Urban Informatics

Program Description

The Master of Science in Urban Informatics (MSUI) program couples comprehensive data analytics skills with an understanding of the big questions faced by cities. Students that graduate from the program will be provided with a framework that outlines the major social, political and environmental challenges in contemporary urbanization along with a focus on the data analytics skills that can help to address these challenges.

The MSUI program is built upon a unique cross-college initiative at Northeastern University, which offers comprehensive state-of-the-art training in the core skills of data analytics including quantitative analysis, data mining, machine learning, and data visualization. Students in the MSUI program supplement training in these core skills with a sequence of courses that address how data and technology are being used to address the key social and environmental challenges faced by contemporary cities. This theoretically informed perspective on urban data and technology is applied through professional research practicums and client-based projects.

Graduates from the Urban Informatics program will be a part of the next wave of urban professionals ready to integrate data analytic skills with knowledge of the socio-political dimension of cities. Given the continuous growth in urban data and technology, these professionals will be essential to shaping the future of cities throughout the globe.

This program provides a uniquely integrated urban and informatics degree with a substantial experiential education component. Embedded within the School of Public Policy and Urban Affairs, the Northeastern program in Urban Informatics connects substantial education in data analytics with applied policy and urban theory. As cities increasingly define the frontier of professional applications in data analytics, such training is essential for our urban future. 

Degree Requirements

The Master of Science in Urban Informatics degree requires students to complete 32 credit hours. The coursework is structured around four interdisciplinary core courses and an additional 16 credits focused on urban-specific applications of core skills. The curriculum is designed to offer methodological rigor, a theoretical framework, and opportunities for applied experiences. The table below lists the courses required for the Master of Science in Urban Informatics including core and degree-specific courses.

Urban Informatics Master Degree Requirements

Course

Credits

Summary

Core Data Analytic Courses (Required, 16 Credits)

Introduction to Computational Statistics

4 Semester Credits

Introduction to the fundamental techniques for quantitative data analysis, with an emphasis on large or complex data sets

Collecting, Storing, and Retrieving Data

4 Semester Credits

Trains students to build large-scale information repositories of different types of info. objects

Introduction to Data Mining/ Machine Learning

4 Semester Credits

Provides an introduction to the fundamental techniques for Data Mining

Information Design and Visual Analytics

4 Semester Credits

Introduces the systematic use of visualization techniques for supporting the discovery of new information as well as effective presentation of known facts

Introductory Urban Informatics Course (Required, 3 Credits)

 PPUA 5262 Big Data for Cities

3 Semester Credits

Investigates the city and its spatial, social and economic dynamics through the lens of data and visual analytics

Urban Informatics Theory Course (Required, 3 Credits)

PPUA 5266 Designing Participatory Urban Infrastructures

3 Semester Credits

Focuses on urban theory through the lens of infrastructural networks in the context of urban sensing and participatory technologies

Geographic Information Systems Course (Required, 3 Credits)

PPUA 5263 GIS for Urban Policy

3 semester credits

Provides basic spatial analytic skills and introduces students to some of the urban social scientific and policy questions that have been answered with these methods

Elective Urban Data Skills (choose 1, 3 credits)

PPUA 7237 Advanced Spatial Analysis of Urban Systems

3 Semester Credits

Provides advanced spatial analytic skills focused on spatial statistics and building spatial analytic models

PPUA 5261 Dynamic Modeling for Environmental Investment and Policy Making

3 Semester Credits

Introduces students to the theory, methods and tools of dynamic modeling for policy and investment decision making, with special focus on environmental issues

Applied Urban Analytics (choose 1, 3 credits)

PPUA 7673 Capstone

3 Semester Credits

Applied, client-based course—must be approved with an urban informatics focus

PPUA 6266 Research Practicum

3 Semester Credits

Applied research project proposed by student. May be affiliated with a professional experience such as an internship or job placement

Urban Informatics Portfolio (Required, 1 credit)

PPUA XXXX Urban Informatics Portfolio

1 Semester Credit

Students assemble and submit a professional portfolio of at least three projects completed within courses. Approved by affiliated faculty

The Required Interdisciplinary Core

The four interdisciplinary core courses in Data Science/Analytics serve as a foundation for the proposed professional master degree in Urban Informatics. The goal of the core is to provide foundational knowledge in a data science/analytics. These courses examine how data is collected, stored and retrieved, how data can be extracted from large datasets, whether they are structured or unstructured; how to analyze information using data mining and machine learning, and finally how to use information design and visual analytics to analyze and present the results.

Students will leave the core curriculum with a working knowledge of “R”, an open source software for computational statistics, visualization and graphic presentation. Students will be able to execute basic analytic methods in R.

The courses are tailored to a professional masters audience, and require undergraduate statistics as a pre requisite for entry into the core.

The Urban Informatics Curriculum

The goal of the non-data analytics courses is to provide foundational knowledge in urban informatics. Students will be provided with an overview of the field, introduced to the major categories of urban data, provided with specific spatial analytic and mapping skillsets, and will apply these skills to real-world urban policy and research questions. Throughout these courses, assignments will ask students to integrate the core course skills with urban data and policy questions. As well, students will develop a high quality professional portfolio of work that can be presented to future employers.

The six courses beyond the data analytics core necessary to complete the Master of Science Urban Informatics requirements include three foundational courses that provide an overview of the field, a theoretical frame, and introductory geographic information systems skills (a necessary data analytic skill not provided in the core). In addition, students may select any two of the elective courses. One elective course is chosen from a list of urban data skills courses. A second elective course is chosen from a list of applied urban analytics courses. Finally, all students must take the 1-credit urban informatics portfolio course which requires students to assemble and submit a portfolio of at least 3 projects that were completed during their coursework.

Description of Courses:

Big Data for Cities (Required)
The course focuses on investigating the city and its spatial, social and economic dynamics through the lens of data and visual analytics. In this workshop class, the students will work with large public datasets such as the US census and other sources including Social Media, and learn about visual methods for analyzing them. Goals of the course are (1) to gain a critical understanding of data-structures, collection methodologies and their inherent biases; (2) to acquire a methodical approach for rigorous visual analysis and inference; (3) to develop visual strategies for communicating the results.

Designing Participatory Urban Infrastructures (Required)
This design / theory class focuses on urban infrastructural networks in the context of urban sensing and participatory technologies. The goal is to understand urban infrastructure as a complex socio-technical system that has to be approached from multiple perspectives – including social, political, ecological and cultural. Students will analyze a particular system (including communication, waste and sanitation, transportation), and develop concepts for its improvement. To inform the design process, the course offers a theoretical perspective on infrastructural studies with a focus on urban technologies, using key texts from the domain of Science and Technology Studies (STS), Ubiquitous Computing, Urban Interaction Design, and Urban Informatics. In all, students will gain insight into how scientific and technological ‘fixes’ for urban policy problems interact with social and political systems. As well, students will develop theoretically informed research projects based upon established research design strategies that lead them to the proposals for urban infrastructural improvements.

Introduction to Computational Statistics (Required)
This course provides an introduction to the fundamental techniques for quantitative data analysis, with an emphasis on large or complex data sets. It lays the foundation for many of the other core and non-core courses in the Data Analytics program, including topics such as data acquisition and management, scripting and sampling, probability and statistical tests, econometric models, and data visualization. These diverse skills are developed using the R statistics language and data sets that emphasize real-world data problems.

Collecting, Storing and Retrieving Data (Required)
Students will learn how to build large-scale information repositories of different types of information objects so that they can be selected, retrieved, and transformed for analytics and discovery, including statistical analysis. Students will learn how traditional approaches to data storage can be applied alongside modern approaches that use massively parallel computation and non-relational data structures. Through case studies, readings on background theory, and hands-on experimentation, students will learn how to select, plan, and implement storage, search and retrieval components of large-scale structured and unstructured information repositories. 

Introduction to Data Mining / Machine Learning (Required)
This course provides an introduction to the fundamental techniques for Data Mining. Several basic learning algorithms are discussed. It lays the foundation for the Data Analytics core of how learning models from data works, both algorithmically and practically, providing support for other specialized non-core courses in the program. Coding can be done in R, Matlab or Python; ability to use R is part of the grade. Students must demonstrate ability to setup data for learning, training, testing, and evaluating.

Information Design and Visual Analytics (Required)
This course introduces the systematic use of visualization techniques for supporting the discovery of new information as well as the effective presentation of known facts. Based on principles from art, graphic design, perceptual psychology and rhetoric, we will learn how to successfully choose appropriate visual languages for representing various kinds of data in order to support insights relevant to the user’s goals.

GIS for Urban Policy (Required)
This course provides basic spatial analytic skills and introduces students to some of the urban social scientific and policy questions that have been answered with these methods. It covers introductory concepts and tools in Geographic Information Systems (GIS), and database management. As well, the course introduces students to the process of developing and writing an original policy-oriented spatial research project with an urban social science focus.

Urban Informatics Portfolio (Required)
All students are required to submit a three-project portfolio developed from projects completed within courses. The projects must be presented in high quality and concise visualizations and text.

Advanced Spatial Analysis of Urban Systems (Elective)
This course builds on the skills provided in GIS for Urban Policy (PPUA 6215) in order to give students advanced skills for analyzing urban systems (including social, built, and natural systems). Students will complete a series of small-scale projects focused on spatial statistical analysis, cartography, advanced modeling, spatial network analysis, and 3D visualization skills. The small scale projects will be based upon current data and policy challenges within cities. Students will employ these skills to develop an original research project that analyzes how at least two systems (social, built, natural) interact within urban space.
 
Dynamic Modeling for Environmental Investment and Policy Making (Elective)
This course introduces students to the theory, methods and tools of dynamic modeling for policy and investment decision making, with special focus on environmental issues. The course makes use of state-of-the-art computing methods to translate theory and concepts into executable models, and provides extensive hands-on modeling experience. Topics include discounting, intertemporal optimization, dynamic games, and treatment of uncertainty. Students will learn to Throughout this course you will learn to set up their own models of urban, economic and environmental systems; develop computer models that solve systems of simultaneous equations which may contain nonlinearities, time lags, and random variables; conduct sensitivity analyses of computer models; explore a system’s dynamics under a wide range of what-if scenarios; use dynamic modeling to guide your own research, organize available data, and streamline the collection of new data; use dynamic modeling to generate group consensus about the structure and behavior of nonlinear dynamic systems; learn how to transfer insights and skills from one discipline to solve problems of another discipline.

Capstone (Elective)
This course links a small group (4-6) of graduate students with a professional client that has a “real world” issue for the students to engage. In the context of Urban Informatics, some examples of past and proposed capstones include (1) the development and analysis of data from technologies designed for citizen engagement (2) the development and application of open data APIs for enhancing urban management capacity.

Research Practicum (Elective)
The research practicum offers students the opportunity to structure professional internships into their academic training. With assistance from PPUA, students are placed within 1-semester professional internships and produce an academic product at the end.

 Admission Criteria

Every applicant to the Master in Urban Informatics degree program must hold at least a bachelor’s degree. Applicants should complete the following materials in order to be considered for admission:

  • Online application completed through the Apply Yourself portal
  • Application fee – US $75.00
  • Transcript of undergraduate degree
  • Evidence of completion of undergraduate course in statistics
  • Statement of Purpose including description of relevant work experience
  • TOEFL score of at least 100 for international students who have a Bachelors degree from a non-English speaking country
  • Online interview for international students who have a Bachelors degree from a non-English speaking country
  • 3 letters of reference from individuals that know your academic record and/or potential for graduate study
  • Complete and up-to-date résumé

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