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.
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.
This course introduces students to important conversations within and about oral history through a series of curated public events.
This semester our events will explore how oral history lives in places and communities around New York City through a series of place-based events, curated in collaboration with partners around the city.
We meet approximately 6 times/semester.
This 1.5-credit onsite graduate course examines the ethical dynamics shaping contemporary technology management as innovation, regulation, and societal expectations rapidly evolve. Drawing on globally recognized perspectives and frameworks in responsible AI leadership and governance, the course explores how ethical considerations inform strategic decision-making across the technology lifecycle, from human-centered design to enterprise-scale digital transformation. Students critically analyze emerging challenges in artificial intelligence, data stewardship, collective decision-making systems, and value alignment, with attention to governance models, institutional accountability, and evolving regulatory frameworks. An emphasis is placed on evaluating the economic, social, and sustainable implications of technologies to strengthen ethical leadership discernment and strategic risk evaluation. The course equips students to critically navigate the ethical complexities of technological progress while aligning innovation with institutional values, societal responsibility, and long-term organizational integrity in a rapidly evolving global landscape.
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.
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.
This is 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 the R programming language in computer-based in-class exercises and homework assignments.
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
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.
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.
This course examines the science behind our understanding of how and why forests are responding to changing climatic baseline conditions, and how this can inform management, mitigation and restoration efforts going forward. We will study how climate change affects forest structure, function, biodiversity, and ecosystem services, and delve into the emerging field of climate-smart forestry. Students will integrate theory with hands-on field methods in a pair of 1-hectare forest plots, where they will learn how to measure, analyze, and interpret climate impacts at local scales.
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.
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.
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.
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.
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.
Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Prerequisites: some familiarity with the basic principles of partial differential equations, probability and stochastic processes, and of mathematical finance as provided, e.g. in MATH W5010. Review of the basic numerical methods for partial differential equations, variational inequalities and free-boundary problems. Numerical methods for solving stochastic differential equations; random number generation, Monte Carlo techniques for evaluating path-integrals, numerical techniques for the valuation of American, path-dependent and barrier options.
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 provides an overview of the science related to observing and understanding sea-level rise, which has a profound impact on the sustainability of coastal cities and ecosystems. In modern research, sea-level rise is viewed as a complex response of the Earth “system of systems” to climate change. Measuring ongoing sea-level change is challenging due to the great natural variability of sea level on short time scales caused by tides, weather, and ocean currents. Interpreting measurements so that one can assess (and mitigate against) potential economic and societal impacts of sea-level rise is crucial but can be complicated, since so many Earth-system processes play a role. Some of these processes are related and others are unrelated to climate change; some of the latter are natural and others are of anthropogenic origin. Students enrolled in this course will through lectures and class discussions address topics related to the underlying observational basis for sea-level rise. Given the complexity of sea level rise, it is important for those in technical positions to understand the systems level interactions that not only lead to rising waters but also the consequences that these changes inflict on other parts of our environment. What we hear most commonly is that sea level rise will affect hundreds of millions of people living in coastal areas and make those populations susceptible to flooding. But in addition to this community effect, sea level rise also have dramatic effects on coastal habitats, leading to issue such as erosion, soil contamination, and wetland flooding, just to name a few. This course will introduce and prepare students to develop a more comprehensive and holistic approach to sea level rise. By training students to observe, measure, interpret, and begin to predict how sea level rise affects populations and communities differently, students will be in strong positions to address, mitigate, and adapt to the challenges more effectively using evidence-based approaches.
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.
Grad section for FILM UN 2190 Topics in American Cinema.
This course surveys the American film genre known as film noir, focusing primarily on the genre’s heyday in the 1940s and early 1950s, taking into account some of its antecedents in the hard-boiled detective novel, German Expressionism, and the gangster film, among other sources. We will consider a number of critical and theoretical approaches to the genre, and will also study a number of film noir adaptations and their literary sources.
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.
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).
This seminar offers participants the opportunity to listen to practitioners discuss a range of important topics in the financial industry. Topics may include portfolio optimization, exotic derivatives, high frequency analysis of data and numerical methods. While most talks require knowledge of mathematical methods in finance, some talks are accessible to a more general audience.
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.
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.
Fashion’s consistent ranking among the top 3 global polluters has become a decades old fact struggling to gain a proportionate response among the brand startup and sourcing community. With industry revenues set to exceed $1 trillion, there is an opportunity to critically address existing revenue models predicated on traditional metrics, such as constant growth, and singular bottom lines. The course attempts to create a nexus between the fashion entrepreneur and systems thinker to explore strategic solutions that address sustainability though an environmental, social and economic lens. The aim is to foster a mindful, yet critical discourse on fashion industry initiatives, past and present, and to practice various tools that help transition existing organizations and incubate new startups towards sustainable outcomes.
Students in the Master of Science in Sustainability Science program will encounter a range of scientific problems throughout their Science-specific courses that require a strong working knowledge of computer programming. This course provides an introduction to scientific programming using Python. Computer coding skills gained in the course will prepare students for coursework in the Master of Science in Sustainability Science program as well as to succeed in a career having a programming component. Students enrolled in this course will learn through lectures, class discussion, and hands-on exercises that address the following topics:
Basics of computer programming, including precision of variables, arrays and data structures, input/output, control flow, and subroutines.
Applying Python to read common scientific data formats, including NetCDF for gridded climate and other environmental data.
Applying Python for data analysis, with a focus on popular machine learning methods including linear regression, decision trees, neural networks, principal component analysis, and clustering.
Applying Python to visualize scientific data through basic X-Y plots as well as images of data fields on a global map.
This course will train students to analyze and model scientific data using Python in order to better understand current and future environments and their interactions with human systems. By learning analysis and modeling with Python, students will be better able to inform sustainability policy, management, and decision-making.
The Proseminar fulfills two separate goals within the Free-Standing Masters Program in Sociology. The first is to provide exposure, training, and support specific to the needs of Masters students preparing to move on to further graduate training or the job market. The second goal is to provide a forum for scholars and others working in qualitative reserach, public sociology, and the urban environment.
This two-semester sequence supports students through the process of finding a fieldwork site, beginning the field work required to plan for and develop a Masters thesis, and the completion of their Masters thesis.
This seminar gives you an opportunity to do original sociological research with the support of a faculty member, a teaching assistant, and your fellow classmates.
Social scientists need to engage with natural language processing (NLP) approaches that are found in computer science, engineering, AI, tech and in industry. This course will provide an overview of natural language processing as it is applied in a number of domains. The goal is to gain familiarity with a number of critical topics and techniques that use text as data, and then to see how those NLP techniques can be used to produce social science research and insights. This course will be hands-on, with several large-scale exercises. The course will start with an introduction to Python and associated key NLP packages and github. The course will then cover topics like language modeling; part of speech tagging; parsing; information extraction; tokenizing; topic modeling; machine translation; sentiment analysis; summarization; supervised machine learning; and hidden Markov models. Prerequisites are basic probability and statistics, basic linear algebra and calculus. The course will use Python, and so if students have programmed in at least one software language, that will make it easier to keep up with the course.
The ability to communicate effectively is a key competency for professionals. As emerging industry leaders, understanding the audience, framing the message, and using media channels to achieve specified objectives are critical skills, whether written or spoken. Through a variety of written and oral assignments, students learn to apply foundational communication theory to inform and engage stakeholders. The first part of the course focuses on written deliverables, emphasizing audience-framed messaging and developing simple, clear and persuasive content. The second part transitions to enhancing spoken delivery and presentation skills where students gain experience in speechwriting, storytelling and using data visualization to motivate an audience to act.
Prerequisites: Undergraduate Statistics This course introduces students to basic spatial analytic skills. It covers introductory concepts and tools in Geographic Information Systems (GIS) and database management. As well, the course introduces students to the process of developing and writing an original spatial research project. Topics to be covered include: social theories involving space, place and reflexive relationships; social demography concepts and databases; visualizing social data using geographic information systems; exploratory spatial data analysis of social data and spatially weighted regression models, spatial regression models of social data, and space-time models. Use of open-source software (primarily the R software package) will be taught as well.
This course is intended to provide a detailed tour on how to access, clean, “munge” and organize data, both big and small. (It should also give students a flavor of what would be expected of them in a typical data science interview.) Each week will have simple, moderate and complex examples in class, with code to follow. Students will then practice additional exercises at home. The end point of each project would be to get the data organized and cleaned enough so that it is in a data-frame, ready for subsequent analysis and graphing. Therefore, no analysis or visualization (beyond just basic tables and plots to make sure everything was correctly organized) will be taught; and this will free up substantial time for the “nitty-gritty” of all of this data wrangling.
Prerequisites: basic probability and statistics, basic linear algebra, and calculus This course will provide a comprehensive overview of machine learning as it is applied in a number of domains. Comparisons and contrasts will be drawn between this machine learning approach and more traditional regression-based approaches used in the social sciences. Emphasis will also be placed on opportunities to synthesize these two approaches. The course will start with an introduction to Python, the scikit-learn package and GitHub. After that, there will be some discussion of data exploration, visualization in matplotlib, preprocessing, feature engineering, variable imputation, and feature selection. Supervised learning methods will be considered, including OLS models, linear models for classification, support vector machines, decision trees and random forests, and gradient boosting. Calibration, model evaluation and strategies for dealing with imbalanced datasets, n on-negative matrix factorization, and outlier detection will be considered next. This will be followed by unsupervised techniques: PCA, discriminant analysis, manifold learning, clustering, mixture models, cluster evaluation. Lastly, we will consider neural networks, convolutional neural networks for image classification and recurrent neural networks. This course will primarily us Python. Previous programming experience will be helpful but not requisite. Prerequisites: basic probability and statistics, basic linear algebra, and calculus.
Machine learning algorithms continue to advance in their capacity to predict outcomes and rival human judgment in a variety of settings. This course is designed to offer insight into advanced machine learning models, including Deep Learning, Recurrent Neural Networks, Adversarial Neural Networks, Time Series models and others. Students are expected to have familiarity with using Python, the scikit-learn package, and github. The other half of the course will be devoted to students working in key substantive areas, where advanced machine learning will prove helpful -- areas like computer vision and images, text and natural language processing, and tabular data. Students will be tasked to develop team projects in these areas and they will develop a public portfolio of three (or four) meaningful projects. By the end of the course, students will be able to show their work by launching their models in live REST APIs and web-applications.
Effective leaders are able to think critically about problems and opportunities, imagine unexpected futures, craft a compelling vision, and drive change. In this course, we study the theoretical underpinnings of leadership communication, relying on empirical evidence as a guide for practice. Students gain important perspective on leadership styles, mastering the competencies required for a variety of contexts.
The “Quantum Physics Lab” will give students in the Quantum Science and Technology Masters program hands-on experience in quantum physics and its applications. Students will work in small groups on several distinct experiments through the semester. Each experimental project might last for 3-4 weeks, comprising the steps outlined in the Program below. Initial experimental offerings include: a quantum optics (entangled photon) platform, a Josephson junction experiment, a nitrogen vacancy (NV) center for direct manipulation of quantum states, along with experiments on nuclear magnetic resonance, quantum conductance and the quantum Hall effect. We expect to add additional experiments in the near future.
Students will observe and measure fundamental quantum behaviors, reinforcing material they are learning in the Masters lecture courses, while simultaneously being introduced to forefront technology that will be the basis of the second “quantum revolution” that could eventually lead to revolutionary applications in electronics, computing, energy technology and medical devices.
Program:
1 x 230 min lab meeting per week (small group work)
Background research on selected experiments, and associated physics and instrumentation
Data analysis, discussions with instructor and teaching assistants
Project writeups and presentations
Digital media opens new opportunities for increasingly targeted communications across a variety of channels, which rapidly expands the importance of analytics in tracking and measuring key performance indicators (KPIs). This course prepares students to work within data- and model-driven environments with an emphasis on using analytics to develop insights and support strategic decisions.
Students will have hands-on learning experiences using camera controls and techniques and optics to accentuate psychological and atmospheric aspects surrounding the subject. Additionally, through visual storytelling, composition and basic color theory students will understand how to incorporate theories of cinematic language to emphasize the mood and perception of the story. This course will cover basic lighting techniques for the interview in a hands-on practical experience that will strengthen participants’ camera, cinematography and storytelling skills. Students will complete the course by creating a final short video, having collaboratively conceptualized, filmed, interviewed and shot the necessary B-roll to structure a basic visual storytelling piece with the use of image, sound and basic editing.