NUMERICAL METHODS IN FINANCE

Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Review of the basic numerical methods for partial differential equations, variational inequalities and free-boundary problems. Numerical methods for solving stochastic differential equations; random number generation, Monte Carlo techniques for evaluating path-integrals, numerical techniques for the valuation of American, path-dependent and barrier options.

• Department: Mathematics
• TR 6:10 p.m.-7:25 p.m.
• 3 points

Experiments

Field experiments have become increasingly important ways of studying the effectiveness of political interventions, be they campaign tactics for mobilizing or persuading voters, fundraising tactics for political or charitable efforts, lobbying, recruiting volunteers, or influencing administrative or judicial outcomes through direct communication.  In this course, we will discuss the logic of experimentation, its strengths and weaknesses compared to other methodologies, and the ways in which experimentation has been -- and could be -- used to investigate political phenomena. We will discuss a wide array of applications.  Students will learn how to interpret and design experiments. In order to better understand the nuances of experimental design and analysis, we will roll up our sleeves and reanalyze some of the data from the weekly readings.

• Department: Political Analytics
• T 6:10 p.m.-8 p.m.
• Instructor: Donald P Green
  CULPA: 2 h-index: 87 Info
• 2 points

ENTREPRENEURSHIP: DEVELOP/IMPLEMENT NEW

This course is designed for students interested in entrepreneurship and becoming CEO/Founders or leaders in industry as innovators and operators. The class is appropriate for those with a strong interest in new ventures or innovation at the corporate level, or for those who want to develop an entrepreneurial mindset even if you have no plans to start a business. This includes potential entrepreneurs, those interested in the financing of new ventures, working in new ventures, or a portfolio company, or in broader general management of entrepreneurial firms. Entrepreneurial topics include: the entrepreneurial journey, founders & co-founders, the art of the pitch, shaping opportunities, traditional business models, business models for the greater good, the lean startup method and the hypothesis-driven approach, technology strategy, product testing, marketing strategy, entrepreneurial marketing, venture financing and emerging developments. Academic readings, analysis of case studies, class discussions, independent exercises, reading assessments, team work, guest speakers, investor panels, weekly deliverable options and a final investor pitch are the main modalities used to help you learn and assist you on your entrepreneurial path. There are no prerequisites for this course.

• Department: Technology Management
• M 4:10 p.m.-6 p.m.
• Instructor: Michael E Jr McGuire
  Info
• 3 points

Genetics

Prerequisites: BIOL UN2005 and BIOL UN2006 or the equivalent. General genetics course focused on basic principles of transmission genetics and the application of genetic approaches to the study of biological function. Principles will be illustrated using classical and contemporary examples from prokaryote and eukaryote organisms, and the experimental discoveries at their foundation will be featured. Applications will include genetic approaches to studying animal development and human diseases.  All students must get permission from the instructor to be added from the waitlist.

• Department: Biological Sciences
• TR 10:10 a.m.-11:25 a.m.
• 3 points

Disaster Research Methods

The field of disaster research is relatively new in the United States, as a specific field of study, with the first disaster research center being founded in the early 1960s. The field itself is now highly multi-disciplinary, drawing from the social sciences, anthropology, political science, computer science, engineering, earth sciences, psychology, and medicine and public health. These academic fields have intersected with the practice community by informing holistic emergency planning for all members of a community. Furthermore, these research outputs have informed federal and state policy, the private sector, and community organizations to inform program design and implementation. Translating research into practice remains a constant challenge in this rapidly evolving field. The methodological approaches to disaster research are just as diverse and have become increasingly complex with the advent of big data, the ubiquity of spatial information, and novel cross-disciplinary research. As a new era of compound and cascading disasters has triggered a constant “response” mode within the field of emergency management, the need for practitioners and research with a fluency in research and evaluation methods is required to critically evaluate or generate high quality and ethically based research.

• Department: Climate School
• T 10:10 a.m.-12:40 p.m.
• Instructor: Jonathan J Sury
  Info
• 3 points

DECISION MODELS & MANAGEMENT

DECISION MODELS & MGMT

This course provides an introduction to computer-based models for decision-making.  The emphasis is on models that are widely used in diverse industries and functional areas, including finance, accounting, operations, and marketing.  Applications will include advertising planning, revenue management, asset-liability management, environmental policy modeling, portfolio optimization, and corporate risk management, among others. The applicability and usage of computer-based models have increased dramatically in recent years, due to the extraordinary improvements in computer, information and communication technologies, including not just hardware but also model-solution techniques and user interfaces.  Twenty years ago working with a model meant using an expensive mainframe computer, learning a complex programming language, and struggling to compile data by hand; the entire process was clearly marked “experts only.”  The rise of personal computers, friendly interfaces (such as spreadsheets), and large databases has made modeling far more accessible to managers.  Information has come to be recognized as a critical resource, and models play a key role in deploying this resource, in organizing and structuring information so that it can be used productively.

• Department: Sustainability Management
• M 6:10 p.m.-8 p.m.
• Instructor: Lucius J Riccio
  CULPA: 4 Info
• 3 points

GHG EMISSIONS: MEASURING AND MINIMIZING

GHG EMISSIONS CARBON FOOT

Global greenhouse gas (GHG) emissions are now at a record high, and the world’s scientific community agrees that continued unabated release of greenhouse gases will have catastrophic consequences. Many efforts to curb greenhouse gas emissions, both public and private, have been underway for decades, yet it is now clear that collectively these efforts are failing, and that far more concerted efforts are necessary. In December 2015, the world’s nations agreed in Paris to take actions to limit the future increase in global temperatures well below to 2°C, while pursuing efforts to limit the temperature increase even further to 1.5°C. Achieving this goal will require mitigation of greenhouse gas emissions from all sectors, both public and private. Critical to any attempt to mitigate greenhouse gas emissions is a clear, accurate understanding of the sources and levels of greenhouse gas emissions. This course will address all facets of greenhouse gas emissions accounting and reporting and will provide students with tangible skills needed to direct such efforts in the future.

Students in this course will gain hands-on experience designing and executing greenhouse gas emissions inventories for companies, financial institutions and governments employing all necessary skills including the identification of analysis boundaries,  data collection, calculation of emissions levels, and reporting of results. In-class workshops and exercises will complement papers and group assignments. A key component of this course will be critical evaluation of both existing accounting and reporting standards as well as GHG emissions reduction target setting practices.

This course will introduce many of the challenges facing carbon accounting practitioners and will require students to recommend solutions to these challenges derived through critical analysis. Classes will examine current examples of greenhouse gas reporting efforts and will allow students the opportunity to recommend improved calculation and reporting methods.

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• 3 points

Climate Risk Assessment

This graduate-level course surveys the many components that go into climate risk assessment, with an emphasis on climate impacts and vulnerability.  We will survey the latest research on climate hazards, with an emphasis on the types of extreme weather events that have the largest societal impacts.  We will then explore these impacts in detail, by sector and system.  We will next investigate determinants of vulnerability, and how vulnerability magnifies climate impacts.  We then query how climate solutions can be integrated into risk assessments in a recursive manner. Throughout the course, we will strike a balance between foundational (‘IPCC-type’) examples on the one hand, and emerging topics like compound extreme events and existential risk on the other.  Throughout the course we will study and employ a number of risk assessment methodologies, based on case studies from within the private, public, and non-profit sectors.

• Department: Climate School
• T 10:10 a.m.-12:40 p.m.
• Instructor: Radley M Horton
  Info
• 3 points

Extreme Weather Events and Climate Chang

Extreme Weather and Clima

This seminar class will give an overview of the current knowledge of extreme weather events and the impact of anthropogenic climate change on their characteristics. We will start the course by defining extreme weather events and the current state-of-the-art knowledge of how anthropogenic activities influence them, including trends, detection and attribution, as well as future projections. We will discuss the methods typically used for analyzing extreme events and what are the existing uncertainties. The existing warning systems and forecasts for extreme events will be briefly discussed, including their communication and impacts and possible mitigation measures.

• Department: Climate School
• R 1:10 p.m.-3:40 p.m.
• Instructor: Suzana J de Camargo
  Info
• 3 points

Advanced Biodiversity Analysis I

Adv Biodiversity Analysis

This course focuses on understanding the concepts of science-based biodiversity metrics and demonstrates the application of advanced methods in deriving robust and data-backed metrics for populations and species. This course introduces students to the field of population ecology and the use of data in global, national, and regional policies responsible for the conservation of biodiversity. Students will learn how to use various statistical packages in program R to analyze biodiversity data for estimating metrics such as habitat occupancy, abundance and density trends through time while considering the effects of human actions. Species are the “currency” in most biodiversity-related policies and regulations. As such, students will also examine how species distributions can shift due to climate change and evaluate the use of species-level metrics, including extinction risk and genetic markers, for supporting sustainability and conservation goals. This course will provide students with a strong foundation of statistical models commonly used in biodiversity conservation and the use of such models for deriving metrics and indicators to support decision-making in terrestrial and marine environments.

• Department: Biodiversity Data Analytics
• W 6:10 p.m.-8 p.m.
• Instructor: Viorel Popescu
  Info
• 3 points

SECURITY ANALYSIS

Prerequisites: BUSI PS5001 Intro to Finance and BUSI PS5003 Corporate Finance or Professor Approval required. If you have not taken PS5001 or PS5003 at Columbia University, please contact the course instructor for approval. Students will learn about the valuation of publicly traded equity securities. By the end of the semester students will be able to perform fundamental analysis (bottoms-up, firm-level, business and financial analysis), prepare pro forma financial statements, estimate free cash flows and apply valuation models.

• Department: Enterprise Risk Management
• T 6:10 p.m.-8 p.m.
• Instructor: Hany S Guirguis
  CULPA: 3 Info
• 3 points

ESG ALIGNED CORPORATE GOVERNANCE

ESG ALIGNED CORP GOVERNAN

Environmental, social and governance issues (‘ESG’) are moving to center stage for corporate boards and executive teams.  This elective course complements management and operations courses by focusing on the perspective and roles of the board and C-suite of corporations, financial institutions and professional firms in addressing ESG risks as well as promoting and overseeing governance aligned with ESG principles. The course focuses on the interchange between the external legal, competitive, societal, environmental and policy ‘ecosystems’ corporations face (which vary around the world) and a company’s internal structure, operations and pressures. We will use the United Nations Guiding Principles on Business and Human Rights and the UN Global Compact Principles (which incorporate all aspects of ESG) as the central frameworks to explore the concept of a corporation’s responsibility to respect and remedy human rights and environmental harms.  We will also examine the Equator Principles and other frameworks that spell out good practices for project finance and other investment decisions, and reference a wide range of the myriad indices, supplier disclosure portals and benchmarks that exist in this inter-disciplinary field.  Relevant regulations, corporate law regimes and court cases will be discussed from the point of view of what business managers need to know. While most of the course will deal with companies and firms serving global, regional or national markets, several examples will deal with the question of how the ESG ecosystem affects or offers opportunities to start-ups.

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• 3 points

Survey & Polling Methodology

Survey research has played a pivotal role in politics for the better part of the last century, with a wide range of campaign and public policy professionals conducting surveys to gain insight into the thoughts, feelings, and opinions of the electorate and citizenry as a whole. Since the early 2000s, the use of survey experiments has become exponentially more prevalent in the political realm as a way to assess attitudes, anticipate reactions, or measure causal relationships. Recent trends point to the growing importance of the internet and social media to conduct surveys and the linkage of survey data with the wealth of publicly available personal information as well as with information on individuals’ social and economic behavior. In this course, students will learn about the strengths and weaknesses of survey research as well as limitations associated with survey design and various analytical techniques, and they will acquire concrete knowledge of practical tools used in campaigns, advocacy, and election forecasting. Students will be introduced to a set of principles for conducting survey research and analyzing survey data that are the basis for standard practice in the field. Students will be familiarized with terminology and concepts associated with survey questionnaire design, sampling, data collection and aggregation, and survey data analysis to gain insights and to test hypotheses about the nature of human and social behavior and interaction. The course will present a framework that will enable students to evaluate the influence of different sources of error on the quality of data.

• Department: Political Analytics
• R 6:10 p.m.-8 p.m.
• Instructor: Anja Kilibarda
  h-index: 3 Info
• 4 points

Spatial Analysis of Food Security

Spatial Analysis Food Sec

Globally, over 2 billion people are suffering from moderate-to-severe food insecurity and the Sudan famine being the first famine declared in 2025 since 2020. One key aspect to understanding food insecurity is its spatial distribution and trends that contribute to how food secure a population is. This course will teach students how to collect and analyze spatial data related to food security, as well as touch on important topics in food insecurity. The course will focus on taking real-life food security questions and applying spatial analysis techniques to these questions. In the course, we will cover an introduction to spatial analysis, natural experiments in geography, applying remote sensing to food insecurity, climate shocks and food security, and seasonal forecasting and food security.  

This class will have a lecture aspect, which will mainly focus on topics in food security and how they relate to data collection, and a lab section, which will be an opportunity for students to collect data directly, clean the data, and analyze the data using the R programming language with spatial research methods. Example topics in class will be climate variability and food insecurity, policies that can successfully address food insecurity, and understanding spatial aggregation in food security statistics and how that can influence interpretation. These topics will then be further explored in the lab section of the class: specifically focusing on downloading weather data for time series analysis, using a convergence of datasets to map hotspots, and investigating how survey data intersects with spatial datasets.

• Department: Climate School
• R 10:10 a.m.-12:40 p.m.
• Instructor: Sarah L Blakeley
  Info
• 3 points

INDUSTRY INSIDER

This course gives students visibility into the rapidly changing communication industry and the wide range of careers available. Curated site visits take us inside world-class agencies and corporate/nonprofit organizations to see how they use strategic communication in the real world. Students gain firsthand exposure to leading practitioners while learning the dynamics of collaboration between internal and external stakeholders. Relevant coursework provides additional perspective.

• Department: Strategic Communication
• R 4:10 p.m.-6 p.m.
• Instructor: Juli Falkoff
  Info
• Instructor: Andrea Legnani
  Info
• 3 points

ILLNESS-THOUGHT-ACTIVISM

ILLNESS THOUGHT ACTIVISM

• Department: Narrative Medicine
• W 6:10 p.m.-8 p.m.
• Instructor: Richard Slusarczyk
  Info
• 1 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

GEO INFO SYSTEMS (GIS) FOR SUST SCIENCE

GEO INFO SYS FOR SUST SCIENCE

Many environmental and sustainability science issues have a spatial, location-based component. Increasingly available spatial data allow location-specific analysis and solutions to problems and understanding issues. As result, analyzing and identifying successful and sustainable solutions for these issues often requires the use of spatial analysis and tools. This course introduces common spatial data types and fundamental methods to organize, visualize and analyze those data using Geographic Information Systems (GIS). Through a combination of lectures and practical computer activities the students will learn and practice fundamental GIS and spatial analysis methods using typical sustainable science case studies and scenarios. A key objective of this course is to provide students with essential GIS skills that will aid them in their professional career and to offer an overview of current GIS applications. In the first part, the course will cover basic spatial data types and GIS concepts. The students will apply those techniques by analyzing potential impacts of storms on New York City as part of a guided case study. A mid-term report describing this case study and the results is required. In the second part, building on the basic concepts introduced in the first part, students will be asked to identify a sustainable science question of their choice that they would like to address as a final project. Together with the instructor they will be developing a strategy of analyzing and presenting related spatial data. While the students are working on their projects additional GIS method and spatial analysis concepts will be covered in class. At the end of the course Students will briefly present their final project and submit a paper describing their project. This course does not assume that students have had any previous experience with GIS.

• Department: Sustainability Science
• M 4:10 p.m.-6 p.m.
• Instructor: Frank Nitsche
  CULPA: 1 h-index: 29 Info
• 3 points

MATH FINANCE PRACTITIONERS SEMINAR II

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
• R 7:40 p.m.-8:55 p.m.
• Instructor: Jaehyuk Choi
  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

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 4:10 p.m.-6 p.m.
• R 12:10 p.m.-2 p.m.
• Instructor: Nicole C Dicrecchio
  Info
• Instructor: Michael R Haupt
  Info
• 3 points

METHODS WORKSHOP

Introducing students to a series of methods, methodological discussions, and questions relevant to the focus of the Masters program: urban sociology and the public interest. Three methodological perspectives will frame discussions: analytical sociology, small-n methods, and actor-network theory.

• Department: Sociology
• R 10:10 a.m.-noon
• Instructor: Yao Lu
  h-index: 25 Info
• 4 points

Computing and Research Methods for Clima

Nearly all subdisciplines in climate now rely on aspects of data science to understand problems and evaluate solutions. Climate science is generating “big data” including petabytes of observations from sources such as NASA and even more from climate models. With this big, complex data burden comes the need for advanced data science techniques such that scientists can better understand our climate system and explore possible solutions to the challenge of climate change. 

This course is a broad introduction to research computing and data science drawing from examples in climate science. Students will learn how to work with, analyze, and visualize big datasets. The course will utilize the scientific python ecosystem, Unix/Linux, shell scripting, terminal commands, git/github, and other techniques.

• Department: Climate School
• M 1:10 p.m.-3:40 p.m.
• Instructor: Daniel Westervelt
  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
• R 4:10 p.m.-6 p.m.
• W 4:10 p.m.-6 p.m.
• Instructor: Nicole C Dicrecchio
  Info
• Instructor: Benjamin Kinsella
  Info
• 3 points

Sovereign Finance, Climate and Developme

Sovereign Finc, Climate,

The ability of many emerging market and developing economy (EMDE) countries to meet their climate financing objectives depends to a large extent on their cost of capital, which in turn is impacted by the actual and perceived risks of their sovereign debt.  According to the Jubilee Report published in June 2025 by the Pontifical Academy of Social Sciences and the Columbia University Initiative for Policy Dialogue, 54 countries spend over ten percent of their revenues on interest payments, and 3.3 billion people live in countries that spend more on interest than healthcare.  EMDE countries need workable tools to reduce financing costs and manage sovereign debt in order to fund their development needs and to achieve the climate objectives set out in their nationally determined contributions under the Paris Agreement.  

This course will explore the roots of the problem, including the pro-cyclical nature of sovereign financing and refinancing; the role of international financial institutions and credit rating agencies; and the main obstacles to resolving sovereign debt distress in an orderly and timely way, contrasting resolution tools for sovereign debt with those available in the private sector.  Drawing on real-world case studies, we will look at structural innovations that could help debottleneck climate finance, and how effective they can be.  We will consider several prominent proposals to reorient the global financial architecture, focusing on whether and to what extent they can reduce EMDE funding costs and unlock financing for sustainable development and climate investment.

• Department: Climate School
• F 1:10 p.m.-3:40 p.m.
• Instructor: Andrew A Bernstein
  Info
• 1.5 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

Climate in Practice: Leadership and Coll

Climate in Practice-Leade

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.
• 1.5 points

Climate in Practice: Leadership and Coll

Climate in Practice-Leade

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: Sandra Goldmark
  CULPA: 4 Info
• Instructor: Andrew J Kruczkiewicz
  Info
• Instructor: Radley M Horton
  Info
• Instructor: Marcia N Macedo
  Info
• 1.5 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.
• T 4:10 p.m.-6 p.m.
• Instructor: Charles R Riemann
  Info
• Instructor: Gregory A Kossinets
  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.
• T 4:10 p.m.-6 p.m.
• Instructor: Charles R Riemann
  Info
• Instructor: Gregory A Kossinets
  Info
• 3 points

Water Governance

Water is widely recognized as the most essential natural resource for both society and Earth’s ecosystems. Yet the relationship between society and water is complex. While water is critical for livelihoods, it is also frequently a hazard. Floods, droughts, and contaminated water are formidable threats to human well-being. To deal with this dual nature of water, people have long modified the water cycle through engineering schemes like dams, reservoirs, irrigation systems, and interbasin transfer systems as well as through land use and land-cover change.

We need more than just technical solutions. Society needs a clear and robust plan to manage and govern water given its intertwined relationship with this critical resource. In Water Governance, we will explore the political, social, economic, and administrative systems that affect the use, development, and management of water resources. You will be introduced to current themes that influence water governance including sustainable development, integrated water resource management, water rights and pricing, corruption, and equity for marginal groups. These themes will be explored at the local, national, and international levels to provide you with a broad understanding of water governance issues.

• Department: Climate School
• R 1:10 p.m.-3:40 p.m.
• Instructor: Michael J Puma
  h-index: 23 Info
• 3 points

Insurance for Climate Change in the Glob

Climate change is overwhelming the world’s capacity to manage climate risk. Index insurance and other financial risk transfer tools, such as derivatives, cat-bonds, and humanitarian disaster funds are being increasingly looked to as solutions to address year to year climate risk to support climate adaptation. However, success is mixed, with experiences of inadequate performance, opaqueness, exploitation, incentivization of mal-adaptation, and approaches that undermine sovereignty and governance. Co-generation and governance tools are not yet developed enough to assure that those who the financing is intended to benefit have an adequate voice in design, or understand critical limitations. There are no easy solutions, with many difficult trade-offs, unsolved problems, and justice challenges. 

In the class, students work to understand the relationship between academic research and project level challenges. The course will be an insider view of practical real world case studies. Students will use real world project materials, data, and software tools to work through the evolution of thought over time, exploring issues such as incentivization of adaptation through community driven strategies, avoiding potential pitfalls, and responsibly implementing financial solutions for large numbers of low-income people. Students will use interactive software tools, quantitative games, and project workflows to analyze case studies and cumulatively develop their own real world case study, guided by academic debates and their own insights to build their own interactive tools and finance solutions.

• Department: Climate School
• W 1:10 p.m.-3:40 p.m.
• Instructor: Daniel Osgood
  h-index: 23 Info
• 3 points

SOCIAL NETWORK ANALYSIS

The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time.

• Department: Quantitative Methods/Social Sciences
• F 12:10 p.m.-2 p.m.
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 3 points

DATA VISUALIZATION

This course is designed to the interdisciplinary and emerging field of data science. It will cover techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data. Students will be required to complete several scripting, data analysis and visualization design assignments as well as a final project. Topics include: data and image models, social and interactive visualizations, principles and designs, perception and attention, mapping and cartography, network visualization. Computational methods are emphasized and students will be expected to program in R, Javascript, D3, HTML and CSS and will be expected to submit and peer review work through Github. Students will be expected to write up the results of the project in the form of a conference paper submission.

• Department: Quantitative Methods/Social Sciences
• W 6:10 p.m.-8 p.m.
• 3 points

BAYESIAN STATS FOR THE SOC SCI

An introduction to Bayesian statistical methods with applications to the social sciences. Considerable emphasis will be placed on regression modeling and model checking. The primary software used will be Stan, which students do not need to be familiar with in advance. Students in the course will access the Stan library via R, so some experience with R is necessary. Any QMSS student is presumed to have sufficient background. Any non-QMSS students interested in taking this course should have a comparable background to a QMSS student in basic probability. Topics to be covered are a review of calculus and probability, Bayesian principles, prediction and model checking, linear regression models, Bayesian calculations with Stan, hierarchical linear models, nonlinear regression models, missing data, and decision theory.

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

FIELD WORK II

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

NATURAL LANG PROCESSING SOCIAL SCIENCES

NATURAL LANG PROC SOC SCI

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
• R 2:10 p.m.-4 p.m.
• Instructor: Samira Zaroudi
  Info
• 3 points

THESIS SEMINAR II

• 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

APPLIED DATA SCI FOR SOC SCIENTISTS

• Department: Quantitative Methods/Social Sciences
• M 6:10 p.m.-8 p.m.
• 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

Machine Learning in Sustainability Scien

This course is for students who wish to participate in a hands-on, project-based, exploration of how machine learning (ML) can be harnessed to tackle challenges in sustainability science.  Students will work collaboratively on a semester-long coding project, applying ML techniques to real sustainability problems in data analysis and visualization. Along the way, they will sharpen their skills in AI-assisted coding with tools such as ChatGPT, using these technologies not just to write and debug code more efficiently, but also to enhance creativity, streamline collaboration, and bring complex ideas to life. By the end of the course, students will have experienced the full arc of designing, building, and refining an ML solution with direct relevance to the future of our planet.

This course equips students with experience using advanced scientific and computational tools for tackling environmental and sustainability challenges. Through AI-assisted coding of ML solutions, students develop creative approaches to analyzing, modeling, and interpreting state-of-the-art observations of Earth systems using rigorous, quantitative methods. Guided readings and discussion will help students achieve mastery of the subject. The class takes place in a project-based setting where students collaborate on a semester-long project, requiring them to plan together, manage tasks, and integrate diverse skills in applying scientific principles to real-world problems. In doing so, the course not only deepens technical expertise but also strengthens students’ ability to provide data-driven insights that inform sustainability decision-making.

• Department: Sustainability Science
• R 6:10 p.m.-8 p.m.
• Instructor: James L Davis
  h-index: 57 Info
• 3-4 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
• T 12:10 p.m.-2 p.m.
• 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.
• M 4:10 p.m.-6 p.m.
• Instructor: Mazen Asaad
  Info
• Instructor: Haiyuan Wang
  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 4:10 p.m.-6 p.m.
• Instructor: Alexander Peterhansl
  Info
• 3 points

Climate and Energy Solutions

Key scientific concepts related to sustainable fuels, energy carriers, emissions management technologies, and the role of subsurface resources in meeting our energy needs in a sustainable manner are discussed. Students will apply the fundamentals of material and energy conversions and recovery to better establish the technological basis of harnessing natural resources in a responsible manner. Students will also evaluate the impacts of existing and emerging energy technologies on the environment.

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

MONITORING AND ANALYSIS OF COASTAL MARIN

MARINE & ESTUARY SYSTEMS

From a global perspective, many of the earth’s most important environments and resources for global sustainability are located in marine and estuarine areas.  This class will explore open-ocean and estuarine processes, reviewing what is known about the temporal variability and interconnectedness of these physical and biologic systems.. A few examples include; 1.) oceanic environments were incompletely understood processes regulate the exchange of heat, water and carbon dioxide gas with the atmosphere, 2.) the relationship between nutrients and primary production and fisheries in open ocean, estuarine and coral reef environments and climatic phenomenon such as El Niño South Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). 3.) For estuaries, current sea level and urbanization stresses on the coastal environments will be discussed.  Professionals working in the environmental and engineering fields will benefit from a wide-ranging discussion of the multi-scaled processes influencing estuaries. Knowledge of the processes operating in these environments will lead to a more thorough understanding of the complex issues that may influence infrastructure and coastal development in and around estuarine environments in the near-future. Throughout the class we will explore marine and estuarine processes by studying regional and local responses to broader scale climatic forcing. Reading of textbook chapters and journal articles will supplement in-class lectures and discussion. Grading will be based on class participation, a two exams and a research paper. At the end of the course, students will have a strong scientific understanding about the impacts made on marine and estuary systems through physical, chemical, and biological processes. The course will prepare students to be well-trained in the core features of these systems and the relationship between natural and human processes, and equip them with the skills needed to explore marine and estuary systems in diverse scales and functions in the future.

• Department: Sustainability Science
• W 6:10 p.m.-8 p.m.
• Instructor: Braddock Linsley
  h-index: 42 Info
• 3 points

Quantum Simulation and Computing Lab

Quantum Simul. & Computin

The “Quantum Simulation and Computing Lab” will give students in the Quantum Masters program hands-on experience in quantum optics, quantum simulation and quantum computing. The course combines lectures, tutorials, and two lab sections. In one lab section, students will do experiments with entangled photons. In the second lab section, students will program quantum computers and run algorithms on them using the IBM Qiskit platform.

The course starts with a recap of linear algebra and quantum mechanics, followed by an introduction to quantum optics and quantum information. Two-level systems, Bloch sphere, quantum gates, and elementary quantum algorithms will be discussed. Quantum teleportation and quantum key distribution will be introduced as applications of entanglement. The lecture content will be directly applied in experiments with entangled photons. In the following, state-of-the-art quantum algorithms will be discussed, related to cutting-edge research results in quantum computing. This includes quantum Fourier transform, quantum simulation of the Schroedinger equation, and the variational quantum eigensolver (VQE) algorithm. During the course students will do one experimental project with entangled photons and one quantum programming project. Students will be guided to implement a quantum algorithm of their choice and run it on a quantum computer (IBM, IonQ, QuEra).

• Department: Physics
• WF 1:10 p.m.-2:25 p.m.
• Instructor: Zhenjie Yan
  Info
• 3 points

Information for Strategic Climate and Di

Climate/Disaster Communic

Better climate communications have become a priority across sectors as the impacts of climate change continue to shift daily lives for individuals, communities, and organizations. Whether it be exploring more effective ways to communicate science, navigating difficult conversations stemming from different viewpoints, or connecting more meaningfully with individuals and groups in a changing world, at the center of climate communication is the convergence of science and the dynamics of human behavior. This course invites participants to explore how to apply research from psychology, adult learning theory, cognitive science, and social neuroscience to improve climate change communications and support meaningful change in their specific contexts. 

This course is designed for students who are, or will be, tasked with communicating climate change–related information in a range of settings, from professional and policy environments to community engagement, education, and everyday encounters. Every context and audience is unique; for that reason, the course emphasizes tailoring climate communication strategies to specific audiences, institutional settings, and decision-making environments. Using an applied, action-learning approach, the course follows a structured developmental arc in which students move from analyzing climate communication challenges, to modeling cognitive and systemic dynamics, to developing and refining a practicum proposal, and ultimately to designing a final applied climate communication strategy. 

The course combines short lectures, guest speakers from the field, and applied, in-class activities focused on mental modeling, systems thinking, and decision-making under uncertainty. Students will complete a practicum project that allows them to generate localized, evidence-informed communication strategies addressing an ongoing or emerging climate communication challenge relevant to their work, research, or community context.

• Department: Climate School
• T 1:10 p.m.-3:40 p.m.
• Instructor: Joshua Devincenzo
  h-index: 1 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 10:10 a.m.-noon
• M 6:10 p.m.-8 p.m.
• Instructor: Mariana Y Pinos
  Info
• Instructor: Lilian Ore
  Info
• 3 points

FUNDAMENTLS OF OMBUDS PRACTICE

This is a practice-based course designed to introduce students to ombuds work, and to help students develop the skills, knowledge, and attitudes required to succeed in the role of an organizational ombuds.  Underlying theory will be explored in the context of practice. This course will emphasize the Standards of Practice and Code of Ethics put forth by the International Ombudsman Association (IOA).  Upon successful completion of the course, students will be fundamentally prepared to apply for entry-level positions in the field of organizational ombuds and should have the requisite knowledge base to sit for the IOA’s Certified Organizational Ombuds Practitioner exam.

Note for NECR Students
: This course is offered as an elective in the Negotiation and Conflict Resolution (NECR) program. The course builds upon students’ negotiation and mediation skills and self-awareness as a practitioner.  It is highly recommended that students complete PS5105, PS5107 and PS5124 prior to enrolling in this course.

• Department: Negotiation & Conflict Resolution
• R 6:10 p.m.-8 p.m.
• Instructor: Amy E Sereday
  Info
• 3 points

APPLIED ANALYTICS IN ORGANIZATIONAL CONT

APP AN IN ORG CONTEXT

• Department: Applied Analytics
• M 6:10 p.m.-8 p.m.
• Instructor: Panagiotis Tarhanidis
  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
• T 2:10 p.m.-4 p.m.
• M 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Sim Segal
  Info
• Instructor: Duncan Campbell
  Info
• Instructor: Aristargos Christodoulou
  Info
• Instructor: James S Kim
  Info
• Instructor: Michael Leibrock
  Info
• Instructor: Benjamin M Perlman
  Info
• Instructor: Qingji Yang
  Info
• Instructor: Yi Zuo
  Info
• 3 points

SELF & OTHER IN CLIN ENCOUNTER

TBD

• Department: Narrative Medicine
• M 5:10 p.m.-8 p.m.
• Instructor: Michael E Davidovits
  Info
• 4 points

NONPROFIT DEVELOPMENT, FUNDRAISING, AND

NP DEVELOPMENT & FUNDRAIS

This course provides a comprehensive overview of the design, implementation and management of the components of a philanthropic program and its relationship to the financial sustainability of the nonprofit organization. It introduces the philosophical, ethical and historical underpinnings of fundraising practice, also providing the nomenclature, characteristics, and methods of gift generation and their sources, and the management and stewardship of those sources. Additionally, it examines the relationship of the organization’s mission to its strategic vision and the planning, management and impact of fundraising to the organization’s advancement and sustainability.

• Department: Non Profit Management
• M 6:10 p.m.-8 p.m.
• M 6:30 p.m.-8 p.m.
• 3 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
• M 6:10 p.m.-8 p.m.
• T 4:10 p.m.-6 p.m.
• Instructor: Howard N Apsan
  CULPA: 1 Info
• 3 points

PAINTING I

• Department: Visual Arts
• R 10 a.m.-4 p.m.
• Instructor: Naomi Safran-Hon
  Info
• 3 points

Business Analytics and Artificial Intell

Business Analytics and AI

• Department: Decision, Risk and Operations
• Instructor: Thomas Vladeck
  Info
• Instructor: Benjamin J Tubiana
  Info
• 1.5 points

Operations Management

• Department: Decision, Risk and Operations
• Instructor: Hongyao Ma
  h-index: 7 Info
• 1.5 points

Operations Management

• Department: Decision, Risk and Operations
• Instructor: Hongyao Ma
  h-index: 7 Info
• 1.5 points

PAINTING III

• Department: Visual Arts
• M 10 a.m.-4 p.m.
• 3 points

Project Management Fundamentals: Analyti

PMGT FUNDAMENTALS: ANALYT

This core course introduces students to the quantitative, and analytical foundations of project management, with emphasis on the application of mathematical, and systems-based methods to the planning, execution, and control of complex projects. The course is designed for projects in quantitatively intensive environments such as engineering, technology, construction, consulting, and related sectors where structured planning, measurement, and optimization are essential.

Students develop practical skills in project selection and initiation, scope definition, network-based scheduling, resource allocation, cost and budget modeling, and performance measurement using established analytical techniques. Core topics include work breakdown structures, logic networks, critical path and PERT analysis, resource loading and leveling, financial and life-cycle cost analysis, earned value management, and probabilistic risk assessment, including Monte Carlo–based uncertainty analysis.

Through computational exercises, analytical simulations, and applied projects, students learn to model constraints, evaluate trade-offs, and interpret performance data to support decision-making throughout the project life cycle. By emphasizing structured methodologies and analytical rigor, the course prepares students to manage technically complex, data-rich projects across a range of industries.

• Department: Project Management
• W 4:10 p.m.-6 p.m.
• Instructor: Evangelia Ieronymaki
  Info
• 3 points

NEGOTIATION & DISPUTE RESOLUTION IN SPRT

• Department: Sports Management
• MTWRF 9 a.m.-5 p.m.
• Instructor: Scott Rosner
  Info
• 3 points

FOUNDATIONS OF CLINICAL MEDICINE I

• Department: Medicine
• 0 points

FOUNDATIONS OF CLINICAL MEDICINE II

FOUNDATIONS OF CLINICAL MED II

• Department: Medicine
• 0 points

THE BODY IN HEALTH & DISEASE I

• Department: Medicine
• 0 points

Negotiation for Everyday Use

In this course, we will explore negotiation from several points of view and approaches. We will also look at characteristics that impact the quality of our negotiations and the outcomes, such as the role of emotions, cultural considerations, effectiveness of our communication, and opportunities to seek out negotiation to transform relationships. The course will be a blend of concepts and skills, theory and practice. On some occasions, you will be introduced to a concept and then asked to apply those concepts in an experiential activity. At other times, you will be asked to engage the activity or simulation and then the concepts will be elicited based on your experience. You will have several opportunities to practice developing your skills throughout the course, in terms of enhancing your practice and honing your analytical and conceptual understanding.

• Department: Negotiation & Conflict Resolution
• W 6:10 p.m.-8 p.m.
• Instructor: Martin E Epstein
  Info
• 3 points

PRACTICUM: FACILITATING NARRATIVE MEDICI

PRACTICUM: FACILITATING W

TBA

• Department: Narrative Medicine
• R 2:10 p.m.-4 p.m.
• 3 points

Project Planning, Scheduling and Control

PRJCT PLANNING, SCHED, CN

This course provides graduate students with an in-depth exploration of project controls and schedule-based decision support with the project schedule treated as a management and control model rather than as a technical output. The course emphasizes deterministic and analytical modeling methods, including Critical Path Method (CPM), network-based optimization, schedule performance metrics, earned value analytics, and quantitative forecasting techniques, positioning the project schedule as a formal analytical model rather than a descriptive artifact. Primavera is used as an implementation and analysis tool to support schedule development, updating, performance measurement, and forecasting across diverse industries. The course equips students with the analytical, technical, and control-oriented skills, methodologies, and frameworks needed to manage projects effectively, ensuring completion on time, within budget, and according to specifications. Through a combination of standards-based analysis, software application, case studies, and practical assignments, students will develop the ability to build, baseline, update, interpret, and communicate schedules to monitor performance through KPIs, evaluate progress, and make informed decisions across the project lifecycle. Throughout the course, students are required to interpret what the schedule communicates to management, including the meaning of variances, implications of forecasts, and recommended corrective actions, rather than solely producing technical files or reports.

Designed for students pursuing advanced studies in project management, engineering management, construction management, and related professional tracks, this course is particularly valuable for those seeking careers that require deep expertise in planning, scheduling, and project controls. Emphasis is placed on developing mastery of schedule development, critical path analysis, resource and cost loading, progress measurement, forecasting, and schedule risk and change analysis as a component of an integrated project controls function, using industry-standard tools and methods. Building on foundational project management concepts, the course provides specialized training in schedule-centric project controls and equips students with the analytical, technical, and communication skills needed to design, manage, analyze, and govern complex project schedules as decision-support instruments across diverse industries.

This course prepares students for professional roles such as project cont

• Department: Project Management
• M 4:10 p.m.-6 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Sadia I Janjua
  Info
• Instructor: Muriel Rachid Campomizzi
  Info
• 3 points

Technology Integration in Project Manage

TECH INTEGRATION IN PMGT

Technology Integration in Project Management
explores the real-world adoption of digital transformation within complex project environments, moving beyond theoretical frameworks to provide a fully immersive and experiential learning environment. The course is uniquely structured as
Scrum sprints
, where students operate within standard agile events, roles, and artifacts to analyze and compare industry-leading Project Management Information Systems (PMIS). We will also explore waterfall and/or hybrid approaches throughout the course. Students will gain hands-on proficiency in platforms such as
Jira, Asana, Smartsheet, and Trello
, while simultaneously integrating data analytics tools like
Power BI
to monitor performance and enhance decision-making accuracy in Project, Program and Portfolio Management.

As an elective within the Master of Science in Project Management program, this course is designed for practitioners who wish to lead digitally driven organizations with efficiency and innovation. It serves a critical programmatic goal by bridging the gap between foundational project management methodologies and the high-tech execution required in the modern workforce. Another distinctive feature of this course is the mandatory and continuous evaluation of
Artificial Intelligence (AI) technologies
; students are required to leverage generative AI and automation tools to streamline project delivery, simulating the real-world evolution of value delivery.

The course is a three-credit, full-semester elective delivered in an
in-person modality
. While tailored for Project Management students, it is open to cross-registrants from other Columbia University graduate programs, provided space is available. There are no formal prerequisites; however, students are expected to have a basic understanding of project lifecycles and a willingness to engage in an open and collaborative environment where rapid and iterative technical learning occurs. All technology platforms required for the sprint cycles will be accessible through university licenses or open-access academic versions.

• Department: Project Management
• T 6:10 p.m.-8 p.m.
• R 4:10 p.m.-6 p.m.
• Instructor: Karolina Athanasopoulou
  Info
• Instructor: Stephen Cognetta
  Info
• 3 points

SCREENWRITING II

• Department: Film
• M 6 p.m.-9 p.m.
• W 9:30 a.m.-12:30 p.m.
• R 10 a.m.-1 p.m.
• Instructor: Keith Bunin
  Info
• Instructor: Constance Tsang
  Info
• Instructor: Jordana A Spiro
  Info
• 3 points