INTRO TO THE MATH OF FINANCE

Prerequisites: MATH UN1102 and MATH UN1201 , or their equivalents. Introduction to mathematical methods in pricing of options, futures and other derivative securities, risk management, portfolio management and investment strategies with an emphasis of both theoretical and practical aspects. Topics include: Arithmetic and Geometric Brownian ,motion processes, Black-Scholes partial differential equation, Black-Scholes option pricing formula, Ornstein-Uhlenbeck processes, volatility models, risk models, value-at-risk and conditional value-at-risk, portfolio construction and optimization methods.

• Department: Mathematics
• MW 7:40 p.m.-8:55 p.m.
• Instructor: Mikhail Smirnov
  CULPA: 16 Info
• 3 points

Introduction to Political Analytics

Intro to Political Analyt

Analytics and data-driven decision-making is playing an ever-larger role in modern political campaigns, think tanks, and media discussions of politics. In this foundations course, students learn about the history and evolution of data science in politics in the US and abroad, with a review of recent developments and trends as well as issues related to data governance, data privacy, and data ethics. Students are introduced to the scope of analytic methods employed across a range of relevant areas including campaigns, government, policy-making, and journalism. The course familiarizes students with a range of available tools and the ways in which they can be practically applied to politics and related fields. The course lays the groundwork for the specialized courses which will follow and helps students consider paths they may want to pursue to acquire specific knowledge and skills as they progress through the degree program.

• Department: Political Analytics
• R 6:10 p.m.-8 p.m.
• Instructor: Michael Schwam-Baird
  Info
• 2 points

QUANT THRY/METHODOLOGY-SOC SCI

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.

• Department: Quantitative Methods/Social Sciences
• W 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• T 6:10 p.m.-8 p.m.
• W 4:10 p.m.-6 p.m.
• T 4:10 p.m.-6 p.m.
• R 6:10 p.m.-8 p.m.
• M 2:10 p.m.-4 p.m.
• Instructor: Maiko Yomogida
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• Instructor: Jingyan Yang
  h-index: 19 Info
• 3 points

MANAGING HUMAN BEHAVIOR IN THE ORGANIZAT

Students will gain an overview of major concepts of management and organization theory, concentrating on understanding human behavior in organizational contexts, with heavy emphasis on the application of concepts to solve managerial problems. By the end of this course students will have developed the skills to motivate employees, establish professional interpersonal relationships, take a leadership role, and conduct performance appraisal.

• Department: Human Capital Management
• W 6:10 p.m.-8 p.m.
• M 8:30 p.m.-10 p.m.
• T 8:10 p.m.-10 p.m.
• Instructor: Skye Cleary
  Info
• Instructor: Aaron S Wallen
  CULPA: 1 h-index: 5 Info
• Instructor: Patti Ippoliti
  Info
• 3 points

CLIMATE SCI FOR DEC MAK: MOD, ANL & AP

MODELING ANALYSIS AND APPLS

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.

• Department: Sustainability Science
• W 4:10 p.m.-6 p.m.
• Instructor: Yutian Wu
  h-index: 15 Info
• 3 points

Proseminar

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.

• Department: Classics
• F 2:10 p.m.-4 p.m.
• T 4:10 p.m.-6 p.m.
• Instructor: Elizabeth K Irwin
  CULPA: 7 Info
• Instructor: Katharina Volk
  CULPA: 7 Info
• 1 points

INTRO-STAT-ECOLGY/EVOL BIO-LAB

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.

• Department: Ecology, Evolution, and Environmental Biology
• T 6:10 p.m.-7:25 p.m.
• W 6:10 p.m.-7:25 p.m.
• Instructor: Steffen Foerster
  Info
• 0 points

CINEMA HIST II: 1930-1960

• Department: Film
• R 2:10 p.m.-3:25 p.m.
• Instructor: Ronald Gregg
  CULPA: 2 Info
• 3 points

DATA ANALYSIS FOR THE SOCIAL SCIENCES

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

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

FIFTH YEAR JAPANESE I

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.

• Department: East Asian Languages and Cultures
• MW 11:40 a.m.-12:55 p.m.
• Instructor: Miharu Nittono
  CULPA: 10 Info
• 4 points

Roots and Branches of Oral History

Roots & Branches of Oral

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• Department: Oral History
• T 10:10 a.m.-1 p.m.
• Instructor: Mary M Clark
  CULPA: 1 Info
• 4 points

TIME SERIES, PANEL DATA & FORECASTING

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.

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

ORAL HISTORY WORKSHOP

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.

• Department: Oral History
• R 6 p.m.-8 p.m.
• Instructor: Amy A Starecheski
  h-index: 5 Info
• 1 points

STRATEGIC STORYTELLER

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.

• Department: Strategic Communication
• F 10:10 a.m.-noon
• Instructor: Lauren R Glover
  Info
• 3 points

CINEMA HIST III:1960-1990

• Department: Film
• M 2:10 p.m.-6 p.m.
• Instructor: Annette Insdorf
  CULPA: 10 Wikipedia Info
• 3 points

CLOSE READING: GIVING AND RECEIVING ACCO

CLOSE READING: GIVING + R

• Department: Narrative Medicine
• M 2:10 p.m.-5 p.m.
• M 6:10 p.m.-9 p.m.
• T 1:10 p.m.-4 p.m.
• Instructor: Danielle R Spencer
  h-index: 5 Info
• 4 points

ORAL HIST:FIELDWORK/DOCUMENTN

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

• Department: Oral History
• W 10:10 a.m.-1 p.m.
• Instructor: Amy A Starecheski
  h-index: 5 Info
• 5 points

INTRODUCTION TO MARKETING

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.

• Department: Strategic Communication
• W 4:10 p.m.-6 p.m.
• W 12:10 p.m.-2 p.m.
• Instructor: Young Mi Park
  Info
• Instructor: Andrew Blum
  Info
• 3 points

COST BENEFIT ANALYSIS

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.

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Anyi Wang
  Info
• Instructor: Alexander Heil
  Info
• 3 points

RES SEM IN QUANT MTHS-SOC SCI I

RES SEMINAR I

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.

• Department: Quantitative Methods/Social Sciences
• R 8:10 p.m.-10 p.m.
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 3 points

THEORY & PRACTICE OF LIFE CYCLE ASSESSME

THEORY & PRAC LIFE CYCL ASSMNT

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.

• Department: Sustainability Management
• W 4:10 p.m.-6 p.m.
• W 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• Instructor: Christoph J Meinrenken
  h-index: 15 Info
• Instructor: Kim Matsoukas
  Info
• Instructor: Adriana B Kliegman
  Info
• 3 points

DEVELOPMENTAL BIOLOGY

• Department: Biological Sciences
• TR 1:10 p.m.-2:25 p.m.
• Instructor: Alice Heicklen
  CULPA: 15 Info
• 3 points

RES SEM IN QUANT MTHS-SOC SCI II

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.

• Department: Quantitative Methods/Social Sciences
• R 8:10 p.m.-10 p.m.
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 3 points

STRATEGIC COMMUNICATION MANAGEMENT

STRATEGIC COMMUNICATION MGMT

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.

• Department: Strategic Communication
• W 10:10 a.m.-noon
• W 4:10 p.m.-6 p.m.
• W 10:10 a.m.-noon
• SF 8:30 a.m.-12:30 p.m.
• Instructor: Andrea Legnani
  Info
• Instructor: Gabriel Amor
  Info
• Instructor: Tim Gingrich
  Info
• Instructor: Don Waisanen
  Info
• 3 points

ILLNESS/DISABILITY NARRATIVES

• Department: Narrative Medicine
• W 12:10 p.m.-3 p.m.
• Instructor: Sayantani T Dasgupta
  CULPA: 2 Info
• 4 points

Social Science and Other Approaches to S

Soc Sci & Other Apps

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.

• Department: Oral History
• R 1:10 p.m.-3 p.m.
• Instructor: William McAllister
  Info
• 4 points

MARKETING STRATEGY

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 team based projects. By the end of this course, you will be able to develop a marketing strategy based on market assessments and company needs.

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

CORPORATE SUSTAINABILITY REPORTING AND S

CORP SUST REPORTING & STR

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.

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

Experiments

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

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

DISABILITY STUDIES

This course is intended and designed to provide a focus on disability as distinct from illness. Disability Studies approaches disability not as an attribute of an individual's body or mind but as a social relation that creates disadvantages for some people and privileges others based on the presence or absence of stigmatized physical and mental differences. Disability studies provides a critical framework to examine the social practices, cultural representations in various media, and public policies through which shared understandings of disability are developed and expressed, reproduced, contested and reimagined. This course draws on various disciplinary perspectives to understand the broad and complex phenomenon of disability in historical perspective, as represented in literature and culture, and especially as it impinges on medical scenarios.

• Department: Narrative Medicine
• T 2:10 p.m.-4 p.m.
• Instructor: Griffith T Couser
  Info
• 3 points

GHG EMISSIONS: MEASURING AND MINIMIZING

GHG EMISSIONS CARBON FOOT

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

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

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

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Jonathan C Dickinson
  CULPA: 1 Info
• Instructor: Ryan A Meinke
  Info
• Instructor: Anthony M Guerrero
  Info
• 3 points

SECURITY ANALYSIS

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

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• Instructor: Mario Gonzalez-Corzo
  h-index: 8 Info
• 3 points

ESG ALIGNED CORPORATE GOVERNANCE

ESG-ALIGNED CORP GOVERNANCE

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

• Department: Sustainability Management
• T 6:10 p.m.-8 p.m.
• Instructor: Jessica M Wasser
  Info
• 3 points

SUSTAINABILITY IN THE FACE OF NATURAL DI

SUST IN FACE OF NATRL DIS

Natural hazards, naturally occurring phenomena, which can lead to great damage and loss of life, pose a great challenge for the sustainability of communities around the world. This course aims to prepare students to tackle specific hazards relevant to their life and work by providing them the scientific background and knowledge of the environmental factors that combine to produce natural disasters. The course will also train students about the methods used to study certain aspects of natural hazards and strategies for assessing risk and preparing communities and businesses for natural disasters. The course will cover a range of natural hazards, including geological, hydro-meteorological, and biological. The course will emphasize the driving physical, chemical and biological processes controlling the various hazards, and the observation and modeling methods used by scientists to assess and monitor events. Many case examples, including hurricanes, earthquakes and volcanic eruptions that occurred in the last five years, will be given and analyzed for the characteristics of the event, the preparation, and the response.

By providing students with a solid understanding of past natural disasters, the course prepares them to think more critically about creating more resilient communities, which can resist catastrophic events. Students will be studying the underpinning scientific principles of natural disasters but will also learn specific strategies for planning, mitigation, and response. During the course, students will master cutting-edge tools and technologies that will prepare them to work in the complex and demanding field of disaster management. After completing the course, students will be able to understand past events, communicate risk, and make critical decision related to disaster and preparedness. In increasingly unpredictable times, there is a need for more resilient and connected communities, and this particular course will train students in both the knowledge and skills needed to lead and strengthen those communities and resilience efforts at scale.

Advising Note:
Students are expected to have taken college-level Calculus, Physics, and Introductory Statistics. Students are expected to have experience with computer based data analysis (Excel, R, Matlab or Python).

• Department: Sustainability Science
• R 4:10 p.m.-6 p.m.
• Instructor: Einat Lev
  h-index: 16 Info
• 3 points

CELL BIOLOGY

• Department: Biological Sciences
• MW 10:10 a.m.-11:25 a.m.
• Instructor: Erin L Barnhart
  Info
• 3 points

SUMA PS5046 Critical ESG: Navigating Opp

This course critically examines the growing opposition to Environmental, Social, and Governance (ESG) frameworks in the United States and globally. Students will explore the historical evolution of sustainability, the political, economic, and cultural dimensions of ESG backlash, and how this impacts ESG integration in business, policy, and investment. The objective is to prepare students to engage productively with critiques of ESG in their professional careers, whether in academia, government, or the private sector.

Through an open-discussion format, case studies, and engagement with perspectives from both ESG proponents and skeptics, students will refine their ability to articulate ESG-related arguments, navigate controversy, and develop pragmatic strategies for sustainability leadership in an increasingly polarized landscape.

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

Program Evaluation

Political campaign managers, policymakers, lobbying firms, advocacy organizations, and other professionals operating in the political arena need to be able to distinguish effective programs from ineffectual ones. Electoral campaigns, policy-making initiatives, advocacy efforts, lobbying operations, social movement activities, and media investigations can all be assessed through a program evaluation lens, enabling improved data-based decision-making regarding whether an existing program should be continued, expanded, enhanced, or discontinued. Program evaluation techniques can also be used to assess the potential impact of new programs and to improve the effectiveness of program administration. This course focuses on methods for evaluating program designs, evidence collection, analysis, and interpretation, frameworks for decision-making, and reporting and communicating findings. Students will build upon the foundational knowledge that was established during the Strategic Thinking course and develop practical skills related to various types of program evaluation.

• Department: Political Analytics
• R 6:10 p.m.-8 p.m.
• Instructor: Michael Schwam-Baird
  Info
• 2 points

INTERNSHIP IN SOCIAL RESEARCH

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

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

CRITICAL URBAN INFRASTRUCTURE FOR SUSTAI

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

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

INTERNSHIP IN SOCIAL RESEARCH II

INTERNSHIP IN SOCIAL RSRC

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

SOCIOLOGICAL THEORY

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

• Department: Sociology
• W 4:10 p.m.-6 p.m.
• Instructor: Peter Bearman
  CULPA: 19 Wikipedia h-index: 54 Info
• 4 points

PRACTICUM IN DATA ANALYSIS

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

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

PRACTICUM IN DATA ANALYSIS II

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

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

internships:

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

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

• conducting basic and advanced analysis of big data; and

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

like dashboards, reports, interactive graphics, or apps.

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

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

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

INTERNSHIP IN SOCIAL III

INTERNSHP III

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

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

PRACTICUM IN DATA ANALYSIS

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

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

PRACTICUM IN DATA ANALYSIS

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

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

DATA MINING FOR SOCIAL SCIENCE

The class is roughly divided into three parts: 1) programming best practices and exploratory data analysis (EDA); 2) supervised learning including regression and classification methods and 3) unsupervised learning and clustering methods. In the first part of the course we will focus writing R programs in the context of simulations, data wrangling, and EDA. Supervised learning deals with prediction problems where the outcome variable is known such as predicting a price of a house in a certain neighborhood or an outcome of a congressional race. The section on unsupervised learning is focused on problems where the outcome variable is not known and the goal of the analysis is to find hidden structure in data such as different market segments from buying patterns or human population structure from genetics data.

• Department: Quantitative Methods/Social Sciences
• 4 points

SUSTAINABLE FASHION AND STARTUP STRATEGY

SUST FASHION & STARTUP ST

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

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

STATISTICS, DATA ANALYSIS, AND CODING FO

STATS, DATA, ANLYS&CODING

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.

• Department: Sustainability Science
• W 6:10 p.m.-8 p.m.
• Instructor: James L Davis
  h-index: 57 Info
• 3 points

PROSEMINAR I

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

• Department: Sociology
• R 4:10 p.m.-6 p.m.
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• 1 points

FIELD WORK I

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

• Department: Sociology
• F 8:10 a.m.-10 a.m.
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• 1 points

THESIS SEMINAR I

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

• Department: Sociology
• T 2:10 p.m.-4 p.m.
• R 10:10 a.m.-noon
• Instructor: Denise Milstein
  CULPA: 9 h-index: 5 Info
• Instructor: Adam Reich
  CULPA: 7 Info
• 3 points

NATURAL LANG PROCESSING SOCIAL SCIENCES

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

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

THE COMPELLING COMMUNICATOR

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

• Department: Strategic Communication
• T 10:10 a.m.-noon
• T 2:10 p.m.-4 p.m.
• T 10:10 a.m.-noon
• Instructor: Kevin Visconti
  Info
• 3 points

GIS & SPATIAL ANALYSIS-SOC SCI

Prerequisites: Undergraduate Statistics This course introduces students to basic spatial analytic skills. It covers introductory concepts and tools in Geographic Information Systems (GIS) and database management. As well, the course introduces students to the process of developing and writing an original spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well.

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

MODERN DATA STRUCTURES

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

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

MACHINE LEARNING SOC SCI

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

• Department: Quantitative Methods/Social Sciences
• T 6:10 p.m.-8 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Mazen Asaad
  Info
• Instructor: Hasan Aljabbouli
  Info
• 3 points

PROJECTS IN ADVANCED MACHINE LEARNING

ADV MACHINE LEARNING

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

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

ORAL HISTORY INTERNSHIP

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

VIDEO PRODUCTION FOR VISUAL STORYTELLING

VIDEO PRO FOR VISUAL STOR

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

• Department: Oral History
• W 3:10 p.m.-6 p.m.
• Instructor: Oscar Frasser
  Info
• 4 points

APPLIED ANALYTICS IN ORGANIZATIONAL CONT

APP AN IN ORG CONTEXT

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

ESTIMATING PROJECT COSTS

• Department: Construction Administration
• W 4:10 p.m.-6 p.m.
• Instructor: Jason I Levine
  Info
• 3 points

VALUE-BASED ENTERPRISE RISK MANAGEMENT

VALUE-BASED ERM

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

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