GLOBAL INDIGENOUS RELIGIOUS HISTORIES
GLOBAL INDIGENOUS RELIG H
Nomads, natives, peasants, hill people, aboriginals, hunter-gatherers, First Nations—these
are just a handful of the terms in use to define indigenous peoples globally. The names these groups
use to describe themselves, as well as the varying religious practices, attitudes, and beliefs among
these populations are far more numerous and complex. For much of recorded history however,
colonial centers of power have defined indigenous peoples racially and often in terms of lacking
religion; as pagan, barbarian, non-modern, and without history or civilization.
Despite this conundrum of identity and classification, indigenous religious traditions often
have well-documented and observable pasts. This course considers the challenges associated with
studying indigenous religious history, as well as the changing social, political, and legal dimensions
of religious practice among native groups over time and in relationship to the state. Organized
thematically and geographically, we will engage with classic works of ethnohistory, environmental
history, indigenous studies, anthropology, and religious studies as well as primary sources that
include legal documentation, military records, personal testimony, and oral narrative.
- Department: Religion @Barnard
- W 10:10 a.m.-noon
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Instructor:
Tiffany Hale
CULPA: 4
Info
- 4 points
MA THESIS
This course will enable students to complete a research study of considerable length that will (i) enable them to explore a given area of research in substantive detail; (ii) put them on the path to true competence as independent researchers; and (iii) provide those who go on to apply to PhD programs with a substantial writing sample that shows off their technical abilities to the best advantage.
- Department: Classics
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Instructor:
Elizabeth K Irwin
CULPA: 7
Info
- 4 points
RES SKILLS/MTHDS-INT/WRLD HIST
In the first semester a series of workshops will introduce the field of international history and various research skills and methods such as conceptualization of research projects and use of oral sources. The fall sessions will also show the digital resources available at Columbia and how students can deploy them in their individual projects. In the second semester students will apply the skills acquired in the fall as they develop their proposal for the Master's thesis, which is to be completed next year at the LSE. The proposal identifies a significant historical question, the relevant primary and secondary sources, an appropriate methodology, what preliminary research has been done and what remains to be done. Students will present their work-in-progress.
- Department: History
- F 12:10 p.m.-2 p.m.
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Instructor:
Line Lillevik
Info - 1 points
THEORY AND METHODS I
This course will be the first part of a two part introduction to theoretical approaches to modern social science and cultural studies in Asian and African contexts. The first course will focus primarily on methodological and theoretical problems in the fields broadly described as historical social sciences - which study historical trends, and political, economic and social institutions and processes. The course will start with discussions regarding the origins of the modern social sciences and the disputes about the nature of social science knowledge. In the next section it will focus on definitions and debates about the concept of modernity. It will go on to analyses of some fundamental concepts used in modern social and historical analyses: concepts of social action, political concepts like state, power, hegemony, democracy, nationalism; economic concepts like the economy, labor, market, capitalism, and related concepts of secularity/secularism, representation, and identity. The teaching will be primarily through close reading of set texts, followed by a discussion. A primary concern of the course will be to think about problems specific to the societies studied by scholars of Asia and Africa: how to use a conceptual language originally stemming from reflection on European modernity in thinking about societies which have quite different historical and cultural characteristics.
- Department: Middle Eastern, South Asian and African Studies
- M 4:10 p.m.-6 p.m.
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Instructor:
Wael Hallaq
CULPA: 9
Wikipedia
Info
- 4 points
GRADUATE RESEARCH COLLOQUIUM
The Graduate Research Colloquium is a forum that offers two types of research seminars over the course of the semester. In the first, formerly the Graduate Colloquium, up to six outside speakers are invited by the graduate organizers to present research papers to an audience of graduates, faculty and others interested within the larger NYC Classics community, and afterwards to engage in discussion. The second is a Work-in-Progress seminar in which Columbia Classics graduate students present their research to their graduate peers in whatever format they deem most conducive to conveying their research to their audience and receiving feedback. The audience for these eight seminars is restricted to graduate students, the instructor who presides over the course, and any faculty the graduate student presenters choose should choose to invite. At least one semester of the Graduate Research Colloquium is required for MAO students and PhD students must attend the course in both the Fall and Spring semesters of their first year.
- Department: Classics
- F 2:10 p.m.-4 p.m.
- TF 4:10 p.m.-6 p.m.
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Instructor:
Paraskevi Martzavou
CULPA: 2 Info -
Instructor:
Katharina Volk
CULPA: 7
Info
- 2 points
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.
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Instructor:
Mikhail Smirnov
CULPA: 16 Info - 3 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.
- F 10:10 a.m.-noon
- 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.
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Instructor:
Maiko Yomogida
Info -
Instructor:
Jingyan Yang
h-index: 19 Info -
Instructor:
Gregory M Eirich
CULPA: 1 Info -
Instructor:
Mike Z He
Info -
Instructor:
Kenneth Grossberger
Info -
Instructor:
Gregory A Kossinets
Info -
Instructor:
Emma Sacks
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 12:10 p.m.-2 p.m.
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 3 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
- M 10:10 a.m.-noon
- R 10:10 a.m.-noon
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Instructor:
Gregory M Eirich
CULPA: 1 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
- W 6:10 p.m.-8 p.m.
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 3 points
RES SEM IN QUANT MTHS-SOC SCI 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 6:10 p.m.-8 p.m.
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Instructor:
Sandhya Kajeepeta
Info - 3 points
DEVELOPMENTAL BIOLOGY
- Department: Biological Sciences
- TR 1:10 p.m.-2:25 p.m.
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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
- W 6:10 p.m.-8 p.m.
- R 6:10 p.m.-8 p.m.
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Instructor:
Gregory M Eirich
CULPA: 1 Info -
Instructor:
Sandhya Kajeepeta
Info - 3 points
NUMERICAL METHODS IN FINANCE
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.
- Department: Mathematics
- M 6:10 p.m.-8:40 p.m.
- 3 points
HLTH PROM/DISEASE PREVNT CLRKSHIP I
- Department: Dental and Oral Surgery
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Instructor:
Folarin Odusola
h-index: 5 Info - 0 points
CELL BIOLOGY
- Department: Biological Sciences
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Erin L Barnhart
Info - 3 points
MATH FINANCE PRACTITIONERS SEMINAR I
MATH FINANCE PRACTITION S
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.
- Department: Mathematics
- W 6:10 p.m.-7:25 p.m.
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Instructor:
Jaehyuk Choi
Info - 1.5 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
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 1 points
INTERNSHIP IN SOCIAL RESEARCH II
INTERNSHIP IN SOCIAL RSRC
- Department: Quantitative Methods/Social Sciences
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 1 points
SOCIOLOGICAL THEORY
- Department: Sociology
- W 2:10 p.m.-4 p.m.
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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.
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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.
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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.
- R 6:10 p.m.-8 p.m.
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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
THESIS SEMINAR I
- Department: Sociology
- T 2:10 p.m.-4 p.m.
- R 4:10 p.m.-6 p.m.
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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.
- T 6:10 p.m.-8 p.m.
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Instructor:
Patrick Houlihan
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.
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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.
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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.
- F 2:10 p.m.-4 p.m.
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Instructor:
Mazen Asaad
Info -
Instructor:
Kyle Horn
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.
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Instructor:
Alexander Peterhansl
Info - 3 points
Molecular Mechanisms
MOLECULAR MECHANISM HLTH/DISEA
- Department: Dental and Oral Surgery
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Instructor:
Jonathan M Barasch
Info - 0 points
Settler Colonialism
This course examines the relationship between colonialism, settlement and anthropology and the specific ways in which these processes have been engaged in the broader literature and locally in North America. We aim to understand colonialism as a theory of political legitimacy, as a set of governmental practices and as a subject of inquiry. Thus, we will re-imagine North America in light of the colonial project and its technologies of rule such as education, law and policy that worked to transform Indigenous notions of gender, property and territory. Our case studies will dwell in several specific areas of inquiry, among them: the Indian Act in Canada and its transformations of gender relations, governance and property; the residential and boarding school systems in the US and Canada, the murdered and missing women in Juarez and Canada and the politics of allotment in the US. Although this course will be comparative in scope, it will be grounded heavily within the literature from Native North America.
- Department: Anthropology
- F 12:10 p.m.-2 p.m.
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Instructor:
Audra Simpson
CULPA: 5
Wikipedia
Info
- 4 points
Essentials for Technology Management and
Essentials for Tech Mgmt.
This 3-credit core course in the M.S. in Technology Management program provides an overview of the strategic role of the technology function to improve business processes, drive transformations, and fuel innovation. Through lectures and applied case study work, students will learn how to develop and keep technology strategies aligned with business goals, navigate governance, regulatory, and budgetary frameworks, and evaluate risks to protect the organization’s IT investments.
- Department: Technology Management
-
Instructor:
Shahryar Shaghaghi
Info - 3 points
ELEMENTS LATIN PROSE STYLE
Prerequisites: at least four semesters of Latin, or the equivalent. Intensive review of Latin syntax with translation of English sentences and paragraphs into Latin.
- Department: Classics @Barnard
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Kristina Milnor
CULPA: 21
Wikipedia
h-index: 10
Info
- 4 points
WORKSHOP ON POPULATION ORAL HEALTH
WRKSHP ON POPULATION ORAL HLTH
- Department: Dental and Oral Surgery
- 0 points
PRIN/APPL OF SOCIETY & CULTURE
Prerequisites: graduate standing. Introductory survey of major concepts and areas of research in social and cultural anthropology. Emphasis is on both the field as it is currently constituted and its relationship to other scholarly and professional disciplines. Required for students in Anthropology Department's master degree program and for students in the graduate programs of other departments and professional schools desiring an introduction in this field.
- Department: Anthropology
- T 2:10 p.m.-4 p.m.
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Instructor:
Ellen Marakowitz
CULPA: 1 Info - 3 points
QUANT MTHDS IN INVESTMENT MGMT
Prerequisites: Knowledge of statistics basics and programming skills in any programming language. Surveys the field of quantitative investment strategies from a buy side perspective, through the eyes of portfolio managers, analysts and investors. Financial modeling there often involves avoiding complexity in favor of simplicity and practical compromise. All necessary material scattered in finance, computer science and statistics is combined into a project-based curriculum, which give students hands-on experience to solve real world problems in portfolio management. Students will work with market and historical data to develop and test trading and risk management strategies. Programming projects are required to complete this course.
- Department: Mathematics
- R 7:40 p.m.-10 p.m.
- 3 points
CAPITAL MARKETS & INVESTMENTS
Risk/return tradeoff, diversification and their role in the modern portfolio theory, their consequences for asset allocation, portfilio optimization. Capitol Asset Pricing Model, Modern Portfolio Theory, Factor Models, Equities Valuation, definition and treatment of futures, options and fixed income securities will be covered.
- Department: Mathematics
- S 7 p.m.-9:20 p.m.
- 3 points
HEDGE FUNDS STRATEGIES & RISK
The hedge fund industry has continued to grow after the financial crisis, and hedge funds are increasingly important as an investable asset class for institutional investors as well as wealthy individuals. This course will cover hedge funds from the point of view of portfolio managers and investors. We will analyze a number of hedge fund trading strategies, including fixed income arbitrage, global macro, and various equities strategies, with a strong focus on quantitative strategies. We distinguish hedge fund managers from other asset managers, and discuss issues such as fees and incentives, liquidity, performance evaluation, and risk management. We also discuss career development in the hedge fund context.
- Department: Mathematics
- T 8:10 p.m.-10 p.m.
- 3 points
PRE-CLINICAL OPPERATIVE DENT
PRE-CLINICAL OP DENT
- Department: Dental and Oral Surgery
- 0 points
PRECLINICAL OPERATIVE DENTISTRY LAB
PRECLIN OPERTV DENT LAB
- Department: Dental and Oral Surgery
-
Instructor:
Roseanna Graham
h-index: 4 Info - 0 points
Oral Health Considerations for Patients
Special Health Care Needs
- Department: Dental and Oral Surgery
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Instructor:
Emily R Byington
Info - 0 points
Oral Health Considerations for Patients
Special Health Care Needs
- Department: Dental and Oral Surgery
- 0 points
ETHICAL ISSUES IN MUSEUMS
Ethical questions about museum activities are legion, yet they are usually only discussed when they become headlines in newspapers. At the same time, people working in museums make decisions with ethical and legal issues regularly and seldom give these judgments even little thought. In part, this is due to the fact that many of these decisions are based upon values that become second nature. This course will explore ethical issues that arise in all areas of a museum's operations from governance and management to collections acquisition, conservation, and deaccessioning. We will examine the issues that arise when the ownership of objects in a museum's are questioned; the ethical considerations involved in retention, restitution and repatriation; and what decolonization means for museums.
- Department: Anthropology
- F 2:10 p.m.-4 p.m.
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Instructor:
Sally Yerkovich
Info - 3 points
INSURANCE RISK MANAGEMENT
Review of types of insurance risk, such as pricing risk, underwriting risk, reserving risk, etc. Includes case studies, risk quantification methods (e.g., market-consistent economic capital models, dynamic financial analysis (DFA) models, catastrophe models, etc.), and common mitigation techniques, such as asset-liability management (ALM), reinsurance, etc. Also addresses traditional risk management at insurance companies and ERM actuarial standards of practice (ASOPs).
- Department: Enterprise Risk Management
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Instructor:
Rich Lauria
Info - 3 points
CAPSTONE
Capstone projects afford a group of students the opportunity to undertake complex, real-world, client-based projects for nonprofit organizations, supervised by a Nonprofit Management program faculty member. Through the semester-long capstone project, students will experience the process of organizational assimilation and integration as they tackle a discrete management project of long or short-term benefit to the client organization. The larger theoretical issues that affect nonprofit managers and their relationships with other stakeholders, both internal and external, will also be discussed within the context of this project-based course.
- Department: Non Profit Management
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Instructor:
Basil A Jr Smikle
Info - 3 points
NON-LINEAR OPTION PRICING
Prerequisites: familiarity with Brownian motion, Itô's formula, stochastic differential equations, and Black-Scholes option pricing. Prerequisites: Familiarity with Brownian motion, Itô's formula, stochastic differential equations, and Black-Scholes option pricing. Nonlinear Option Pricing is a major and popular theme of research today in quantitative finance, covering a wide variety of topics such as American option pricing, uncertain volatility, uncertain mortality, different rates for borrowing and lending, calibration of models to market smiles, credit valuation adjustment (CVA), transaction costs, illiquid markets, super-replication under delta and gamma constraints, etc. The objective of this course is twofold: (1) introduce some nonlinear aspects of quantitative finance, and (2) present and compare various numerical methods for solving high-dimensional nonlinear problems arising in option pricing.
- Department: Mathematics
- F 6 p.m.-8:10 p.m.
- 3 points
MODEL & TRADE DERIVATIVES
Required Prerequisite: Math GR5010 Intro to the Math of Finance (or equivalent). Recommended Prerequisite: Stat GR5264 Stochastic Processes – Applications I (or equivalent).
The objective of this course is to introduce students, from a practitioner’s perspective with formal derivations, to the advanced modeling, pricing and risk management techniques of vanilla and exotic options that are traded on derivatives desks, which goes beyond the classical option pricing courses focusing solely on the theory. It also presents the opportunity to design, implement and backtest vol trading strategies. The course is divided in four parts: Advanced Volatility Modeling; Vanilla and Exotic Options: Structuring, Pricing and Hedging; FX/Rates Components: Discounting, Forward Projection, Quanto and Compo Options; Designing and Backtesting Vol Trading Strategies in Python.
- Department: Mathematics
- S 10:10 a.m.-noon
- 3 points
Machine Learning for Finance
The application of Machine Learning (ML) algorithms in the Financial industry is now commonplace, but still nascent in its potential. This course provides an overview of ML applications for finance use cases including trading, investment management, and consumer banking.
Students will learn how to work with financial data and how to apply ML algorithms using the data. In addition to providing an overview of the most commonly used ML models, we will detail the regression, KNN, NLP, and time series deep learning ML models using desktop and cloud technologies.
The course is taught in Python using Numpy, Pandas, scikit-learn and other libraries. Basic programming knowledge in any language is required.
- Department: Mathematics
- T 12:10 p.m.-2 p.m.
- 3 points
Supervised Research
Students conduct research related to biotechnology under the sponsorship of a mentor within the University. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
Supervised Research
Students conduct research related to biotechnology under the sponsorship of a mentor within the University. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
Introduction to Medieval Manuscript Stud
Intro to Medieval Manuscr
- Department: Medieval and Renaissance Studies
- F 10 a.m.-1 p.m.
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Instructor:
Emily Runde
Info - 4 points
Supervised Independent Research
Students conduct research related to biotechnology under the sponsorship of a mentor outside the University within the New York City Metropolitan Area unless otherwise approved by the Program. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
Supervised Independent Research
Students conduct research related to biotechnology under the sponsorship of a mentor outside the University within the New York City Metropolitan Area unless otherwise approved by the Program. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
MAFN FIELDWORK
Prerequisites: all 6 MAFN core courses, at least 6 credits of approved electives, and the instructors permission. See the MAFN website for details. This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.
- Department: Mathematics
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Instructor:
Jaehyuk Choi
Info - 1-3 points
Career Development for Quantitative Fina
Career Develop for Quant
This course helps the students understand the job search process and develop the professional skills necessary for career advancement. The students will not only learn the best practices in all aspects of job-seeking but will also have a chance to practice their skills. Each class will be divided into two parts: a lecture and a workshop.
In addition, the students will get support from Teaching Assistants who will be available to guide and prepare the students for technical interviews.
- Department: Mathematics
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Jaehyuk Choi
Info - 0 points
Topics in Mathematical Finance
The purpose of this course is for MA in Mathematics of Finance students to gain knowledge and practical skills that are essential in the finance industry. The course will run as a series of lectures and discussions on various relevant topics, such as business communications and career talks that may feature guest speakers from the industry as well as the full-time faculty members. This will prepare the students for their job search, networking, and in their industry jobs in the future.
- Department: Mathematics
- TR 6:10 p.m.-7:25 p.m.
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Instructor:
Jaehyuk Choi
Info - 0 points
Perspectives on International and World
Persptvs on Int'l & World
This course gives students the opportunity to design their own curriculum: To attend lectures, conferences and workshops on historical topics related to their individual interests throughout Columbia University. Students may attend events of their choice, and are especially encouraged to attend those sponsored by the History Department (www.history.columbia.edu). (The Center for International History - cih.columbia.edu - and the Heyman Center for the Humanities - heymancenter.org/events/ - also have impressive calendars of events, often featuring historians.) The goal of this mini-course is to encourage students to take advantage of the many intellectual opportunities throughout the University, to gain exposure to a variety of approaches to history, and at the same time assist them in focusing on a particular area for their thesis topic.
- Department: History
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Instructor:
Line Lillevik
Info - 1-3 points
INDEPENDENT STUDY
This course offers students an opportunity to expand their curriculum beyond the established course offerings. Interested parties must consult with the QMSS Program Director before adding the class. This course may be taken for 2-4 points.
- Department: Quantitative Methods/Social Sciences
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 1-4 points
MASTER'S THESIS
This course fulfills the Masters Thesis requirement of the QMSS MA Program. It is designed to help you make consistent progress on your master’s thesis throughout the semester, as well as to provide structure during the writing process. The master’s thesis, upon completion, should answer a fundamental research question in the subject matter of your choice. It should be an academic paper based on data that you can acquire, clean, and analyze within a single semester, with an emphasis on clarity and policy relevance.
- Department: Quantitative Methods/Social Sciences
- M 4:10 p.m.-6 p.m.
- R 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Elena B Krumova
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Instructor:
Hadi Katesari
Info -
Instructor:
Maiko Yomogida
Info - 3-4 points
Intro Social Data Analysis I
- Department: Sociology
- R 10:10 a.m.-noon
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Instructor:
Yao Lu
h-index: 25 Info - 4 points
The Central Dogma: Mechanisms and Regula
THE CENTRAL DOGMA
- Department: Biological Sciences
- MW 10:10 a.m.-11:25 a.m.
- 3 points
MESAAS RESEARCH COLLOQUIUM
This course provides a structured setting for stand-alone M.A. students in their final year and Ph.D. students in their second and third years to develop their research trajectories in a way that complements normal coursework. The seminar meets approximately biweekly and focuses on topics such as research methodology; project design; literature review, including bibliographies and citation practices; grant writing. Required for MESAAS graduate students in their second and third year.
- Department: Middle Eastern, South Asian and African Studies
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Instructor:
Alison Vacca
Info - 2 points
ENVIRONMENTAL DATA ANALYSIS
- Department: Architecture, Planning and Preservation
- M 3 p.m.-5 p.m.
- 3 points
OLD RUSSIAN LITERATURE
- Department: Slavic Languages
- T 4:10 p.m.-6 p.m.
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Instructor:
Valentina Izmirlieva
CULPA: 14 Info - 4 points
INTRO TO HUMAN RIGHTS
This course provides a wide-ranging survey of conceptual foundations and issues in contemporary human rights. The course examines the philosophical origins of human rights, their explication in the evolving series of international documents, questions of enforcement, and current debates. It also explores topics such as womens rights, development and human rights, the use of torture, humanitarian intervention, and the horrors of genocide. The broad range of subjects covered in the course is intended to assist students in honing their interests and making future course selections in the human rights field.
- Department: Institute for Study of Human Rights
- W 4:10 p.m.-6 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Glenn Mitoma
Info -
Instructor:
Daniela R Ikawa
Info - 3 points