This course emphasizes the perspectives of foundational thinkers on the evolution and dynamics of social life. Readings address key sociological questions; including the configuration of communities, social control, institutions, exchange, interaction, and culture.
Course Objectives
By the end of this course, you should be able to:
Understand and critically engage with the varied narratives, objectives, and instruments of climate finance, and how they apply across sectors, regions, and institutional settings.
Understand and analyze the roles of key actors—governments, multilateral institutions, investors, insurers, and civil society—and the financial and legal mechanisms that shape capital flows.
Identify and assess the persistent challenges in climate finance, including disparities in access and cost of capital, weak institutional coherence, and gaps between risk frameworks and climate impact.
Critically evaluate current tools and approaches—such as blended finance, credit guarantees, insurance products, and rating systems—and engage with proposals for structural reform.
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.
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.
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.
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.
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.
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.
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.
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.
This course is designed to provide incoming first-year graduate students in Classics with a small reading class that will allow a faculty instructor to assess students’ needs before they advance further into the graduate program.
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.
This course is designed to provide incoming first-year graduate students in Classics with a small reading class that will allow a faculty instructor to assess students’ needs before they advance further into the graduate program.
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This course is designed to strengthen the academic writing skills of SIPA students whose first language is not English. Emphasizing clarity, structure, and academic rigor, the course supports students in developing the writing competencies necessary for success in the MIA and MPA programs. Students will practice summarizing complex texts, crafting literature reviews, explanatory and argumentative essays, and revising their work based on detailed instructor feedback. The course also reinforces advanced grammar, vocabulary, and citation practices, with an emphasis on avoiding plagiarism and promoting original thought.
Assignments include short weekly exercises, midterm and final in-class essays, and three major take-home writing projects. Active participation, peer review, and group discussion of assigned readings are essential components of the course. By the end of the semester, students will gain confidence writing in English across academic and policy contexts while deepening their understanding of key public affairs topics.
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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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.
In this introductory workshop, students write several short screenplays over the course of the semester and learn the basics of the craft. Character, action, conflict, story construction, the importance of showing instead of telling, and other essential components are explored.