This interdisciplinary course, taken in the fall semester, is a comprehensive introduction to quantitative research in the social sciences. The course focuses on foundational ideas of social science research, including strengths and weaknesses of different research designs, interpretation of data drawn from contemporary and historical contexts, and strategies for evaluating evidence. The majority of the course is comprised of two-week units examining particular research designs, with a set of scholarly articles that utilize that design. Topics include: the “science” of social science and the role of statistical models, causality and causal inference, concepts and measurement, understanding human decision making, randomization and experimental methods, observation and quasi-experimentation, sampling, survey research, and working with archival data.
Both human and natural systems are growing more vulnerable to climate variability (e.g., the anomalous weather induced by the El Nino-Southern Oscillation, or the increase in hurricanes that occurs when ocean currents warm the Atlantic) and to human-induced climate change, which manifests itself primarily through increases in temperature, precipitation intensity, and sea level, but which can potentially affect all aspects of the global climate. This course will prepare you to estimate climate hazards in your field thereby accelerating the design and implementation of climate-smart, sustainable practices. Climate models are the primary tool for predicting global and regional climate variations, for assessing climate-related risks, and for guiding adaption to climate variability and change. Thus, a basic understanding of the strengths and limitations of such tools is necessary to decision makers and professionals in technical fields.
This course will provide a foundation in the dynamics of the physical climate system that underpin climate models and a full survey of what aspects of the climate system are well observed and understood and where quantitative uncertainties remain. Students will gain a fundamental understanding of the modeling design choices and approximations that distinguish Intergovernmental Panel on Climate Change (IPCC)-class climate models from weather forecasting models and that create a diversity of state-of-the-art climate models and climate projections.
This course will provide an overview of the ways in which climate model output and observations can be merged into statistical models to support applications such as seasonal and decadal projections of climate extremes, global and regional climate impacts, and decision-making. Students will develop the skills to visualize, analyze, validate, and interpret climate model output, calculate impact-relevant indices such as duration of heat waves, severity of droughts, or probability of inundation, and the strategies to characterize strengths and uncertainties in projections of future climate change using ensembles of climate models and different emission scenarios.
Proseminar is designed to offer beginning MA and PhD students an overview of (i) the major sub-disciplinary areas that are gathered under the umbrella term ‘classics’, making it a fundamentally interdisciplinary field of enquiry, and (ii) the diverse methodologies that are standardly applied in many subfields of classical research and publication.
Utilizing a case-study approach, this course will offer a focused study of climate change adaptation policy, exploring dimensions of adaption across sectors and scales. With a thematic focus on pervasive global inequities, students will also consider challenges associated with international development and disaster risk management. An inter-disciplinary framework will enrich the course, and students will learn about perspectives from the natural sciences, law, architecture, anthropology, humanitarian aid, and public policy.
Prerequisites: some background in ecology, evolutionary biology, and/or statistics is recommended. An introduction to the theoretical principles and practical application of statistical methods in ecology and evolutionary biology. The course will cover the conceptual basis for a range of statistical techniques through a series of lectures using examples from the primary literature. The application of these techniques will be taught through the use of statistical software in computer-based laboratory sessions.
Prerequisites: One semester of undergraduate statistics The data analysis course covers specific statistical tools used in social science research using the statistical program R. Topics to be covered include statistical data structures, and basic descriptives, regression models, multiple regression analysis, interactions, polynomials, Gauss-Markov assumptions and asymptotics, heteroskedasticity and diagnostics, models for binary outcomes, naive Bayes classifiers, models for ordered data, models for nominal data, first difference analysis, factor analysis, and a review of models that build upon OLS. Prerequisite: introductory statistics course that includes linear regression. There is a statistical computer lab session with this course: QMSS G4017 -001 -DATA ANALYSIS FOR SOC SCI
Prerequisites: Students must meet with the instructor prior to taking the course. This course is intended to help students increase their ability level in the four core language skills (reading, writing, listening, and speaking) from advanced to super-advanced. It serves as a bridge between mastering the overall Japanese language and using it for analysis, research, and literary criticism. This is a mandatory course for Ph.D students in Japanese Studies.
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This course will introduce students to the main concepts and methods behind regression analysis of temporal processes and highlight the benefits and limitations of using temporally ordered data. Students study the complementary areas of time series data and longitudinal (or panel) data. There are no formal prerequisites for the course, but a solid understanding of the mechanics and interpretation of OLS regression will be assumed (we will briefly review it at the beginning of the course). Topics to be covered include regression with panel data, probit and logit regression of pooled cross-sectional data, difference-in-difference models, time series regression, dynamic causal effects, vector autoregressions, cointegration, and GARCH models. Statistical computing will be carried out in R.
In the 2015 Paris Agreement, the international community vowed to “reach global peaking of greenhouse gas emissions as soon as possible,” and to achieve “rapid emissions reductions thereafter.” Despite that, however, global emissions continue to increase. The lack of progress has spurred interest in the possibility of removing greenhouse gases directly from the atmosphere. Modelling by the Intergovernmental Panel on Climate Change suggests that atmospheric greenhouse gas removal (GHGR) could help to combat climate change in three ways. First, GHGR could be used to reduce net emissions in the short-term, while countries are in the process of decarbonizing. Second, GHGR could be used to offset residual emissions from hard-to-decarbonize sectors (e.g., aviation) and thus achieve net-zero emissions. Third, in the longer-term, GHGR could be used to achieve net-negative emissions if deployed at levels exceeding residual emissions.
Scientists have proposed a variety of GHGR techniques, but questions remain about their efficacy, benefits, and risks. Technical feasibility is not the only consideration as large-scale deployment of GHGR could raise a host of social, ethical, and governance issues. For example, some climate activists have argued GHGR may discourage action to reduce emissions, and further entrench fossil fuel use. Others have expressed concern about the equity and justice implications of deploying GHGR, arguing
that it could exacerbate existing inequalities and place the most vulnerable at greater risk. Existing governance frameworks may not be effective in preventing these negative outcomes nor maximizing the benefits of GHGR.
This course will explore possible technique for large-scale GHGR. We will discuss the feasibility of deploying different techniques, with a particular focus on the ethical, social, and governance issues that large-scale deployment could raise. We will also consider strategies for advancing just and equitable deployment and explore the role of different actors (e.g., governments, the private sector, and civil society) in ensuring that occurs.
This year-long course introduces students to important conversations within and about oral history through a series of curated public lectures. We will meet for six events a semester, plus one session to orient you to the class. From 5:00 – 6:00, students will meet with the speaker for an informal conversation about their career path and research process. The public portion of the event will be from 6:10 to 7:30 PM. You should plan to be in class until 8 in case an event runs slightly over, and so that you can stick around after the event to chat with the speaker or have a glass of wine.
This course presents decision science to students, showing it to be a source of concepts and techniques to promote more extensive and effective climate action. It emphasizes the relevance of decision science to students who are planning professional careers in climate-focused organizations and sectors, while also being of value to students who plan future studies in academic and professional programs.
As is widely recognized, there has been insufficient progress towards the goals of stabilizing greenhouse gas concentrations at safe levels and of adapting to existing and projected climate impacts. Understanding how individuals and organizations make decisions is a key step to reducing this gap. Decision science can help design resources (finance, regulation, governance, information and communications) in ways that promote action.
The field of decision science emerged decades ago, drawing on psychology, economics and other social science fields to address problems of poor decision-making in areas such as finance and health. Recent research has extended this field to climate action. It has clarified the obstacles that impede climate decision-making in many settings, and it has developed techniques to improve these decisions. There is an increasing body of empirical research that tests the effectiveness of these techniques in a wide variety of settings.
This course familiarizes students with central concepts and methods of decision science. The modules of the course focus on specific concepts and on techniques linked to them, drawing on concrete examples from climate-relevant domains such as disaster risk reduction, health, energy, water and food security. The readings include studies which assess the effectiveness of specific techniques to support climate decisions. The course covers a range of different approaches. It shows that each of these can be useful to address obstacles to effective decision-making, but there is no silver bullet. Instead, the course provides students with means to select the decision techniques that are effective to address specific issues in specific contexts.
Students will learn fundamental marketing concepts and their application. By the end of this class you will know: the elements of a market, company strategy, how to identify customers and competition, the fundamental elements of the marketing mix (product, price, placement and promotion) how to research consumer behavior, and pricing strategies. Students will have extensive use of case study projects. Please note that there are separate online and in-person versions of the course, and the modalities offered may vary by semester. Be sure to check the modalities of the sections offered and enroll in the correct modality for your situation.
Students will learn fundamental marketing concepts and their application. By the end of this class you will know: the elements of a market, company strategy, how to identify customers and competition, the fundamental elements of the marketing mix (product, price, placement and promotion) how to research consumer behavior, and pricing strategies. Students will have extensive use of case study projects. Please note that there are separate online and in-person versions of the course, and the modalities offered may vary by semester. Be sure to check the modalities of the sections offered and enroll in the correct modality for your situation.
Students will learn fundamental marketing concepts and their application. By the end of this class you will know: the elements of a market, company strategy, how to identify customers and competition, the fundamental elements of the marketing mix (product, price, placement and promotion) how to research consumer behavior, and pricing strategies. Students will have extensive use of case study projects. Please note that there are separate online and in-person versions of the course, and the modalities offered may vary by semester. Be sure to check the modalities of the sections offered and enroll in the correct modality for your situation.
The purpose of this course is to provide students with a deep and broad understanding of stories and how they can be used in strategic communication. Drawing from a wealth of evidence-based and field-tested work on storytelling from both local and global contexts, students will learn why stories tend to be so powerful and—with a focus on the written, performed, and transmedia aspects of storytelling—gain experience in telling stories to achieve organizational objectives. Your skills will be sharpened through lively seminar discussions, storytelling exercises, workshop-style coaching, and presentations and on-camera practice. By the end, students will walk away with a new mindset and a host of strategies that can be immediately implemented in their everyday work.
This seminar is the first half of a two-semester practicum in which students will learn and practice the critical skills required to conceptualize, conduct, analyze and disseminate oral history projects in a range of contexts and communities.
In the Fall semester, we will learn project design, approaches to interviewing and other genres of oral history, remote and in-person audio recording, transcribing, indexing, and digital archiving. Students will have the option of working on oral history projects conducted in partnership with Fieldwork Partners or working on their own projects.
By the Spring semester course on Curating Oral Histories, students will be expected to be primarily working on their own projects. In the Spring we will focus most of our attention on the analysis and dissemination of oral histories, including audio editing, online presentation, museum exhibits, and other public oral history genres. Our spring work will culminate in a collaboratively curated interactive public exhibit
Politics involves a complex interaction of competing interests. For practitioners, it is crucial to understand how efforts are met with responses, and predicting those responses is critical to designing successful strategies. Game theory is the formal mathematical analysis of strategic interaction across the social sciences. This course provides a general theoretical language to the theory of games, examining the intentional thought process of rational actors in strategic environments. Students will acquire tools for understanding the dynamics that lead to the success of a political campaign or policy-making effort. Course topics include two-person games, dynamic games, bargaining, and signaling. Students will also examine a variety of cases.
Politics involves a complex interaction of competing interests. For practitioners, it is crucial to understand how efforts are met with responses, and predicting those responses is critical to designing successful strategies. Game theory is the formal mathematical analysis of strategic interaction across the social sciences. This course provides a general theoretical language to the theory of games, examining the intentional thought process of rational actors in strategic environments. Students will acquire tools for understanding the dynamics that lead to the success of a political campaign or policy-making effort. Course topics include two-person games, dynamic games, bargaining, and signaling. Students will also examine a variety of cases.
This course is about cost-benefit analysis and the economic evaluations of policies and projects. Cost benefit analysis (CBA) consists of a comprehensive set of techniques used to evaluate government programs. It is now routinely applied in such program areas as transportation, water projects, health, training and education, criminal justice, environmental protection, urban policy and even in the international arena such as foreign direct investment. Many of the techniques of CBA can also be applied to private sector decision-making. The objective of CBA is to determine whether the benefits of a particular program, policy or decision outweigh its costs. The techniques used to determine this are sometimes quite simple, but on other, increasingly frequent occasions are highly sophisticated. Sophisticated cost benefit studies are based on a framework that utilizes the basic concepts of economic theory. In addition, statistical and econometric analyses are often needed to estimate program effects from diverse available data. The course has two parts: methodology and practice. The goal is for students to be practically adept to undertake an independent cost-benefit analysis.
Forests are often called the lungs of the earth, for their role in converting atmospheric CO2 into the life-sustaining Oxygen that we all breathe. Collectively, the global forests contribute to roughly 40% of the annual global carbon sink, and yet little is known about the drivers of terrestrial carbon sequestration, and the processes involved in these systems response to changes in climate. Forested landscapes also comprise some of the most critical habitats on planet Earth, by serving as refuge to diverse and often endangered flora and fauna, and as regulators of water and soils. These services are particularly important for highland regions where forests are heavily exploited and are often the primary source of water and food for marginalized human populations. This course takes an in-depth look into the current, primary literature on the direct and indirect effects of climate change on forest ecosystems around the globe, and examines some of the primary policy solutions to forest loss mitigation and sustainability. Because the instructor is from the LDEO Tree Ring Lab there will be an emphasis on using dendrochronology for understanding changes in biomass for forest environments, with emphasis on the broadleaf forests of eastern North America and the largely coniferous, fire-prone forests of the American West. Students will have access to multiple sources of data, including satellite, forest inventory, tree rings and eddy-flux measurements. The course will have a field component that will take place at the Black Rock Forest (BRF), about two hours north of NYC. Students will conduct primary research for a final project, with the goal being to develop a set of group projects related to forests and climate change. This course will prepare students to assess the impacts of climate extremes on forest systems and to understand the complexities of response possibilities from diverse ecosystems.
This course will combine lectures and assigned course readings to develop the framework for understanding global forest response to climate change. Each class will begin with a 5-question mini-quiz based upon the assigned readings and the previous lecture. This class will discuss the questions asked, techniques used and key findings of the papers, with discussions led by the students. The class includes a field trip to Black Rock Forest (dates TBD) where students will collect data for use in a class project, thereby providing the opportunity to develop skills in field research and data analysis.
Forests are often called the lungs of the earth, for their role in converting atmospheric CO2 into the life-sustaining Oxygen that we all breathe. Collectively, the global forests contribute to roughly 40% of the annual global carbon sink, and yet little is known about the drivers of terrestrial carbon sequestration, and the processes involved in these systems response to changes in climate. Forested landscapes also comprise some of the most critical habitats on planet Earth, by serving as refuge to diverse and often endangered flora and fauna, and as regulators of water and soils. These services are particularly important for highland regions where forests are heavily exploited and are often the primary source of water and food for marginalized human populations. This course takes an in-depth look into the current, primary literature on the direct and indirect effects of climate change on forest ecosystems around the globe, and examines some of the primary policy solutions to forest loss mitigation and sustainability. Because the instructor is from the LDEO Tree Ring Lab there will be an emphasis on using dendrochronology for understanding changes in biomass for forest environments, with emphasis on the broadleaf forests of eastern North America and the largely coniferous, fire-prone forests of the American West. Students will have access to multiple sources of data, including satellite, forest inventory, tree rings and eddy-flux measurements. The course will have a field component that will take place at the Black Rock Forest (BRF), about two hours north of NYC. Students will conduct primary research for a final project, with the goal being to develop a set of group projects related to forests and climate change. This course will prepare students to assess the impacts of climate extremes on forest systems and to understand the complexities of response possibilities from diverse ecosystems.
This course will combine lectures and assigned course readings to develop the framework for understanding global forest response to climate change. Each class will begin with a 5-question mini-quiz based upon the assigned readings and the previous lecture. This class will discuss the questions asked, techniques used and key findings of the papers, with discussions led by the students. The class includes a field trip to Black Rock Forest (dates TBD) where students will collect data for use in a class project, thereby providing the opportunity to develop skills in field research and data analysis.
This course is designed to expose students in the QMSS degree program to different methods and practices of social science research. Seminar presentations are given on a wide range of topics by faculty from Columbia and other New York City universities, as well as researchers from private, government, and non-profit settings. QMSS students participate in a weekly seminar. Speakers include faculty from Columbia and other universities, and researchers from the numerous corporate, government, and non-profit settings where quantitative research tools are used. Topics have included: Now-Casting and the Real-Time Data-Flow; Art, Design - Science in Data Visualization; Educational Attainment and School Desegregation: Evidence from Randomized Lotteries; Practical Data Science: North American Oil and Gas Drilling Data.
Life Cycle Assessment (LCA), a methodology to assess the environmental impact of products, services, and industrial processes is an increasingly important tool in corporate sustainability management. This course teaches both the theoretical framework as well as step-by-step practical guidelines of conducting LCAs in companies and organizations. Particular emphasis is placed on separating the more academic, but less practically relevant aspects of LCA (which will receive less focus) from the actual practical challenges of LCA (which will be covered in detail, including case studies). The course also covers the application of LCA metrics in a companies’ management and discusses the methodological weaknesses that make such application difficult, including how these can be overcome. Product carbon footprinting (as one form of LCA) receives particular focus, owing to its widespread practical use in recent and future sustainability management.
This course has two goals. One, it is designed to expose students in the QMSS degree program to different methods and practices of social science research. Seminar presentations are given on a wide range of topics by faculty from Columbia and other New York City universities, as well as researchers from other settings. Two, it is also designed to give students important professional development skills, particularly around academic writing, research methods and job skills.
Prerequisites: BUSI PS5020 Introduction to Marketing/or Professor Approval is required Students will develop analytical skills used to formulate and implement marketing driven strategies for an organization. Students will develop a deeper understanding of marketing strategies and how to implement tactics to achieve desired goals. Students will work on case study projects in both individual and a team based projects. By the end of this course you will be able to develop a marketing strategy based market assessments and company needs.
Prerequisites: BUSI PS5020 Introduction to Marketing/or Professor Approval is required Students will develop analytical skills used to formulate and implement marketing driven strategies for an organization. Students will develop a deeper understanding of marketing strategies and how to implement tactics to achieve desired goals. Students will work on case study projects in both individual and a team based projects. By the end of this course you will be able to develop a marketing strategy based market assessments and company needs.
This course examines the strategic role for communication in driving organizational outcomes. It covers key aspects of communication management, including how to plan, implement and measure strategic communication initiatives. Students learn to assess organizational needs, identify stakeholders and draft messaging that speaks credibly to a variety of constituencies, both internal and external. We also emphasize fundamental business skills, such as interpreting financial reports and understanding the language of business.
Life histories and narratives don’t speak for themselves. To disclose what these have to offer, we have to analyze them. This can be true even if the teller or author of a story is making a point with her history or narrative. That is, this teller or that author is not the only interpreter of the narrative. And this is so whether it is about herself, about other people, about organizations, about movements, about whatever; whether it’s “real” or “imaginary;” whether the medium is words, images, sound, or whatever senses a “text” engages. Life histories and narratives—usually told as sequences of events, sometimes temporally sequential, maybe connected in the telling but maybe not—have to be analyzed to be understood. Put another way: How are you going to make sense of your interviews? We need to think about analytic methods to do so. This course focuses on what it means to deploy some such methods, the utility of doing so, and the importance of doing so self-consciously. Because we employ methods for substantive purposes, the course focuses on using methods for thinking about the relationship between individual lives and the social structures within which those lives are lived. That is, we learn how to develop and deploy C. Wright Mills’s “sociological imagination” through methods learned.
The course tries to achieve these ends by considering ways in which scholars and writers analyze life history and narrative information. It focuses on the utility and importance ofdifferent approaches to analyzing such information, and exposes students to the mechanics of analytic tools for carrying out such analyses. In particular, we introduce approaches used in formal social science, historical and anthropological analyses of qualitative information analysis and in not so formal social science analyses, e.g., novels! These methods/approaches can be used to reveal underlying dynamics that generate life histories and/or narratives and so deepen our understanding of specific people and their relationship to larger social and historical elements.
The purpose of this course is to provide an overview of trends and best practices in corporate communications relating to sustainability, with a particular focus on global sustainability reporting frameworks and green marketing communications. It is designed for those who hold/will hold positions in organizations with responsibilities for communicating the sustainability goals, challenges and achievements, as well as accurately and honestly communicating the environmental aspects of an organization's products and services. Increasingly, large corporations are creating c-suite roles or dedicated departments to oversee this function. More typically, multiple functions contribute information such as: Corporate Communications, Marketing, Community Affairs, Public Policy, Environmental Health & Safety, R&D, Facilities, Operations and Legal. Benefits of reporting range from building trust with stakeholders, and uncovering risks and opportunities; to contributing to stronger long-term business strategy, and creating new products and services.
What is the meaning of embodiment? For most of us, it seems this is a question we only confront when our bodies “break down,” “fail,” or “betray” us. In truth, however, it is a question we are answering with every intentional movement, feeling, impulse and desire of our conscious life. In this course, we will explore various approaches to embodiment from contemporary thinkers. We will ask what these approaches might tell us about the experience of illness and how they might inform our understanding of
care
, both of ourselves and others.
African and African Diasporic peoples have been central to the creation and transformation of global ecologies and landscapes. As the birthplace of humankind, the African continent features the longest archaeological record in the world, with abundant, yet often underrepresented, material and historical evidence for remarkable Indigenous African innovations in the areas of technology, food production, and resource and land use. This course specifically examines Black ecologies preceding and then radically transformed by the Trans-Atlantic Slave Trade. Beginning in the late fifteenth century, the enslavement of millions of Africans and their forced translocation to the Americas and Caribbean precipitated ecological transformations on all sides of the Atlantic, as African peoples, knowledge, resources and ecological inheritances were appropriated by the European mercantile system. Enslaved Africans transformed American landscapes via extractive industries of plantations and mines and suffered the emergence of toxic landscapes and disease alongside Native American communities. Africans also recreated African ecologies as they created livelihoods and landscapes of resistance and freedom in the Americas. The legacies of the Atlantic Era maintain a persistent dynamic in which African and African Diasporic communities experience disproportionate burdens of environmental injustice today. The concept of Black ecologies reflects the marginality, systemic racism and dispossession experienced by Black peoples and their landscapes. Black ecologies also allow us to understand African and African Diasporic ecological innovations, resistance and resilience, and the pathways to future sustainability and justice they promise.
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.
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.
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.
With climate change visibly affecting communities around the world, it is essential to society to transition to renewable energy sources to minimize further climate warming. As for any generation source, installation of renewables is very capital intensive. This course will examine key “ingredients” necessary to finance a renewable project / make it economic, including but not limited to:
Ability to finance at the project level
Different forms of capital available / requirements to successfully finance
Revenue models for renewables investors and required returns
Role of government incentives in financing renewable energy / latest US legislation
Key technical issues that arise with increased renewables penetration
Global geopolitical landscape and its impact on energy transition
Equity an inclusion in the approach to building a renewable landscape
As part of the course, we will review multiple case studies and will approach the topics both from theoretical and quantitative perspectives.
This course brings students from The School of the Arts and The Climate School together to explore new and compelling approaches to navigating climate solutions in the worlds we create and the stories we tell in theater, film, television, digital, visual art, and creative writing. In our current era of rapid change and transformation, artists and environmentalists have an important role to play in grasping the zeitgeist through an integrated lens of science, culture, and imagination.
Interdisciplinary collaboration in storytelling can drive feelings of understanding and agency by articulating the massive social changes that are imminent and the emotional uncertainties around climate. There’s a rapidly growing audience for these stories — but there are way too few of them. Delving into key areas of environmental concerns, students will learn how to access and analyze systems of science-based research and innovation, and build new muscles in storytelling, cultural strategies and longterm thinking for a wide range of artistic visions. Arts students will strengthen climate literacy and sciencethinking skills; Climate students will strengthen storytelling skills. We will study the connection between stories and audiences, including multi-cultural perspectives across the platforms where we consume arts and entertainment today. Together we will explore a multitude of narrative structures and styles of storytelling and we will produce fresh thinking for this generation around the role that climate storytelling can play in popular culture and adapting to change. Students will track their evolution of their vision over the span of the course, culminating in Week/Class 11: The Republic of Zeitgeist & Our Future, where students lead and co-teach this class around our collective visions for what our future should be.
Students will participate through creative writing assignments, student-led discussions and team exercises, and watching & reading climate content. We will practice collaborative strategies to explore new ways to tell creative and complex human stories in the arts and assess effective ways to shift culture to imagine and adapt to what life on a transformed global scale may become for all of us.
Averting the deepest climate crisis and mitigating the substantial financial costs of global warming and its consequences will require the decarbonization of the world’s energy systems by 2050, necessitating trillions of dollars of public and private investment. Pathways and policies are not clear – or are inconsistent – on the respective roles of public and private investment, and often overlook the myriad structural, legal, and institutional barriers to scaling both. The absence of coherent policy frameworks has also led to a proliferation of voluntary private-sector initiatives, including the explosion of “ESG investing.” Many of the challenges of sustainable investment are approached in silos - focusing specifically on public finance or on private capital, or on certain sectors, technologies, or impacts. This course is decidedly interdisciplinary and international. We will explore the myriad interrelated challenges to investing in the energy transition, to see why the ‘big picture’ is important for really understanding policies or practices at a granular level. And we will understand the importance of policy frameworks to guide, incentive, enable and structure critical investments for the energy transition. Finally, we will engage deeply with the complexities, tensions and policy trade-offs that surround investing in the energy, so that students can think about how to navigate these.
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.
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.
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.
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.
This course gives students two credits of academic credit for the work they perform in such an social science oriented internships.
What are urban infrastructures that promote sustainability? Such infrastructure must reduce environmental pollution at all scales, provide necessary urban services efficiently and enhance urban resilience to multiple potential crises. Sustainable infrastructure also must promote social and economic equity and environmental justice. And sustainable infrastructure must be economically feasible. This class will use these concepts to evaluate urban infrastructure and identify challenges to making urban infrastructure sustainable. Importantly, the course will use theories of urban transitions to help identify the drivers of potential change in infrastructure development and envision the emergence of sustainable infrastructure. This class will examine these notions across the energy, transportation, water supply and waste water treatment, buildings, health and open space urban sectors.
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.
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 will encounter a range of scientific problems throughout their Science-specific courses that require a strong foundational level of mathematical and statistical knowledge. In addition, course-work will involve computer coding to read, analyze, and visualize data sets. This course provides an overview of essential mathematical concepts, an introduction to new concepts in statistics and data analysis, and provides computer coding skills that will prepare students for coursework in the Master of Science in Sustainability Science program as well as to succeed in a career having a sustainability science component. In addition to an overview of essential mathematical concepts, the skills gained in this course include statistics, and coding applied to data analysis in the Sustainability Sciences. Many of these skills are broadly applicable to science-related professions, and will be useful to those having careers involving interaction with scientists, managing projects utilizing scientific analysis, and developing science-based policy. Students enrolled in this course will learn through lectures, class discussion, and hands-on exercises that address the following topics: Review of mathematical concepts in calculus, trigonometry, and linear algebra; Mathematical concepts related to working on a spherical coordinate system (such as that for the Earth); Probability and statistics, including use of probability density functions to calculate expectations, hypothesis testing, and the concept of experimental uncertainty; Concepts in data analysis, including linear least squares, time-series analysis, parameter uncertainties, and analysis of fit; Computer coding skills, including precision of variables, arrays and data structures, input/output, flow control, and subroutines, and coding tools to produce basic X-Y plots as well as images of data fields on a global map.
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.
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: two semesters of a rigorous, molecularly-oriented introductory biology course (such as UNC2005 and UN2006), or the instructor's permission. This course will cover the basic concepts underlying the mechanisms of innate and adaptive immunity, as well as key experimental methods currently used in the field. To keep it real, the course will include clinical correlates in such areas as infectious diseases, autoimmune diseases, cancer, and transplantation. Taking this course won't turn you into an immunologist, but it may make you want to become one, as was the case for several students last year. After taking the course, you should be able to read the literature intelligently in this rapidly advancing field.
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.
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