Courses in the Graduate Certificate in Data Analytics are transferrable into the MS in Urban Informatics degree. By completing the Certificate, students in good standing may enroll in the full master’s degree.

The interdisciplinary Master of Science in Urban Informatics is offered through a collaboration between the the College of Social Sciences and Humanities’ School of Public Policy and Urban Affairs and the College of Computer and Information Sciences. The curriculum is comprised of four foundational data analytics courses and six degree-specific courses: three core courses that provide an overview of the field, a theoretical framework, and introductory geographic information systems skills; two electives—one urban data skills course and one applied urban analytics course; and a required urban informatics portfolio course where students assemble a portfolio of three projects completed during their coursework. Apply now or request information using the form below.

 



 

Format:

The Master of Science in Urban Informatics is offered 100 percent online or hybrid in Boston with a combination of online and on-campus instruction. This flexible format is designed to accommodate students’ busy schedules while providing opportunities to interact with faculty and classmates on campus and through discussion boards and other technology. Classes benefit from Northeastern’s signature experiential learning program, drawing on each student’s professional experiences to make real-world connections.

 

Courses

The Master of Science in Urban Informatics is a 32-credit hour program that takes between 18 months and two years to complete depending upon course load. Upon completion of the four interdisciplinary foundation courses, students complete four required and two elective urban informatics courses.

 

Foundation Courses

Introduction to Computational Statistics

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 courses in the Data Science Programs, 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. The course begins with a review of probability and statistics then progresses to data manipulation, sampling, and scripting; statistical tests; OLS regression; categorical dependent variables; maximum likelihood methods; time series; and hierarchical models. The course finishes with a brief introduction to machine learning methods and visualization using R. Throughout, there will be an emphasis on the challenges and limitations of modeling big data, and students will finish with the basic skills needed to manipulate and model complex data and present their insights to non-experts.

 

Collecting, Storing, and Retrieving Data

Students learn how to build large-scale information repositories for different types of information objects so that later these data can be selected, retrieved, and transformed for analytics and discovery. 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. In particular, students will be able to assess and recommend efficient and effective large-scale information storage and retrieval components that provide data scientists with properly structured, accurate, and reliable access to information needed for investigation.

 

Introduction to Data Mining/Machine Learning

This course provides an introduction to the fundamental techniques for data mining and covers several basic learning algorithms, along with popular data kinds, implementation and execution, and analysis of results. It teaches students how learning models from data work, both algorithmically and practically. Coding can be done in R, Matlab, or Python, but a demonstrated ability to use R is part of a student’s grade. At the end of the course, students will be able to demonstrate the ability to set up and run learning algorithms on various data sets, test models on new data, choose appropriate techniques for particular datasets or tasks, and evaluate and present results to non-experts.

 

Information Design and Visual Analytics

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, students 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.

Topics covered in this course include: visual data mining techniques and algorithms for supporting the knowledge discovery process, principles of visual perception and color theory for revealing patterns in data, semiotics and the epistemology of visual representation, narrative strategies for communicating and presenting information and evidence, and the critical evaluation and critique of data visualizations.

Note: A 12-credit hour, 4-course Graduate Certificate in Urban Informatics comprised of core and elective courses within the MS in Urban Informatics curriculum is also offered.

Core Courses

Big Data for Cities

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 work with large public datasets such as citizen call data, U.S. census data,, and other sources including social media to learn about visual and statistical methods for analyzing and applying the lessons from large urban data sets. Students should expect to gain a critical understanding of data-structures, collection methodologies, and their inherent biases; to acquire a methodical approach for rigorous analysis and inference; and to develop strategies for communicating the results in an urban policy setting.

 

Urban Theory and Science

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.

 

GIS for Urban Policy

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 through the use of proprietary and open source software packages. The course also introduces students to the process of developing and writing an original policy-oriented spatial research project with an urban social science focus. Through this project, students have the opportunity to combine the data science skills gained earlier in the program with spatial analytic techniques that are especially useful in urban-scale analyses.

 

Urban Informatics Portfolio

All students are required to submit a three-project portfolio developed from projects completed within their courses. The projects must be presented to a faculty and expert committee in high quality  and concise visualizations and text.

Specialized Skills Courses and Client-Based Projects

Specialized Skills Course (Choose 1): Advanced Spatial Analysis of Urban Systems

This course builds on the skills developed in GIS for Urban Policy in order to give students advanced skills for analyzing urban systems (including social, built, and natural systems). Students complete a series of small-scale projects based upon current data and policy challenges within cities and focused on spatial statistical analysis, cartography, advanced modeling, spatial network analysis, and 3D visualization skills. 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. Students choose between this course and Dynamic Modeling for Environmental Investment and Policy Making.

 

Specialized Skills Course (Choose 1): Dynamic Modeling for Environmental Investment and Policy Making

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. Throughout this course, students 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 their 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; and learn how to transfer insights and skills from one discipline to solve problems of another discipline. Students choose between this course and Advanced Spatial Analysis Urban Systems. 

 

Client-Based Project (Choose 1): Capstone

This course links a small group of four to six graduate students with a professional client that has a “real world” issue for the students to address. Some examples of past and proposed capstones in urban informatics have included the development and analysis of data from technologies designed for citizen engagement, and the development and application of open data APIs for enhancing urban management capacity. Students choose between this and a Research Practicum.

 

Client-Based Project (Choose 1): Research Practicum

The research practicum offers students the opportunity to structure professional internships into their academic training. With assistance from the School of Public Policy and Urban Affairs, students are placed within 1-semester professional internships and produce an academic product at the end. Students choose between this and a Capstone.