Education
CIESIN regularly offers undergraduate and graduate courses through several different schools and departments at Columbia University, held at either the Morningside campus or the Lamont campus. CIESIN also participates in a variety of educational initiatives and has mapping tools and resources of interest to K–12 teachers and students, college classrooms, and graduate-level education and research.
Academic Courses and Initiatives
The Climate Mobility Network
One of four new “Earth Networks” to facilitate interdisciplinary collaboration across Columbia University and develop fresh approaches to research, education and impact on themes related to climate, sustainability and the future of the planet. Among other outputs aligned with the four purposes of Columbia University and the Climate School, the network is developing a trans-disciplinary course, teaching aids, and curriculum-building tools on climate mobility.
WINTER/SPRING 2023 COURSES
Course#: GR5013
School: Climate and Society Master’s Program, The Climate School
Mode: designated campus classroom
Instructors: Susana Adamo and Alex de Sherbinin
Description: This team-taught interdisciplinary course focuses on the social, demographic, economic, political, environmental and climatic factors that shape mobility as well as the legal categories of international mobility (e.g., migrant versus refugee), exploring underlying drivers of the various types of migration—from forced to voluntary—in order to better understand current and future trends. It brings to the fore equity, climate justice, and human rights considerations, as well as the mental health dimensions of climate displacement and migration. The course will offer students the opportunity to undertake a role-play that explores policy and programmatic responses to climate migration, guided by leading policy experts. In a simulated negotiation of the Global Compact for Migration (GCM), teams of students will employ the knowledge they have gained and a zero-draft of the compact to negotiate the final text of the GCM, taking into account the interests of the party they will represent. This component builds on the Climate School’s Fourth Purpose, which is to apply research to policy design and programs.
Course#: SUMA PS5255
School: School of Professional Studies/Sustainability Management Program
Mode: designated campus classroom
Instructor: Greg Yetman
Description: Data science is an exciting new field of applied research that takes advantage of the ever-growing volume of data being collected to support decision-making in both the public and private sectors, with limits and issues that are important to understand before applying to problem solving. In anticipation of increased use of data science in promoting sustainability, this course introduces the common methods used in data science, best practices in data management, and the basic scripting skills required to start analyzing data in R and Python. After introducing foundational scripting and data analysis methods, a case study approach will be used to highlight both what can be accomplished with data analysis and the limits of the data and methods used. Lab exercises will teach basic skills in scripting in Python and R and then move to a common approach for data analysis: adapting existing scripts and software libraries to solve applied data problems.
Course#: SDEV W3390
School: Columbia College
Mode: designated campus classroom
Instructor: Linda Pistolesi
Description: A comprehensive overview of theoretical concepts underlying geographic information systems (GIS) as a foundation for practical GIS skills used in sustainable development research. Through a mix of lectures, readings, focused discussions, and hands-on exercises, students will acquire an understanding of the variety and structure of spatial data and databases, gain knowledge of the principles behind raster- and vector-based spatial analysis, and learn basic cartographic principles for producing maps that effectively communicate a message. Students will also learn to use emerging Web-based mapping tools such as Google Earth, Google Maps, and similar tools to develop online interactive maps and graphics. The use of other geospatial technologies such as the Global Positioning System (GPS) will also be explored.
Course#: SDEV W3450
School: Columbia College
Mode: designated campus classroom
Instructor: Kytt MacManus
Description: This is an intermediate course in spatial modeling developed specifically for students in the Undergraduate Sustainable Development program. This course will provide a foundation for understanding a variety of issues related to spatial analysis and modeling. Students will explore the concepts, tools, and techniques of GIS modeling and review and critique modeling applications used for environmental planning and policy development.
FALL 2022 COURSES
Course#: SDEV W3390
School: Columbia College
Instructor: Kytt McManus
Description: A comprehensive overview of theoretical concepts underlying geographic information systems (GIS) as a foundation for practical GIS skills used in sustainable development research. Through a mix of lectures, readings, focused discussions, and hands-on exercises, students will acquire an understanding of the variety and structure of spatial data and databases, gain knowledge of the principles behind raster- and vector-based spatial analysis, and learn basic cartographic principles for producing maps that effectively communicate a message. Students will also learn to use emerging Web-based mapping tools such as Google Earth, Google Maps, and similar tools to develop online interactive maps and graphics. The use of other geospatial technologies such as the Global Positioning System (GPS) will also be explored.
Course#: SUMA PS5255
School: School of Professional Studies/Sustainability Management Program
Instructor: Greg Yetman
Description: Data science is an exciting new field of applied research that takes advantage of the ever-growing volume of data being collected to support decision-making in both the public and private sectors, with limits and issues that are important to understand before applying to problem solving. In anticipation of increased use of data science in promoting sustainability, this course introduces the common methods used in data science, best practices in data management, and the basic scripting skills required to start analyzing data in R and Python. After introducing foundational scripting and data analysis methods, a case study approach will be used to highlight both what can be accomplished with data analysis and the limits of the data and methods used. Lab exercises will teach basic skills in scripting in Python and R and then move to a common approach for data analysis: adapting existing scripts and software libraries to solve applied data problems.
Course #: SDEV3400
School: Ecology, Evolution, and Environmental Biology Dept (E3B)
Instructor: Susana Adamo
Description: Demographic processes and their outcomes in terms of population size, distribution and characteristics have a fundamental role in sustainable development and also broad policy implications. This course will introduce students to the scientific study of human populations as a contribution toward their understanding of social structure, relations, and dynamics, as well as society-nature interactions. We will consider the implications for population-environment relationships in the context of consumption trends, economic development, sustainability and cultural change. The aim is to offer a basic introduction to the main theories, concepts, measures, and uses of demography. The course will address: (a) the issues of population size, distribution and composition at different scales from global to regional to local; and (b) the fundamental demographic processes of mortality, fertility and migration, including their trends and transitions. The course will also include an overview of basic demographic techniques and tools used for identifying, managing, analyzing and interpreting population data, and an introduction to population projections. These assignments and exercises will complement readings and lectures by enabling students to explore data sources, calculate rates, and graphically represent demographic data.