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.

Over the course of 1 year (3 semesters), Advanced Certificate students take courses on a concurrent schedule with the MS program. These include non-credit courses in the summer, core classes in the fall and spring, and electives in the spring.


To introduce students to the field of Urban Informatics and help prepare students for their graduate students, 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:

City Challenge Week (Pre-Fall)

City Challenge Week is the start of the MS in Applied Urban Science and Informatics program and CUSP’s new student orientation. The intensive 4-day program 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

Advanced Certificate students may choose from the following CUSP courses:

Fall Courses (6 Credits or 2 Classes)


This course is the introduction to the core disciplines of data acquisition and management, integration, and analytics.  Students will learn the major concepts, tools, and techniques for what informatics can do for cities. It includes background in computation, statistics, error analysis, data acquisition, management, integration, working with large datasets and understanding data sources.  The course covers topics in visualization, data analysis and modeling, and machine learning, focusing on their application on urban problems, and including material not usually covered in computer science courses: how to handle spatial-temporal data, GIS, issues related to citizen science and participatory sensing, instrumentation, physical sensors, imagery, and issues of data ethics, privacy. This class is Python based.


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.


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.


This supplementary lab teaches students to recognize where and understand why ethical issues can arise when applying analytics to urban problems. One of the learning objectives is to consider what ethical obligations scientists may have to those who figure in their research, as well as those to whom the lessons are later applied. The lab considers issues across the lifecycle of projects starting with collection and moving through management, sharing, and analysis of data. The goal is to learn about privacy implications, the repurposing of government data for uses not anticipated at the time of collection, and the legal framework covering these. 

Spring & Summer Courses (6 Credits or 2 Classes)


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.


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.

CUSP-GX-6004: Justice Systems Algorithms

The growing use of data-centric technologies is transforming criminal justice in the United States. These technologies affect the scale and nature of collected data, enable the detection of discriminatory patterns of policing, and influence bail recommendations for pretrial detainees, among other things. Modern computational and statistical methods offer the promise of increased efficiency, equity, and transparency, but their use raises complex legal, social, and ethical questions. In this course, we will discuss the application of techniques from machine learning and statistics to a variety of criminal justice issues, analyze recent court decisions, and examine the relationships between law, public policy, and data. The course will involve readings and class discussion, short assignments, and a data-intensive semester-long project. The course will also feature guest speakers who work in the criminal justice domain. Students should have basic knowledge of statistics, programming, and supervised machine learning, but no prior knowledge of the criminal justice system will be assumed.

CUSP-GX-6004: Managing IT

All new public sector and private systems are information technology (IT) systems. This course will improve your IT IQ. It will explain how to create and operate IT systems and task IT staff so that they meet stakeholder expectations for improving productivity, cost effectiveness, availability and address other government and business challenges. The information in the course will also help students more productively interact with IT professionals to be more satisfied IT customers. IT system development and use have gotten a reputation for problems and high costs. We’ve read about data breaches at popular companies such as Yahoo, Target, Apple, and IBM. We’ve also read about or maybe even experienced well-known failures like the launch of the US’ and NYC’s CityTime payroll management system. Well known fiascoes like these cause people to think that it’s impossible to deliver and operate quality IT systems. How then can government and business improve their operations? Organizations can achieve high performance creating and running IT systems by using well researched and documented practices to deliver IT projects and conduct IT operations with high levels of quality. Many best practices can be found in both the private and public sectors. This course will emphasize best practices for public sector organizations.


The goal of this course is to provide the students with the tools and methods to understand basics of traffic flow theory, modeling and simulation. The emphasis will be on the use of real-world data to supplement the understanding of the theory behind the models. Small scale simulation models will be developed, tested and validated against real-world data.


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.


The goal of the Applied Data Analytics class is to develop the key computer science and data science 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. It is designed for students who are seeking a stronger foundation in data analytics.


Course description coming soon.


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.


This course will provide an understanding of Big Data and current technologies for managing and processing Big Data. At the course end, students will have the ability to decipher when and which software tools to utilize for a particular project/urban challenge: e.g. NoSQL for web UI, Spark for back-end, SQL for transaction controls, etc. The course is designed to provide a high-level understanding of big data platforms.