Climate in Practice: Professional Founda

Climate in Practice-Found

This course is a core course for all Climate School students in the MA in Climate and Society and MS in Climate 1.5 credits in the fall and 1.5 credits in the spring. It is a practicum-style course focused on the application of classroom learnings in a range of professional and real-world situations.

At the beginning of the fall semesters, students will be grouped in teams and assigned a previous years’ Capstone project (a summer project that former CS students have produced in partnership with an external partner). Students will use this previous capstone project to practice skills including: stakeholder engagement strategies, communication and presentation skills, systems thinking, and project planning. 

The fall will be focused on grounding in the topic and challenge of the capstone project, stakeholder discovery and mock engagement, and evaluating its application to the New York City context. The spring will be focused on evaluation of problem definition of the client, work planning and project planning, learning from the client and/or alumni about the outcomes and contemporary challenges/applications of the project, and producing a final project as a team. By the end, students will be prepared to fully engage with their own capstone projects in future semesters, will have honed critical skills to support successful professional applications of their Climate School courses, and will have a ‘mission and values statement’ to guide their future practice as professionals.

• Department: Climate School
• F 10:10 a.m.-11:25 a.m.
• Instructor: Radley M Horton
  Info
• Instructor: Marcia N Macedo
  Info
• 1.5 points

MATH FINANCE PRACTITIONERS SEMINAR I

MATH FINANCE PRACTITION S

This seminar offers participants the opportunity to listen to practitioners discuss a range of important topics in the financial industry.  Topics may include portfolio optimization, exotic derivatives, high frequency analysis of data and numerical methods. While most talks require knowledge of mathematical methods in finance, some talks are accessible to a more general audience.

• Department: Mathematics
• W 6:10 p.m.-7:25 p.m.
• Instructor: Jaehyuk Choi
  Info
• 1.5 points

INTERNSHIP IN SOCIAL RESEARCH

This course gives students two credits of academic credit for the work they perform in such an social science oriented internships.

• Department: Quantitative Methods/Social Sciences
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 1 points

CRITICAL URBAN INFRASTRUCTURE FOR SUSTAI

What are urban infrastructures that promote sustainability? Such infrastructure must reduce environmental pollution at all scales, provide necessary urban services efficiently and enhance urban resilience to multiple potential crises. Sustainable infrastructure also must promote social and economic equity and environmental justice. And sustainable infrastructure must be economically feasible. This class will use these concepts to evaluate urban infrastructure and identify challenges to making urban infrastructure sustainable. Importantly, the course will use theories of urban transitions to help identify the drivers of potential change in infrastructure development and envision the emergence of sustainable infrastructure. This class will examine these notions across the energy, transportation, water supply and waste water treatment, buildings, health and open space urban sectors.

• Department: Sustainability Management
• R 6:10 p.m.-8 p.m.
• Instructor: Peter J Marcotullio
  Info
• 3 points

New York City s Climate Policy From Sa

NYC Climate Policy Sandy-

Following the events of Hurricane Sandy, New York City has emerged as a leading city for climate action, pushing forward and experimenting with a broad range of climate policies and tools, including climate adaptation and resilience measures, decarbonization actions and legislation, environmental justice, and fossil fuel divestment, among others. 

This course will offer a focused study of New York City’s approach to confronting our climate crisis.  This will include an exploration of the many actions taken by NYC, their effectiveness, and proposals to build upon or improve them. This course is designed to encourage active participation and practical application of the material. The assignments and activities aim to help students build a solid understanding of key concepts while developing analytical skills, which will then apply to real-world scenarios. Guest lecturers with experience in New York City’s climate policy actions may join from time to time.

• Department: Climate School
• R 4:10 p.m.-6:40 p.m.
• Instructor: Daniel A Zarrilli
  Info
• 1.5 points

INTERNSHIP IN SOCIAL RESEARCH II

INTERNSHIP IN SOCIAL RSRC

• Department: Quantitative Methods/Social Sciences
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 1 points

SOCIOLOGICAL THEORY

This course emphasizes the perspectives of foundational thinkers on the evolution and dynamics of social life. Readings address key sociological questions; including the configuration of communities, social control, institutions, exchange, interaction, and culture.

• Department: Sociology
• W 2:10 p.m.-4 p.m.
• Instructor: Peter Bearman
  CULPA: 19 Wikipedia h-index: 54 Info
• 4 points

International Climate Finance

Course Objectives 

By the end of this course, you should be able to: 


Understand and critically engage with the varied narratives, objectives, and instruments of climate finance, and how they apply across sectors, regions, and institutional settings. 


Understand and analyze the roles of key actors—governments, multilateral institutions, investors, insurers, and civil society—and the financial and legal mechanisms that shape capital flows. 


Identify and assess the persistent challenges in climate finance, including disparities in access and cost of capital, weak institutional coherence, and gaps between risk frameworks and climate impact. 


Critically evaluate current tools and approaches—such as blended finance, credit guarantees, insurance products, and rating systems—and engage with proposals for structural reform.

• Department: Climate School
• F 10:10 a.m.-12:40 p.m.
• 3 points

PRACTICUM IN DATA ANALYSIS

This practicum course is meant to offer valuable training to students. Specifically, this practicum will mimicthe typical conditions that students would face in an internship in a large data-intense institution. Thepracticum will focus on four core elements involved in most internships: (1) Developing the intuition andskills to properly scope ambiguous project ideas; (2) practicing organizing and accessing a variety oflarge-scale data sources and formats; (3) conducting basic and advanced analysis of big data; and (4)communicating and “productizing” results and findings from the earlier steps, in things like dashboards,reports, interactive graphics, or apps. The practicum will also give students time to reflect on their work, andhow it would best translate into corporate, non-profit, start-up and other contexts.

• Department: Quantitative Methods/Social Sciences
• M 4:10 p.m.-6 p.m.
• Instructor: Nicole C Dicrecchio
  Info
• 3 points

PRACTICUM IN DATA ANALYSIS II

This practicum will mimic the typical conditions that students would face in an internship in a

large data-intense institution. The practicum will focus on four core elements involved in most

internships:

• developing the intuition and skills to properly scope ambiguous project ideas;

• practicing organizing and accessing a variety of large-scale data sources and formats;

• conducting basic and advanced analysis of big data; and

• communicating and “productizing” results and findings from the earlier steps, in things

like dashboards, reports, interactive graphics, or apps.

The practicum will also give students time to reflect on their work, and how it would best

translate into corporate, non-profit, start-up and other contexts.

• Department: Quantitative Methods/Social Sciences
• M 4:10 p.m.-6 p.m.
• Instructor: Nicole C Dicrecchio
  Info
• 3 points

INTERNSHIP IN SOCIAL III

INTERNSHP III

Students enrolled in the Quantitative Methods in the Social Sciences M.A. program have a number of opportunities for internships with various organizations in New York City. Over the past three years, representatives from a number of different organizations – including ABC News, Pfizer, the Manhattan Psychiatric Center, Merrill Lynch, and the Robert Wood Johnson Foundation – have approached students and faculty in QMSS about the possibility of having QMSS students work as interns. Many of these internships require students to receive some sort of course credit for their work. All internships will be graded on a pass/fail basis.

• Department: Quantitative Methods/Social Sciences
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 1 points

PRACTICUM IN DATA ANALYSIS

This practicum course is meant to offer valuable training to students. Specifically, this practicum will mimicthe typical conditions that students would face in an internship in a large data-intense institution. Thepracticum will focus on four core elements involved in most internships: (1) Developing the intuition andskills to properly scope ambiguous project ideas; (2) practicing organizing and accessing a variety oflarge-scale data sources and formats; (3) conducting basic and advanced analysis of big data; and (4)communicating and “productizing” results and findings from the earlier steps, in things like dashboards,reports, interactive graphics, or apps. The practicum will also give students time to reflect on their work, andhow it would best translate into corporate, non-profit, start-up and other contexts.

• Department: Quantitative Methods/Social Sciences
• R 6:10 p.m.-8 p.m.
• Instructor: Charles R Riemann
  Info
• 3 points

PRACTICUM IN DATA ANALYSIS

This practicum course is meant to offer valuable training to students. Specifically, this practicum will mimicthe typical conditions that students would face in an internship in a large data-intense institution. The practicum will focus on four core elements involved in most internships: (1) Developing the intuition andskills to properly scope ambiguous project ideas; (2) practicing organizing and accessing a variety oflarge-scale data sources and formats; (3) conducting basic and advanced analysis of big data; and (4)communicating and “productizing” results and findings from the earlier steps, in things like dashboards,reports, interactive graphics, or apps. The practicum will also give students time to reflect on their work, andhow it would best translate into corporate, non-profit, start-up and other contexts.

• Department: Quantitative Methods/Social Sciences
• R 6:10 p.m.-8 p.m.
• Instructor: Charles R Riemann
  Info
• 3 points

GIS for Climate Data Analysis

GIS for Climate Data Analysis will provide a foundation for understanding and applying spatial analysis and modeling with GIS and Climate Data. This course is focused on a rigorous look at the analysis of climate data in different contexts through a combination of lectures, labs, applied assignments, and a final project. Underlying all of the analyses will be the goal of learning how to apply spatial statistical and data visualization techniques to inform decision-making. The course exercises will illustrate the research process life cycle from data collection to publication preparation.  

Students will explore concepts, tools, and techniques of GIS modeling and review and critique modeling applications used in a variety of contexts. The course will also offer students the opportunity to design, build and evaluate their own spatial analysis models. The course will cover both vector and raster-based methods of analysis.  

We will draw examples from a wide range of applications in climate data analysis, use of satellite earth-observation (EO) data, climate risk assessment, climate data visualization, and how EO data are collected and used to map the spatial and temporal dimensions of climate and environmental change. Hands-on work will introduce students to a wide range of EO data – such as Sentinel, Landsat, and PlanetScope – and scripting in Python, (e.g., to build new data sets or map climate hazards and risks to support climate adaptation and decision-making).

• Department: Climate School
• R 1:10 p.m.-3:40 p.m.
• Instructor: Gregory G Yetman
  Info
• 3 points

Advanced Quantitative Methods for Climat

Advanced Quant Methods

This course builds on the quantitative analysis tools developed in the core course Quantitative Methods of Climate Applications and the certificate core course Computing and Research Methods for Climate Data. It focuses on advanced methods in hypothesis testing, regression models, time series and spectral analysis, geospatial analysis, significance testing, uncertainty quantification, modeling for assessing climate risk, and decision theory. Through in-class practice and course assignments using Python programming, students will apply these methods to understand climate signals, conduct risk assessments, and evaluate the value of climate information in decision-making.

• Department: Climate School
• W 1:10 p.m.-3:40 p.m.
• Instructor: Chia-Ying Lee
  Info
• 3 points

SUSTAINABLE FASHION AND STARTUP STRATEGY

SUST FASHION & STARTUP ST

Fashion’s consistent ranking among the top 3 global polluters has become a decades old fact struggling to gain a proportionate response among the brand startup and sourcing community. With industry revenues set to exceed $1 trillion, there is an opportunity to critically address existing revenue models predicated on traditional metrics, such as constant growth, and singular bottom lines. The course attempts to create a nexus between the fashion entrepreneur and systems thinker to explore strategic solutions that address sustainability though an environmental, social and economic lens. The aim is to foster a mindful, yet critical discourse on fashion industry initiatives, past and present, and to practice various tools that help transition existing organizations and incubate new startups towards sustainable outcomes.

• Department: Sustainability Management
• T 6:10 p.m.-8 p.m.
• Instructor: Ammar Belal
  Info
• 3 points

SUSTAINABLE FASHION AND STARTUP STRATEGY

SUST FASHION & STARTUP ST

Fashion’s consistent ranking among the top 3 global polluters has become a decades old fact struggling to gain a proportionate response among the brand startup and sourcing community. With industry revenues set to exceed $1 trillion, there is an opportunity to critically address existing revenue models predicated on traditional metrics, such as constant growth, and singular bottom lines. The course attempts to create a nexus between the fashion entrepreneur and systems thinker to explore strategic solutions that address sustainability though an environmental, social and economic lens. The aim is to foster a mindful, yet critical discourse on fashion industry initiatives, past and present, and to practice various tools that help transition existing organizations and incubate new startups towards sustainable outcomes.

• Department: Auditing
• T 6:10 p.m.-8 p.m.
• Instructor: Ammar Belal
  Info
• 0 points

Scientific Programming for Sustainabilit

Students in the Master of Science in Sustainability Science program will encounter a range of scientific problems throughout their Science-specific courses that require a strong working knowledge of computer programming.  This course provides an introduction to scientific programming using Python. Computer coding skills gained in the course will prepare students for coursework in the Master of Science in Sustainability Science program as well as to succeed in a career having a programming component.  Students enrolled in this course will learn through lectures, class discussion, and hands-on exercises that address the following topics:


Basics of computer programming, including precision of variables, arrays and data structures, input/output, control flow, and subroutines.

Applying Python to read common scientific data formats, including NetCDF for gridded climate and other environmental data.

Applying Python for data analysis, with a focus on popular machine learning methods including linear regression, decision trees, neural networks, principal component analysis, and clustering.

Applying Python to visualize scientific data through basic X-Y plots as well as images of data fields on a global map.


This course will train students to analyze and model scientific data using Python in order to better understand current and future environments and their interactions with human systems.  By learning analysis and modeling with Python, students will be better able to inform sustainability policy, management, and decision-making.

• Department: Sustainability Science
• T 4:10 p.m.-6 p.m.
• Instructor: Michael Previdi
  Info
• 3 points

PROSEMINAR I

The Proseminar fulfills two separate goals within the Free-Standing Masters Program in Sociology. The first is to provide exposure, training, and support specific to the needs of Masters students preparing to move on to further graduate training or the job market. The second goal is to provide a forum for scholars and others working in qualitative reserach, public sociology, and the urban environment.

• Department: Sociology
• R 4:10 p.m.-6 p.m.
• Instructor: Ryan Hagen
  Info
• 1 points

FIELD WORK I

This two-semester sequence supports students through the process of finding a fieldwork site, beginning the field work required to plan for and develop a Masters thesis, and the completion of their Masters thesis.

• Department: Sociology
• F 4:10 p.m.-6 p.m.
• Instructor: Ryan Hagen
  Info
• 1 points

Introduction to Climate Modeling

Intro to Climate Modeling

Earth system and climate models are critically important tools for climate science research. They are used to study climate variability in the past, determine how climate change has contributed to recent trends and extreme events, and understand how various natural and anthropogenic forcing affect the evolution of the climate system now and into the future. This class serves as an introduction to the history, development, process representations, and practical application of earth system models. 

Students will learn the history and evolution of earth system modeling and how these models have been used to inform some of the most important topics in climate science (e.g., detection and attribution, future projections, climate sensitivity). Lectures, and associated lab work, will introduce the processes integrated into various components of earth system models (e.g., atmosphere, ocean, land, carbon cycle, etc), important interactions between these components (e.g., climate system feedbacks, climate sensitivity), and how earth system models are used for future projections. Students will familiarize themselves with the wealth of climate model simulation data available from free public archives (e.g., CMIP6, the Multi-Model Large Ensemble Project), the protocols used for designing and running simulations, and practical tools for analyzing these datasets. 

Classroom lectures will be supplemented by practical lab-assignments, where the students will use and develop their own models demonstrating the concepts learned in class.  As a final project, students will develop their own research questions using available climate model simulations for their primary analyses.

• Department: Climate School
• R 10:10 a.m.-12:40 p.m.
• Instructor: Dhruv Balwada
  Info
• 3 points

THESIS SEMINAR I

This seminar gives you an opportunity to do original sociological research with the support of a faculty member, a teaching assistant, and your fellow classmates.

• Department: Sociology
• T 2:10 p.m.-4 p.m.
• W 10:10 a.m.-noon
• Instructor: Adam Reich
  CULPA: 7 Info
• Instructor: Ryan Hagen
  Info
• 3 points

NATURAL LANG PROCESSING SOCIAL SCIENCES

Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.

• Department: Quantitative Methods/Social Sciences
• M 6:10 p.m.-8 p.m.
• T 2:10 p.m.-4 p.m.
• Instructor: Patrick Houlihan
  Info
• Instructor: Samira Zaroudi
  Info
• 3 points

Methods for Analysis of Food Systems and

Food Systems Analysis

The Methods for Analysis of Food Systems and Climate course is a required course for the Climate and Food Systems Certificate. Building on the core knowledge provided in the Global Food Trade, Shocks, and Migration and Food Systems and Climate Interactions courses, the first half of the class will cover foundational qualitative and quantitative methods employed in food systems and climate research. These methods include surveys, participatory research, life cycle assessment, spatial analysis, and more. The second half of the course will cover key applied methods used to analyze climate and environmental problems, including understanding the environmental footprint of the current food system, climate scenarios, climate risk and vulnerability assessment, and the economic and social disruptions these changes generate. By the end of the course, students will learn how to apply these methods to assess both the impacts of climate change on aspects of food production, distribution, and consumption, the impact of food systems on the changing climate, and approaches to reduce emissions and enhance the resilience and efficiency of food systems. Students will also learn how to apply these tools for policy analysis and recommend effective policies that support sustainable and resilient food systems to mitigate the impacts of climate change. The course will involve a mix of instructor and guest lectures, case studies, readings, hands-on group projects, and practical exercises to enhance the students' analytical and problem-solving skills.

• Department: Climate School
• M 1:10 p.m.-3:40 p.m.
• Instructor: Sarah L Blakeley
  Info
• 3 points

THE COMPELLING COMMUNICATOR

The ability to communicate effectively is a key competency for professionals. As emerging industry leaders, understanding the audience, framing the message, and using media channels to achieve specified objectives are critical skills, whether written or spoken. Through a variety of written and oral assignments, students learn to apply foundational communication theory to inform and engage stakeholders. The first part of the course focuses on written deliverables, emphasizing audience-framed messaging and developing simple, clear and persuasive content. The second part transitions to enhancing spoken delivery and presentation skills where students gain experience in speechwriting, storytelling and using data visualization to motivate an audience to act.

• Department: Strategic Communication
• T 10:10 a.m.-noon
• T 2:10 p.m.-4 p.m.
• T 10:10 a.m.-noon
• Instructor: Josephine Cox-Saleh
  Info
• 3 points

GIS & SPATIAL ANALYSIS-SOC SCI

Prerequisites: Undergraduate Statistics This course introduces students to basic spatial analytic skills. 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 spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well.

• Department: Quantitative Methods/Social Sciences
• W 12:10 p.m.-2 p.m.
• Instructor: Edwin Y Grimsley
  Info
• 3 points

MODERN DATA STRUCTURES

This course is intended to provide a detailed tour on how to access, clean, “munge” and organize data, both big and small. (It should also give students a flavor of what would be expected of them in a typical data science interview.) Each week will have simple, moderate and complex examples in class, with code to follow. Students will then practice additional exercises at home. The end point of each project would be to get the data organized and cleaned enough so that it is in a data-frame, ready for subsequent analysis and graphing. Therefore, no analysis or visualization (beyond just basic tables and plots to make sure everything was correctly organized) will be taught; and this will free up substantial time for the “nitty-gritty” of all of this data wrangling.

• Department: Quantitative Methods/Social Sciences
• W 6:10 p.m.-8 p.m.
• Instructor: Thomas Brambor
  h-index: 9 Info
• 3 points

MACHINE LEARNING SOC SCI

Prerequisites: basic probability and statistics, basic linear algebra, and calculus This course will provide a comprehensive overview of machine learning as it is applied in a number of domains. Comparisons and contrasts will be drawn between this machine learning approach and more traditional regression-based approaches used in the social sciences. Emphasis will also be placed on opportunities to synthesize these two approaches. The course will start with an introduction to Python, the scikit-learn package and GitHub. After that, there will be some discussion of data exploration, visualization in matplotlib, preprocessing, feature engineering, variable imputation, and feature selection. Supervised learning methods will be considered, including OLS models, linear models for classification, support vector machines, decision trees and random forests, and gradient boosting. Calibration, model evaluation and strategies for dealing with imbalanced datasets, n on-negative matrix factorization, and outlier detection will be considered next. This will be followed by unsupervised techniques: PCA, discriminant analysis, manifold learning, clustering, mixture models, cluster evaluation. Lastly, we will consider neural networks, convolutional neural networks for image classification and recurrent neural networks. This course will primarily us Python. Previous programming experience will be helpful but not requisite. Prerequisites: basic probability and statistics, basic linear algebra, and calculus.

• Department: Quantitative Methods/Social Sciences
• T 6:10 p.m.-8 p.m.
• R 4:10 p.m.-6 p.m.
• Instructor: Mazen Asaad
  Info
• Instructor: Alexander Peterhansl
  Info
• 3 points

PROJECTS IN ADVANCED MACHINE LEARNING

ADV MACHINE LEARNING

Machine learning algorithms continue to advance in their capacity to predict outcomes and rival human judgment in a variety of settings.  This course is designed to offer insight into advanced machine learning models, including Deep Learning, Recurrent Neural Networks, Adversarial Neural Networks, Time Series models and others.  Students are expected to have familiarity with using Python, the scikit-learn package, and github.  The other half of the course will be devoted to students working in key substantive areas, where advanced machine learning will prove helpful -- areas like computer vision and images, text and natural language processing, and tabular data.  Students will be tasked to develop team projects in these areas and they will develop a public portfolio of three (or four) meaningful projects.  By the end of the course, students will be able to show their work by launching their models in live REST APIs and web-applications.

• Department: Quantitative Methods/Social Sciences
• R 6:10 p.m.-8 p.m.
• Instructor: Alexander Peterhansl
  Info
• 3 points

ORAL HISTORY INTERNSHIP

• Department: Oral History
• W 2:10 p.m.-3 p.m.
• Instructor: Amy A Starecheski
  h-index: 5 Info
• 2-4 points

THE AUTHENTIC LEADER

Effective leaders are able to think critically about problems and opportunities, imagine unexpected futures, craft a compelling vision, and drive change. In this course, we study the theoretical underpinnings of leadership communication, relying on empirical evidence as a guide for practice. Students gain important perspective on leadership styles, mastering the competencies required for a variety of contexts.

• Department: Strategic Communication
• M 4:10 p.m.-6 p.m.
• Instructor: Caitlin McClure
  Info
• 3 points

Quantum Physics Laboratory

The “Quantum Physics Lab” will give students in the Quantum Science and Technology Masters program hands-on experience in quantum physics and its applications. Students will work in small groups on several distinct experiments through the semester. Each experimental project might last for 3-4 weeks, comprising the steps outlined in the Program below. Initial experimental offerings include: a quantum optics (entangled photon) platform, a Josephson junction experiment, a nitrogen vacancy (NV) center for direct manipulation of quantum states, along with experiments on nuclear magnetic resonance, quantum conductance and the quantum Hall effect. We expect to add additional experiments in the near future.

Students will observe and measure fundamental quantum behaviors, reinforcing material they are learning in the Masters lecture courses, while simultaneously being introduced to forefront technology that will be the basis of the second “quantum revolution” that could eventually lead to revolutionary applications in electronics, computing, energy technology and medical devices.    

Program:

1 x 230 min lab meeting per week (small group work)

Background research on selected experiments, and associated physics and instrumentation

Data analysis, discussions with instructor and teaching assistants

Project writeups and presentations

• Department: Physics
• M 1:10 p.m.-5 p.m.
• Instructor: Abhay N Pasupathy
  CULPA: 10 Info
• 3 points

DIGITAL MEDIA & ANALYTICS

Digital media opens new opportunities for increasingly targeted communications across a variety of channels, which rapidly expands the importance of analytics in tracking and measuring key performance indicators (KPIs). This course prepares students to work within data- and model-driven environments with an emphasis on using analytics to develop insights and support strategic decisions.

• Department: Strategic Communication
• R 12:10 p.m.-2 p.m.
• Instructor: Lilian Ore
  Info
• 3 points

DIGITAL MEDIA & ANALYTICS

Digital media opens new opportunities for increasingly targeted communications across a variety of channels, which rapidly expands the importance of analytics in tracking and measuring key performance indicators (KPIs). This course prepares students to work within data- and model-driven environments with an emphasis on using analytics to develop insights and support strategic decisions.

• Department: Auditing
• R 12:10 p.m.-2 p.m.
• Instructor: Lilian Ore
  Info
• 0 points

VIDEO PRODUCTION FOR VISUAL STORYTELLING

VIDEO PRO FOR VISUAL STOR

Students will have hands-on learning experiences using camera controls and techniques and optics to accentuate psychological and atmospheric aspects surrounding the subject. Additionally, through visual storytelling, composition and basic color theory students will understand how to incorporate theories of cinematic language to emphasize the mood and perception of the story. This course will cover basic lighting techniques for the interview in a hands-on practical experience that will strengthen participants’ camera, cinematography and storytelling skills. Students will complete the course by creating a final short video, having collaboratively conceptualized, filmed, interviewed and shot the necessary B-roll to structure a basic visual storytelling piece with the use of image, sound and basic editing.

• Department: Oral History
• T 2:10 p.m.-5 p.m.
• Instructor: Oscar Frasser
  Info
• 4 points

APPLIED ANALYTICS IN ORGANIZATIONAL CONT

APP AN IN ORG CONTEXT

• Department: Applied Analytics
• R 8:10 p.m.-10 p.m.
• T 6:10 p.m.-8 p.m.
• S 10:10 a.m.-noon
• R 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• F 10:10 a.m.-noon
• R 6:10 p.m.-8 p.m.
• T 6:10 p.m.-8 p.m.
• Instructor: Jisang Pack
  Info
• Instructor: Lori Cenci
  Info
• Instructor: Anjana Bhattacharyya
  Info
• Instructor: Huan Liu
  Info
• Instructor: Panagiotis Tarhanidis
  Info
• Instructor: Joseph R Schlosser
  Info
• Instructor: Ashoke S Bhattacharjya
  Info
• Instructor: David Kreutter
  Info
• 3 points

ESTIMATING PROJECT COSTS

• Department: Construction Administration
• T 6:10 p.m.-8 p.m.
• Instructor: Kurt C Decker
  Info
• 3 points

Managerial Statistics

• Department: Decision, Risk and Operations
• T 8:30 a.m.-11:30 a.m.
• Instructor: Assaf Zeevi
  h-index: 40 Info
• 3 points

VALUE-BASED ENTERPRISE RISK MANAGEMENT

VALUE-BASED ERM

Foundational ERM course. Addresses all major ERM activities: risk framework; risk governance; risk identification; risk quantification; risk decision making; and risk messaging. Introduces an advanced yet practical ERM approach based on the integration of ERM and value-based management that supports integration of ERM into decision making. Provides a context to understand the differences between (a) value-based ERM; (b) traditional ERM; and (c) traditional "silo" risk management.

• Department: Enterprise Risk Management
• M 8:10 p.m.-10 p.m.
• W 8:10 p.m.-10 p.m.
• Instructor: Duncan Campbell
  Info
• Instructor: David A. Bartholomew
  Info
• 3 points

ELEMENTS OF DRAMATIC NARRATIVE

A lecture and discussion course on the basics of feature-length screenwriting. Using written texts and films screened for class, the course explores the nature of storytelling in the feature-length film and the ways in which it is an extension and an evolution of other dramatic and narrative forms. A basic part of Film’s first year program, the course guides students in developing the plot, characters, conflict and theme of a feature-length story that they will write, as a treatment, by the end of the semester.

• Department: Film
• M 10 a.m.-1 p.m.
• Instructor: Andrew S Bienen
  Info
• 3 points

INTRODUCTION TO HUMAN CAPITAL MANAGEMENT

• Department: Human Capital Management
• T 6:10 p.m.-8 p.m.
• T 8:30 p.m.-10 p.m.
• Instructor: Steven I Safier
  Info
• Instructor: Aaron S Wallen
  CULPA: 1 h-index: 5 Info
• 3 points

PRODUCT, PRICING & DISTRIBUTION

PRODUCT, PRICING & DISTRB

The insurance business is an outward facing business built around selling products to individual and business consumers. Therefore, insurance service providers, like all sophisticated consumer-driven businesses, must carefully and constantly assess their markets and strategies to remain relevant in a highly competitive environment.  From consumer data analytics, to proper risk pricing, to efficient distribution channels, to navigating social media, to managing the highly regulated nature of insurance sales and distribution, insurance providers operate in a highly competitive environment that rewards discipline as well as innovation.  Successful companies identify and make tough decisions to correct underperforming parts of their portfolios and they temper their approaches to new products where loss costs and pricing requirements are uncertain.  They innovate by thinking first about new and evolving loss exposures their customers face and develop insurance products and services that respond.  They focus on the client experience through the entire insurance process and create specialized/differentiated products and services to either avoid commoditization or leverage it, depending on the needs of that market and the strengths of that insurer.

The focus of this core course, in MSIM’s Insurance Rotation area of study, will include the history and the evolution of the insurance industry across the three main insurance sectors, i.e. property/casualty, life and health.  The course will address factors that drive company investment in and/or withdrawal from specific products and markets and the complexities around developing, pricing and selling a product for which costs are determined only after claims have been paid – something that often occurs many years after the policy was sold.   The course will consider how providers are expanding beyond traditional products into related services and how technology is increasing innovation around product design and marketing.

• Department: Insurance Management
• Instructor: Louis Weltmann
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• 3 points

SELF & OTHER IN CLIN ENCOUNTER

TBD

• Department: Narrative Medicine
• M 2:10 p.m.-5 p.m.
• Instructor: Michael E Davidovits
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• Instructor: Craig Irvine
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• 4 points

SYNTHESIS/ASSESSMENT

• Department: Pathology
• Instructor: Lyndsey Adams
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• 0 points

Leadership and Team Dynamics

This course introduces students to the core principles of effective leadership and collaborative team performance in organizational settings. Through a practical, evidence-based approach, the course examines how leaders influence outcomes, foster engagement, and navigate challenges in dynamic, multidisciplinary environments. Students will explore leadership qualifications, strategic decision-making, ethical considerations, and performance development frameworks. Emphasis is placed on understanding the dynamics of team formation, multicultural collaboration, communication, conflict management, and high-performance team practices.

As a central component of the Project Management curriculum, this course supports the program’s larger goal of preparing graduates to lead effectively in diverse and evolving organizational contexts. By grounding students in evidence-based leadership concepts and team effectiveness frameworks, the course advances the discipline’s primary principles of organizational performance, collaboration, and responsible decision-making. The course aligns closely with other program requirements by complementing technical project management competencies with the interpersonal and strategic skills necessary for successful project execution. In doing so, it bridges technical expertise with leadership acumen, equipping students with a holistic foundation for professional growth.

This is a required core course for all Project Management students and is delivered in person on campus in a full-semester format. Space permitting, the course may also be open to cross-registrants from other Columbia University graduate programs where leadership, management, and teamwork skills are relevant, such as programs in management, public administration, and engineering. There are no formal prerequisites, though prior exposure to management or organizational behavior may be helpful in engaging with course materials. Students will participate in selected readings, interactive discussions, and team-based exercises, as well as hear from guest lecturers with extensive leadership experience. By the end of the course, students will have strengthened their ability to lead ethically, communicate clearly, manage team dynamics, and contribute meaningfully to organizational goals.

• Department: Project Management
• T 6:10 p.m.-8 p.m.
• T 4:10 p.m.-6 p.m.
• T 6:10 p.m.-8 p.m.
• 3 points

English for International and Public Aff

English for Int'l & Publi

• Department: International and Public Affairs
• MW 10 a.m.-11:50 a.m.
• TR 10 a.m.-11:50 a.m.
• Instructor: Brittany Ober
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• Instructor: Carolyn Dunn
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• 3 points

PAINTING I

• Department: Visual Arts
• M 10 a.m.-4 p.m.
• F 10 a.m.-4 p.m.
• Instructor: Dana J Lok
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• Instructor: Kenny Rivero
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• 3 points

THE WEALTH MANAGEMENT LANDSCAPE

This course will provide an overview of the wealth management profession,
including various business models and the role of the advisor within each. Guest
speakers from across the wealth management profession will discuss the various
business models, key trends, including the intersection of technology and wealth
management and the unique nature of each client planner relationship. This course
will also highlight additional services that advisors are offering clients in order to
provide a full suite of solutions. In addition, students will discuss the role and
function of family offices, the scope of services they offer and best practices to
managing a family office.

• Department: Wealth Management
• Instructor: William Woodson
  Info
• 3 points

FICTION WORKSHOP

• Department: Writing
• M 10 a.m.-1 p.m.
• T 4:15 p.m.-7:15 p.m.
• M 4:15 p.m.-7:15 p.m.
• R 1:10 p.m.-4:10 p.m.
• T 1:10 p.m.-4:10 p.m.
• M 1:10 p.m.-4:10 p.m.
• T 1:10 p.m.-4:10 p.m.
• M 1:10 p.m.-4:10 p.m.
• R 1:10 p.m.-4:10 p.m.
• Instructor: Diksha Basu
  Wikipedia Info
• Instructor: Sophie M Dess
  Info
• Instructor: David M Gordon
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• Instructor: Laura V Green
  Info
• Instructor: Heidi S Julavits
  CULPA: 1 Wikipedia Info
• Instructor: Victor D Lavalle
  CULPA: 2 Wikipedia Info
• Instructor: Hilary J Leichter
  Info
• Instructor: Samuel Lipsyte
  CULPA: 5 Info
• Instructor: Benjamin Marcus
  Info
• Instructor: Cleyvis Natera
  Info
• Instructor: Kat Tang
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• Instructor: Galt V Niederhoffer
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• 6 points

FINANCE AND ACCOUNTING FOR THE CONSTRUCT

FIN & ACTG FOR CONSTR IND

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• Department: Construction Administration
• T 6:10 p.m.-8 p.m.
• Instructor: William Fischer
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• 3 points

Business Analytics

• Department: Decision, Risk and Operations
• Instructor: Faculty
  h-index: 37 Info
• 1.5 points

Insurance Management Student Community C

IMGT SCC

Insurance Management Student Community Center helps facilitate remote pre-residency requirements and preparatory activities to preserve the limited in-person time we have during the residency for other activities. Given that we are a remote program, this is the most effective way to introduce, assign, inform and track new student activity prior to starting the core courses. The use of a dedicated site helps the students practice using the LMS, in addition to acclimating to Columbia, the faculty and the resources. The activities in which the students participate for the residency are critical to their success in the 16-months of remote learning in which they engage. Recordings and other materials are provided to students in continuity with completed activities and the site is also used as a general communications tool with the students outside of the dedicated Canvas courses.

• Department: Insurance Management
• 0 points

UNDERSTAND CONFLICT & COOPERATION

UNDERSTAND CONFL & COOPER

• Department: Negotiation & Conflict Resolution
• W 6:10 p.m.-8 p.m.
• Instructor: Pedro D Franco
  Info
• 3 points

Histology & Pathology

• Department: Dental and Oral Surgery
• 0 points

HISTOLOGY & PATHOLOGY

• Department: Pathology
• Instructor: Lyndsey Adams
  Info
• 0 points

PSYCHIATRIC MEDICINE

• Department: Dental and Oral Surgery
• 0 points

PAINTING II

• Department: Visual Arts
• W 10 a.m.-4 p.m.
• Instructor: Heidi Howard
  Info
• 3 points

Wealth Management Student Community Cent

WMGT Student Community Ce

The Wealth Management Student Community Center helps facilitate remote pre-residency requirements and preparatory activities to preserve the limited in-person time we have during the residency for other activities. Given that we are a remote program, this is the most effective way to introduce, assign, inform and track new student activity prior to starting the core courses. The use of a dedicated site helps them practice using the LMS, in addition to acclimating to Columbia, the faculty and the resources. The activities in which the students participate for the residency are critical to their success in the 16-months of remote learning in which they engage. Recordings and other materials are provided to students in continuity with completed activities and the site is also used as a general communications tool with the students outside of the dedicated Canvas courses.

• Department: Wealth Management
• MTW 8:30 a.m.-5:30 p.m.
• 0 points

SAFETY IN THE CONSTRUCTION INDUSTRY

SAFETY IN THE CONSTR INDU

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• Department: Construction Administration
• T 6:10 p.m.-8 p.m.
• Instructor: Joseph Schwed
  Info
• 3 points

Operations Management

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
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• 1.5 points

Molecular Mechanisms

MOLECULAR MECHANISM HLTH/DISEA

• Department: Dental and Oral Surgery
• Instructor: Jonathan M Barasch
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• 0 points

MOLECULAR MECHANISMS HEALTH & DISEASE

• Department: Pathology
• 0 points

Wealth Management Student Community Cent

WMGT Student Community Ce

The Wealth Management Student Community Center helps facilitate remote pre-residency requirements and preparatory activities to preserve the limited in-person time we have during the residency for other activities. Given that we are a remote program, this is the most effective way to introduce, assign, inform and track student activity prior to starting the core courses. The activities in which the students participate for the residency are critical to their success in the 16-months of remote learning in which they engage. Recordings and other materials are provided to students in continuity with completed activities and the site is also used as a general communications tool with the students outside of the dedicated Canvas courses.

• Department: Wealth Management
• WRF 8:30 a.m.-5:30 p.m.
• Instructor: Vanessa D Carrillo
  Info
• 0 points

LEGAL ISSUES IN BUILDING CONSTRUCTION

• Department: Construction Administration
• W 6:10 p.m.-8 p.m.
• Instructor: Tim Hegarty
  Info
• 3 points

CLINICAL GROSS ANATOMY

• Department: Dental and Oral Surgery
• 0 points