Urban Informatics Minor
About The Minor
The minor is open to students across the University, including those in STEM fields, the humanities, the social sciences, and the arts. Given the cross-disciplinary nature of studies of cities, this minor will be of particular interest to students in disciplines such as sociology, public policy, public health, architecture/urban design, business, economics, and politics.
Non-engineering students will gain quantitative skills for requisite understanding of cities in their disciplinary areas of interest, while engineering students will benefit from specialized technical electives in data analytics. For both groups, the minor provides skills that are needed for data-driven analysis and critical thinking. This course of study will help prepare students for graduate study in a range of fields, as well as for work in government, the private sector, and non-governmental organizations that focus on cities and urban living.
The academic mission of the minor in Urban Informatics is to expose undergraduate students from across the University to the emergent field of data-driven urban studies. This includes the application of data acquisition and analytics to understanding the urban environment, infrastructure, operations, management, policy, design, planning, and population health.
The goal is to offer students the necessary foundation to acquire and analyze information to solve critical challenges facing cities in the 21st century.
Students will develop skills in:
- Data acquisition
- Data management
- Data structure and privacy standards
In consultation with the designated minor adviser, students will take one of the following courses as a “prerequisite” to the minor:
- 4 Credits Data Analysis MA-UY2224
- An introductory course to probability and statistics. It affords the student some acquaintance with both probability and statistics in a single term. Topics in Probability include mathematical treatment of chance; combinatorics; binomial, Poisson, and Gaussian distributions; the Central Limit Theorem and the normal approximation. Topics in Statistics include sampling distributions of sample mean and sample variance; normal, t-, and Chi-square distributions; confidence intervals; testing of hypotheses; least squares regression model. Applications to scientific, industrial, and financial data are integrated into the course.NOTE: Not open to students who have taken MA-UY 2233 or MA-UY 3012 or MA-UY 3022.
Prerequisite: MA-UY 1124, MA-UY1424, or MA-UY 1132
- CORE-UY0109 Please refer to the bulletin for more information
- 3 Credits Sensing the City: Methods for Urban Health Monitoring CE-UY4443
- Considering cities as networks of people, infrastructure and the natural environment, this course introduces approaches for monitoring the function and state of wellness of the urban environment including energy, waste, air quality, land use, patterns of activity and mobility. As the world’s urban population grows equivalent to four time the population of New York City every year, the quantitative analysis of key attributes of cities and characterization of the chronological changes has become the engine for advancing urban operations and policies. We will examine methods for tracking the state of health of a city's infrastructure, environment, the ecosystem, and its inhabitants. This is achieved by introducing the students to fundamental of sensing and data acquisition, followed by exercises and case studies with applications.
Prerequisites: (PH-UY 1013 or equivalent) and (CM-UY 1004 or equivalent) or adviser's approval
Electives (2 courses needed)
- 3 Credits Transportation Systems Analytics CE-UY3373
- This course teaches students introductory methods to design transportation systems and informatics to evaluate the behavioral response of travelers. It trains students in fundamental problem solving skills needed to manage cyber-physical transportation networks in a smart cities era. The course is divided into three parts: (1) framework for analyzing urban systems under congestion and queueing, (2) intelligent transportation systems (ITS) to connect traveler decisions to system operations, and (3) constrained optimization methods to design and manage complex urban systems.
Prerequisite: (MA-UY 2224 or an approved equivalent) or Adviser's approval
- 4 Credits Data Structures and Algorithms CS-UY1134
- This course covers abstract data types and the implementation and use of standard data structures along with fundamental algorithms and the basics of algorithm analysis. Not open to students who have taken CS-UY 2134.
Prerequisite for Brooklyn Students: CS-UY 1114 or CS-UY 1123 (C- or better)
Prerequisite for Abu Dhabi Students: CS-UH 1001 or ENGR-UH 1000
Prerequisite for Shanghai Students: CSCI-SHU 101
Corequisite for all Students: EX-UY 1
- EE-UY4144 Please refer to the bulletin for more information
- 3 Credits Computer Programming I CS-UY111
- 3 Credits Information Visualization CS-GY6313
- An introductory course on Information Visualization based on a modern and cohesive view of the area. Topics include visualization design, data principles, visual encoding principles, interaction principles, single/multiple view methods, item/attribute, attribute reduction methods, toolkits, and evaluation. Overviews and examples from state-of-the-art research will be provided. The course is designed as a first course in information visualization for students both intending to specialize in visualization as well as students who are interested in understanding and applying visualization principles and existing techniques.