customer care, project management, and service management needed to create and support quality, innovative systems. We’ll discuss how to create safe systems to address new needs and new methods of achieving civic objectives.

The goal of the Big Data Analytics class is to develop the key data analytics skill sets necessary to harness the wealth of newly-available data. Its design offers hands-on training in the context of real microdata. The main learning objectives are to apply new techniques to analyze social problems using and combining large quantities of heterogeneous data from a variety of different sources. The course will explain through lectures and real-world examples the fundamental principles, uses, and appropriate technical details of machine learning, data mining and data science. It is designed for graduate students who are seeking a stronger foundation in data analytics and want to understand the fundamental concepts and applications of data science.

Skills Learned: Python, Jupyter Notebooks, Web Scraping, APIs, Big Data Analysis

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The past decade has seen the increasing availability of very large scale data sets, arising from the rapid growth of transformative technologies such as the Internet and cellular telephones, along with the development of new and powerful computational methods to analyze such datasets. Such methods, developed in the closely related fields of machine learning, data mining, and artificial intelligence, provide a powerful set of tools for intelligent problem-solving and data-driven policy analysis. These methods have the potential to dramatically improve the public welfare by guiding policy decisions and interventions, and their incorporation into intelligent information systems will improve public services in domains ranging from medicine and public health to law enforcement and security. The LSDA course series will provide a basic introduction to large scale data analysis methods, focusing on four main problem paradigms (prediction, clustering, modeling, and detection). The first course (LSDA I) will focus on prediction (both classification and regression) and clustering (identifying underlying group structure in data), while the second course (LSDA II) will focus on probabilistic modeling using Bayesian networks and on anomaly and pattern detection. LSDA I is a prerequisite for LSDA II, as a number of concepts from classification and clustering will be used in the Bayesian networks and anomaly detection modules, and students are expected to understand these without the need for extensive review.

Skills Learned: Large scale data analysis methods (prediction, clustering, modeling, and detection), Weka

View Syllabus for Large Scale Data Analysis I – CUSP-GX 4147

View Syllabus for Large Scale Data Analysis II – CUSP-GX 41478

The objective of this course is to familiarize students with modern machine learning techniques and demonstrate how they can be effectively applied to urban data. The course is practice-oriented: concepts and techniques are motivated and illustrated by applications to urban problems and datasets. For that reason, it involves a significant programming component, with Python as the primary programming language. Topics include a variety of supervised and unsupervised learning methods, such as support vector machines, clustering algorithms, ensemble learning, Bayesian networks, Gaussian processes, and anomaly detection. Strategies for effective machine learning and discussion of the limitations of ML as well as a variety of supplementary techniques are also considered.

Skills Learned: Machine learning, Python, support vector machines, clustering algorithms, ensemble learning, Bayesian networks, Gaussian processes, and anomaly detection.

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This course will introduce you to urban governance and its current innovation trends. Urban governance comprises of the various forces, institutions, and movements that guide economic, politic, social and physical development, the distribution of resources, social interactions, and other aspects of daily life in cities. Public-sector innovation is indispensable to solve the complex urban challenges we are facing and can bring significant improvements in the services that the government has a responsibility to provide, including those delivered by third parties. Following a Discovery-Design-Delivery approach, students will learn the complex nature of cities, different strategies to solve public problems, how urban administration works and how public policies are crafted, how we can promote urban governance innovation, why collaboration is a must and which are the best tactics to promote effective public-private partnerships and networks, how we can support public engagement at all stages of the policymaking cycle, how to promote effective communications using current technology available, ethical issues that may arise when applying analytics to policy problems, how we can connect artificial and collective intelligence, and different approaches to measuring organizational performance. This course will help students to become public entrepreneurs that know how to effectively deliver data and innovation projects into an urban environment.

The course aims to provide an understanding of big data and state-of-the-art technologies to manage and process them. General topics of this course include: big data ecosystems, parallel and streaming programming model, and spatial data processing. Hands-on labs and exercises in MapReduce, Hadoop, Spark, Hive, and Pig will be offered throughout the class to bolster the knowledge learned in each module.

Skills Learned: Big data ecosystems, parallel and streaming programming models, MapReduce, Hadoop, Spark, Pig, and NoSQL.

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The growing use of data-centric technologies is transforming many aspects of public policy in the United States. These technologies affect the scale and nature of data that can be collected, enabling new approaches for evaluating policies both retrospectively and prospectively; for detecting discriminatory practices; and for auditing and designing “fair” algorithmic systems, among other applications. While modern computational and statistical methods offer the promise of increased efficiency, equity, and transparency, their use also raises complex legal, social, and ethical questions. In this course, we will discuss the use of such methods in a variety of applications, focusing on examples from criminal justice, and will examine the relationships between law, public policy, and data. The course will involve lectures, readings and class discussion, paper presentations, and a data-intensive semester-long project.

Skills Learned: Machine learning, statistics

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The world’s urban population is growing by nearly 60 million per year; equivalent to four cities like New York annually. Monitoring the chronological growth of key attributes of cities, as well as quantifying their current conditions presents a great potential for positive change. Through the acquisition of new data, there are immediate opportunities to influence the sustainable growth of small and medium size cities. There is also the potential for alleviating the extremes in Megacities, where conditions have reached a critical and unmanageable state. Looking at cities as interdependent networks of physical, natural and human systems, this course provides a perspective on how to monitor the function and wellness of these systems. Students obtain an understanding of needs assessment, planning, and technical approaches for instrumenting a city. This includes monitoring patterns of activity, mobility, energy, land use, physical and lifeline infrastructure, urban ecology, vegetation, atmosphere and air quality. The expected outcomes of this course is a comprehensive understanding of what can be instrumented and the monitoring architecture for acquiring and generating new data about cities.

Visualization and visual analytics systems help people explore and explain data by allowing the creation of both static and interactive visual representations. A basic premise of visualization is that visual information can be processed at a much higher rate than raw numbers and text. Well-designed visualizations substitute perception for cognition, freeing up limited cognitive/memory resources for higher-level problems. This course aims to provide a broad understanding of the principals and designs behind data visualization. General topics include state-of-the-art techniques in both information visualization and scientific visualization, and the design of interactive/web-based visualization systems. Hands on experience will be provided through popular frameworks such as matplotlib, VTK and D3.js.

Skills Learned: Interactive/web-based visualization systems, matplotlib, VTK and D3.js.

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In this course, students will learn about ongoing advances in the field of spatial analysis, particularly in current research and development contexts, including (1) process-based spatial modeling, (2) time-enabled spatial analysis, and (3) spatial analysis on new forms of spatial data. Although the class will explore several of these topics using software-based labs, the course is rooted in an exploration of the methodology underpinning spatial analysis and the derivation of analysis schemes. Students are expected to have prior coursework experience in Geographic Information Systems and spatial analysis. Example sets in the course will focus on urban analysis.

Syllabus available upon request; please email

Remote sensing technologies are becoming increasingly available at better resolution levels and lower costs. This course will provide an overview of some of the most common technologies in the areas of imagery, video, sound, and hyperspectral data that can be facilitated through smart phones or other readily accessible means. Students will be given a formal introduction to the aforementioned four areas and then be afforded an opportunity for hands on training in data collection and data analysis. In the course will have the opportunity to work in small groups to investigate an urban problem of interest to them at a site of their choosing. The teams will use these new learned technologies in tandem with other publicly accessible data (either formally available or also collected by the researchers) to investigate a working hypothesis about their chosen urban problem for their site.

In this course, we will study the political dimensions of technology in urban environments. You will learn “to see” the politics of technological artifacts by learning several analytical perspectives from Science and Technology Studies, Human Computer-Interaction, and Design Theory. We will use these perspectives to engage the politics within the design and use of a range of digital technologies: smart city infrastructures, automated decision-making systems, social media platforms, etc. By learning to see the political dimensions of these technologies, students will be better able to create positive social impact in urban environments.

Syllabus available upon request; please email

Efforts to improve life in urban spaces require making decisions regarding use of resources, targets for development, and priorities for financial and political investment. Each of these decisions is dynamic and builds off one another, but regardless of their intended target, these decisions all have consequences for health, a perspective embraced by the “Health in All Policies” framework. More specifically, each of these decisions (and their resulting policies) become structures that ultimately either serve to promote or prevent health equity in urban populations. The impacts of these structures extend beyond consequences for chronic disease (i.e., asthma, diabetes, cancer), as they lead to inequitable patterning of infectious disease risk as well, a phenomenon illustrated by the severe racial/ethnic disparities in disease and mortality risk that have been observed in New York City, throughout the COVID-19 Pandemic. This course is an introduction to structural thinking as it pertains to health in urban spaces. Throughout the course we will discuss items such as zoning, public transit, education, and policing, and think through their implications for health. Furthermore, we will discuss common practices regarding each of these domains and illustrate how these decisions have contributed towards health inequities across multiple axes, including race, ethnicity, gender, and social class. Lastly, we will walk through illustrative examples of policies that have positively influenced health outcomes for the overall population, but exacerbated inequity in doing so, and will discuss the ethics and utility of such trade-offs.

Syllabus available upon request; please email

We live in a world beset with ever more complex urban and global challenges where learning how to combine big data and collective intelligence is a must to create public value. Only learning how we can properly mix data analytics and the use of collaborative and participatory strategies we will be able to secure citizens’ rights, expand the provision of public services and improve their quality. This course reviews big ideas, key debates, policies and innovative/disruptive tools around the combination of these two sources of knowledge that, properly blended, have the capacity to transform how we govern the city and the world. Following a Discovery-Design-Delivery approach, students will also learn how we can promote big data democratization, adding a bottom-up approach in the creation, capture, curation, analysis, visualization and data ethics, and will review how we can connect big data with justice, sustainability, livability, and resilience to secure citizens’ socio-economic human rights and anticipate social problems like the ones derived to the aging population. Other questions that we will tackle will be how we can combine artificial and collective intelligence, the role than “global cities” can play in the global economic system and how we can help them to address the opportunities and challenges of the sharing and platform economy. In conclusion, reviewing good practices from around the world, the course purpose is twofold: (1) to enhance your sophistication in thinking about and analyzing the big data and collective intelligence blended practices, and (2) to hone your skills about how to work with public institutions, especially cities but also regional, state and federal governments, and putting them into practice developing practical collaborative and participatory management skills.

Smart home IoT (Internet-of-Things) devices are gaining popularity in average consumer homes. These “smart” devices, such as cameras, plugs, TVs, dishwashers, etc, are also known to pose various security and privacy threats (e.g., your Alexa listening to you), but the opaque nature of these devices makes it difficult to discover security and privacy vul-nerabilities.

This course introduces basic and advanced topics on Internet-of-Things (IoT) security and privacy from a data-driven perspective. It starts with preliminaries on networking and In-ternet security, followed by security research based on a data-driven approach. Students will review and present peer-reviewed academic papers from multiple disciplines, ranging from computer science, psychology, and policy/law. Furthermore, students will engage in hands-on projects to independently investigate real-world security and privacy issues of IoT devic-es and/or propose solutions to fix these issues.

Skills Learned: IoT security and privacy

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CUSP-GX 5005/5006 - Urban Science Intensive I/II

During the 6 credit, two course Capstone Program, you will work in a multidisciplinary environment with a city agency or industry partner to address a current urban challenge in a particular domain, such as transit, public health, or environmental sustainability. You will play an important role in the project, working with other researchers — and even entrepreneurs — to unlock the potential in big data to make your city better.

Capstone projects may be part of larger, ongoing NYU-CUSP research efforts involving city agencies and NYU‑CUSP industry partners, self-contained projects involving agencies and industry partners, or more entrepreneurial in focus and content, where you and a team of students will work on developing a new solution derived from your analysis.

You can view previous capstone projects here.


As part of the co-curricular education at CUSP, students have the opportunity to participate in an immersion program in one of the leading smart cities around the world. This mini-course targets the global perspective on “urban” skills needed to link data science with the public good. Students travel to different cities around world to work on actual urban challenges using the analytics skills developed during the program. In the Data Dive, the host city provides their data sets and a specific urban problem; students bring their expertise to answer the questions and solve the problems using informatics techniques.