Home
Contact UsCURRICULUM
The curriculum of 120 credits (ECTS) consists of:
- Core Elective Courses,
- Must Course - Applied Urban Science Laboratory,
- Electives,
- Research Methods and Scientific Ethics Course,
- Seminar, Specialization Field Course and Master’s Thesis,
Principles of Urban Informatics (Core Course)
This course is the introduction to data and analytics strategies, tactics, tools that cities deploy in order to bring resolution to a wide variety of complex and important challenges and concerns. This will include detailed accounting of the use of data acquisition and management, integration, and analytics (both quantitative and qualitative) through the thorough investigation of case studies.
Urban Science Lab (Must Course)
The Urban Science Lab (USL) is the experiential learning focus of the program. USL takes place over 14 weeks in the Spring semester and prepares students for delivering Urban Innovation Projects. The core of the course is team-based work on a real-world urban problem, combining problem identification and evaluation, data collection and analysis, data visualization and communication, and finally, solution formulation and testing. This project-based course begins with the Social Impact Project, where students are introduced and immersed in problem definition and project delivery skills. The course also lays the foundation for the Urban Innovation Projects, where students work on integrated teams with Agency and Industry Partners, immersed in the public aspects of the project. The Urban Science Lab courses introduce students to their projects and the Agency and Industry mentors involved and develops team-building; students meet with various officials at the relevant agencies and industry partners, tour relevant projects and facilities, and begin to engage the community; student teams define the problem and craft a strategy to identify solutions, inventory available and needed datasets, and explore possibilities for new instrumentation and citizen engagement to support project objectives. This course involves a combination of lectures, student team project work, in-class group work, site visits, and guest speakers.
Civic Analytics and Urban Intelligence (Elective Course)
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.
Applied Urban Data Science (Core Course)
This course introduces students to the theory, principles and applications of mathematical and computer modeling of data as applied to cities.
Data Governance: Ethics, Law and Politics (Core Course)
The age of social big data brings with it a range of ethical, legal and political issues. From the ethics of protecting individual online privacy, to the legal frameworks regulating internet giants such as Facebook and Google, new data governance issues surface at a rapid pace. This course provides students with an introduction to key legislative, political and ethical principles and debates from the perspectives of anthropology, law, sociology, political science, and related disciplines, concerning the governance of data, needed for a range of analysis and management positions across private, public and non-profit organizations.
Urban Spatial Analytics (Elective Course)
Urban Spatial Analytics focuses on developing spatial analysis skills specifically in urban context, which cuts across various interdisciplinary fields like urban land-use planning, socio-economic development, education, public health, real estate, migration, environmental studies, transportation, and urban demography. This course will equip students with Geographic Information System (GIS) concepts to collect, understand, organize, store, analyze and visualize complex urban geospatial data.
Democracy and the City (Elective Course)
This course will focus on mixing data analytics and the use of collaborative and participatory strategies to secure citizens’ rights, expand the provision of public services and improve their quality. The students will develop 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
Big Data Analytics for Public Policy (Elective Course)
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.
Relational Economy (Elective Course)
This course will focus on the interdisciplinary views of understanding and conceptualizing the changing global economy, by emphasizing a specific spatial perspective that mirrors unequal economic development, and selective specialization and growth processes. It will explain the changing relationship between territory and the organization and evolution of economic action. Students will learn about the relational understanding of human interaction, which recognizes values, interpretative frameworks, and decision making practices embedded in city-regions.
Blockchain and Distributed Systems (Elective Course)
The main objective of this course is to provide the basis for emerging technologies such as decentralized systems (including blockchain) and their use cases in various fields.. In this lecture, history of decentralized technologies and various case studies particularly in urban areas, main attributes of these technologies such as digital identity, encryption, and decentralized (self) governance and most advanced use cases of such technologies, namely cryptocurrencies will be discussed.
Urban and Regional Economic Development Theory (Elective Course)
This course aims to constitute the basic theoretical framework in urban and regional economic development theory and models. Within the context of the course, regional economic development and theoretic approach that occurred during the evolution process of regional planning is planned to discuss with the view of alternative theories and approaches towards regional development.