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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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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.
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.
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.
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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.
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.
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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.
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.
Organizations have adopted formal approaches, such as establishing grievance committees and ombudsman's offices, to mediate conflicts in the workplace setting and, on occasion, hire outside, independent mediators to handle escalated conflicts and public-facing disputes. Attempts to mediate conflict, though, are much more widespread in organizations than these formal approaches would suggest and are often undertaken by professionals from a wide variety of disciplines who have no formal training in mediation. Increasingly, such professionals are tasked to manage conflicts — whether their field is finance, marketing, social media or human resources — and are evaluated in performance reviews on their ability to perform this task. Professionals today must be prepared to acquire the knowledge and skills of a mediator to meet the expectations of the organization.
With that end in mind, this course is designed for professionals who find themselves frequently having to intervene in conflicts between or among others. Although these professionals may choose to become full-time mediators, they are more likely to use mediation principles and techniques as additional tools to help them within their chosen fields of work, be it in public policy, social media, human resources, international development, peace-building or law. This course will aid professionals who wish to become skilled conflict practitioners, constructively managing conflict between or among people or groups with whom they engage.
This course is an elective in the NECR Program. It is open, space permitting, to cross-registrants from other fields and Columbia University schools and programs. There is no prerequisite knowledge or coursework to register for the course; however, if you have not taken a course in negotiation or studied that field, please contact the instructor. The course is delivered in-person on campus; participation by Zoom is not permitted. The course is semi-intensive and delivered over a partial semester.
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.
This course provides an opportunity for students in the Economics Master of Arts Program to engage in off-campus internships for academic credit that will count towards their requirements for the degree. The internships will facilitate the application of economic skills that students have developed in the program and prepare them for future work in the field.
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
Billions of dollars are raised and spent during U.S. presidential and congressional races each election cycle. Campaign expenditures play a critical role in election outcomes and political donations are used by corporations, unions, advocacy groups, and individuals to influence elected officials and public policy. Whether they are working for campaigns, advocacy groups, or consultants, political analysts need to have a sound understanding of campaign finance law and regulations, the chief strategies that contributors and recipients use to pursue their interests, and the incredibly rich data that is available to analyze and study campaign giving in the United States.
In this course, students will learn about the history and current state of campaign finance regulation, what motivates donors to give and what they may (or may not) receive in return, and how campaigns themselves fundraise and spend their billions. Students will become familiar with the ways data analytics have influenced how modern campaigns approach fundraising and the strategies used by candidates to finance a run for office. Finally, students will engage with the potential benefits and pitfalls of campaign finance reforms which, along with technological change, promise to keep political fundraising in a state of flux.
Earth Ethics is a framework for examining the ways in which human societies interrelate with natural systems, and the implications for our collective decision-making and behavior. This includes queries such as: how do we respond to the reality that those hurt first and worst by the modern megatrends of pollution and depletion are generally those least responsible for causing them? What are the laws, policies and social norms that undergird those megatrends? What are our responsibilities to future generations? What values shape our relationships with other species? In the pursuit of sustainability, what exactly are we sustaining and why? How does social change happen? Drawing from faith and wisdom traditions as well as science and history, this course will explore the moral, spiritual, and cultural dimensions of our global ecological circumstances and discern the principles and framing questions that should guide our response.
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.
An exploration of the central concepts of corporate finance for those who already have some basic knowledge of finance and accounting. This case-based course considers project valuation; cost of capital; capital structure; firm valuation; the interplay between financial decisions, strategic consideration, and economic analyses; and the provision and acquisition of funds. These concepts are analyzed in relation to agency problems: market domination, risk profile, and risk resolution; and market efficiency or the lack thereof. The validity of analytic tools is tested on issues such as highly leveraged transactions, hybrid securities, volatility in initial public offerings, mergers and acquisitions, divestitures, acquisition and control premiums, corporate restructurings, and sustainable and unsustainable market inefficiencies.
This course examines the relationship between colonialism, settlement and anthropology and the specific ways in which these processes have been engaged in the broader literature and locally in North America. We aim to understand colonialism as a theory of political legitimacy, as a set of governmental practices and as a subject of inquiry. Thus, we will re-imagine North America in light of the colonial project and its technologies of rule such as education, law and policy that worked to transform Indigenous notions of gender, property and territory. Our case studies will dwell in several specific areas of inquiry, among them: the Indian Act in Canada and its transformations of gender relations, governance and property; the residential and boarding school systems in the US and Canada, the murdered and missing women in Juarez and Canada and the politics of allotment in the US. Although this course will be comparative in scope, it will be grounded heavily within the literature from Native North America.
This course offers a deep dive into French contemporary novelist Annie Ernaux’s auto-sociobiographical fiction. It does so through close readings of some of her major works, organized
thematically and across Ernaux’s oeuvre. Close readings of texts will be paired with recent film/theatre adaptations, sociological and theoretical work that has inspired Ernaux, her work’s
growing critical reception (amplified by her Nobel prize last year), as well as other writers that have been inspired by hers. Themes covered include: writing impersonally in the first person;
what is auto-socio-biography; exploring women’s desire and sexuality; Ernaux’s feminism and other kinds of militancy; ethnographies of contemporary France and the baby-boomer
generation. Throughout, we will consider what kind of genre Ernaux’s writing is, and what writing as a knife can do.
This course provides a comprehensive examination of modern software product development, focusing on creating solutions that address clear user needs and challenges. A “product” in this context refers to a software program that instructs computer hardware to operate, solve problems, and manage tasks effectively.
Modern product development benefits from systematic practices that enhance efficiency, sustainability, and continuity. These practices, including flexibility, iterative development, customer feedback, and efficient project management, are essential for adapting quickly to rapidly evolving market and technology landscapes.
This is a required course that facilitates the capstone research paper writing process for second-year students enrolled in the M.A. Program in Economics. The research paper provides an opportunity to write a substantial piece of academic work in which the student is expected to demonstrate mastery of a field, along with the ability to think originally and to convey results clearly in writing.
TBA
TBA
Investing in professional growth is essential to building strong, adaptive, and innovative nonprofit organizations. Columbia University's M.S. in Nonprofit Management Professional Development Series is an online, bi-weekly, zero-credit seminar class that helps students stay current with best practices, navigate complex challenges, improve organizational sustainability, and enhance their impact in the communities they serve. Students will increase their networks and connect with potential mentors and employers while hearing how they can leverage the M.S. in Nonprofit Management degree in their own careers.
The course, which is a co-registration requirement for NOPM students taking Capstone, is open to all NOPM students and for cross-registration.
This course is designed for Project Management professionals looking to understand how the management of a project affects the overall accounting and financing functions of an organization. This course will also provide an understanding of interpreting financial information that is generated by an organization and being able to compare, contrast and benchmark against similar types of companies.
This course will allow participants in the program to understand the importance of the finance and accounting functions of an organization by teaching them how to read financial statements and related footnotes, obtain an understanding of basic accounting functions and terminology and introduce the participant on how to create and analyze budgets for individual project metrics and as well as overall company metrics. The course is also designed to integrate the participants with the program disciplines of project management including project design, project delivery and legal aspects. The course will also promote ideology of working in teams as the final assignment will be group presentations of a multi-faceted development project anchored by a minor league baseball facility, surrounding commercial and hospitality space and multi-unit residential subdivision. Teams will develop project budgets for each phase of the project. The teams will address how each aspect of the project will benefit the owner of the sports entertainment facility, address sustainability management and how technology and AI from the design and planning phase through the utilization and maintenance phase effects the financing of the project. Each group will customize their presentations from a case study provided by the instructor.
This is a required core class of the degree program and can be offered to other degree programs and certificates related to the development or investment in ground up and or rehabilitation projects. There is no prerequisite knowledge required to take this course. The course is a full semester in person/on campus class but will have an online and historical recording of all classes for those unable to attend any given class session or lecture.
Students are expected to have completed a year of high school physics and chemistry. It would be best to have also taken college level physics and chemistry.
Renewable energy is generated from natural processes that are continuously replenished. Aside from geothermal and tidal power, solar radiation is the ultimate source of renewable energy. In order to have a sustainable environment and economy, CO2 emissions must be reduced (and eventually stopped). This requires that the fossil fuel based technologies underlying our present electricity generation and transportation systems be replaced by renewable energy. In addition, the transition to renewable technologies will move nations closer to energy independence and thereby reduce geopolitical tensions associated with energy trading. This course begins with a review of the basics of electricity generation and the heat engines that are the foundation of our current energy systems. This course will emphasize the inherent inefficiency associated with the conversion of thermal energy to electrical and mechanical energy. The course then covers the most important technologies employed to generate renewable energy. These are hydroelectric, wind, solar thermal, solar photovoltaic, geothermal, biomass/biofuel, tidal and wave power. The course ends with a description of energy storage technologies, energy markets and possible pathways to a renewable energy future.