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 12-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.

PREPARING FOR THE CUSP ADVANCED CERTIFICATE

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:

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.

CURRICULUM (12 CREDITS)

Core Classes (6 Credits)

CUSP Advanced Certificate students take two core classes (6 credits) during the fall semester. These include Principles of Urban Informatics and a choice between Civic Analytics & Urban Intelligence OR Applied Data Science.

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 View Syllabus

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.

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.

Electives (6 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. Advanced Certificate students take 2 CUSP elective offerings (6 credits) in spring or summer.

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

View Syllabus

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.

View Syllabus

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.

View Syllabus

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.

Skills Learned: Machine learning, statistics

View Syllabus

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.

View Syllabus

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.

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.

OPTIONAL STUDENT IMMERSIONS: GLOBAL DATA DIVES

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.