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.
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 3 points

FIFTH YEAR JAPANESE I

Prerequisites: Students must meet with the instructor prior to taking the course. This course is intended to help students increase their ability level in the four core language skills (reading, writing, listening, and speaking) from advanced to super-advanced. It serves as a bridge between mastering the overall Japanese language and using it for analysis, research, and literary criticism. This is a mandatory course for Ph.D students in Japanese Studies.

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

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
• 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.
• 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.
• Instructor: Sandhya Kajeepeta
  Info
• 3 points

DEVELOPMENTAL BIOLOGY

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

RES SEM IN QUANT MTHS-SOC SCI II

This course has two goals.  One, it is designed to expose students in the QMSS degree program to different methods and practices of social science research.  Seminar presentations are given on a wide range of topics by faculty from Columbia and other New York City universities, as well as researchers from other settings. Two, it is also designed to give students important professional development skills, particularly around academic writing, research methods and job skills.

• Department: Quantitative Methods/Social Sciences
• W 6:10 p.m.-8 p.m.
• R 6:10 p.m.-8 p.m.
• 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
• Instructor: Folarin Odusola
  h-index: 5 Info
• 0 points

PHYSICAL DIAGNOSIS

• Department: Dental and Oral Surgery
• 0 points

American Film: Comedy

TPCS IN AMER CINEMA

• Department: Film
• R 10:10 a.m.-12:55 p.m.
• Instructor: Robert King
  CULPA: 7 Info
• 3 points

CELL BIOLOGY

• Department: Biological Sciences
• MW 10:10 a.m.-11:25 a.m.
• 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.
• 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
• Instructor: Gregory M Eirich
  CULPA: 1 Info
• 1 points

INTERNSHIP IN SOCIAL RESEARCH II

INTERNSHIP IN SOCIAL RSRC

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

SOCIOLOGICAL THEORY

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

• Department: Sociology
• W 2:10 p.m.-4 p.m.
• Instructor: Peter Bearman
  CULPA: 19 Wikipedia h-index: 54 Info
• 4 points

PRACTICUM IN DATA ANALYSIS

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

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

PRACTICUM IN DATA ANALYSIS II

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

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

internships:

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

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

• conducting basic and advanced analysis of big data; and

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

like dashboards, reports, interactive graphics, or apps.

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

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

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

INTERNSHIP IN SOCIAL III

INTERNSHP III

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

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

PRACTICUM IN DATA ANALYSIS

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

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

PRACTICUM IN DATA ANALYSIS

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

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

DATA MINING FOR SOCIAL SCIENCE

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

• Department: Quantitative Methods/Social Sciences
• 4 points

PROSEMINAR I

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

• Department: Sociology
• F 2:10 p.m.-4 p.m.
• 1 points

FIELD WORK I

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

• Department: Sociology
• F 4:10 p.m.-6 p.m.
• 1 points

THESIS SEMINAR I

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

• Department: Sociology
• T 2:10 p.m.-4 p.m.
• R 4:10 p.m.-6 p.m.
• 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.
• 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.
• Instructor: Edwin Y Grimsley
  Info
• 3 points

MODERN DATA STRUCTURES

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

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

MACHINE LEARNING SOC SCI

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

• Department: Quantitative Methods/Social Sciences
• T 6:10 p.m.-8 p.m.
• F 2:10 p.m.-4 p.m.
• 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.
• Instructor: Alexander Peterhansl
  Info
• 3 points

INTRODUCTION TO HUMAN CAPITAL MANAGEMENT

• Department: Human Capital Management
• 3 points

Histology & Pathology

• Department: Dental and Oral Surgery
• 0 points

PSYCHIATRIC MEDICINE

• Department: Dental and Oral Surgery
• 0 points

Molecular Mechanisms

MOLECULAR MECHANISM HLTH/DISEA

• Department: Dental and Oral Surgery
• Instructor: Jonathan M Barasch
  Info
• 0 points

CLINICAL GROSS ANATOMY

• Department: Dental and Oral Surgery
• 0 points

THE BODY IN HEALTH & DISEASE I

• Department: Dental and Oral Surgery
• 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.
• 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.
• 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.
• 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

DENTAL ANATOMY & MORPHOLOGY

• Department: Dental and Oral Surgery
• 0 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
• 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.
• 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
• 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
• 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

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
• 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
• Instructor: Lili Yamasaki
  CULPA: 5 Info
• 3-6 points

Introduction to Medieval Manuscript Stud

Intro to Medieval Manuscr

This team-taught course introduces methods for studying medieval manuscripts through weekly hands-on instruction and assignments. Students will become acquainted with the collections of medieval manuscripts at Columbia, and will learn from the scholars at Columbia who specialize in the material study of manuscripts as artifacts as well as in types of manuscripts as defined by their textual contents. The course provides a foundation for advanced work and satisfies the material text requirement of the MA in Medieval and Renaissance Studies.

• Department: Medieval and Renaissance Studies
• F 10 a.m.-1 p.m.
• 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
• 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
• 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
• 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.
• 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.
• Instructor: Jaehyuk Choi
  Info
• 0 points

CINEMA/MEDIA HISTORIOGRAPHY

• Department: Film
• W 10:10 a.m.-12:40 p.m.
• Instructor: Jane Gaines
  CULPA: 4 Info
• 3 points

DENTAL MATERIALS I

• Department: Dental and Oral Surgery
• 0 points

RADIOLOGY I

• Department: Dental and Oral Surgery
• 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
• 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
• 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.
• Instructor: Elena B Krumova
  Info
• Instructor: Hadi Katesari
  Info
• Instructor: Maiko Yomogida
  Info
• 3-4 points

GRADUATE SPECIAL TOPIC

Health as a System

• Department: Biomedical Engineering
• W 10:10 a.m.-12:40 p.m.
• Instructor: Henry Hess
  CULPA: 1 h-index: 45 Info
• 3 points

GRADUATE SPECIAL TOPIC

SciStory:Grants,Papers,En

• Department: Biomedical Engineering
• M 2:30 p.m.-5:15 p.m.
• Instructor: Santiago Correa
  Info
• 3 points

GRADUATE SPECIAL TOPIC

Signal Proc Med Devices

• Department: Biomedical Engineering
• M 10:10 a.m.-12:40 p.m.
• Instructor: Megan Heenan
  Info
• 3 points

Intro Social Data Analysis I

Basic techniques for analyzing quantitative social science data. Emphasis on conceptual understanding as well as practical mastery of probability and probability distributions, inference, hypotheses testing, analysis of variance, simple regression, and multiple regression.

• Department: Sociology
• R 10:10 a.m.-noon
• Instructor: Yao Lu
  h-index: 25 Info
• 4 points

FOUNDATIONS FOR EXCELLENCE IN DENT II

• Department: Dental and Oral Surgery
• 0 points

BIOMEDICAL DESIGN II

• Department: Biomedical Engineering
• MW 2:10 p.m.-4 p.m.
• Instructor: Megan Heenan
  Info
• 3 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
• Instructor: Alison Vacca
  Info
• 2 points

ENVIRONMENTAL DATA ANALYSIS

• Department: Architecture, Planning and Preservation
• M 3 p.m.-5 p.m.
• 3 points

PHYSICAL DIAGNOSIS II

• Department: Dental and Oral Surgery
• 0 points

OLD RUSSIAN LITERATURE

• Department: Slavic Languages
• T 4:10 p.m.-6 p.m.
• Instructor: Valentina Izmirlieva
  CULPA: 14 Info
• 4 points

CHILD ABUSE RECOGNITION & REPORTING

• Department: Dental and Oral Surgery
• 0 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.
• Instructor: Glenn Mitoma
  Info
• Instructor: Daniela R Ikawa
  Info
• 3 points

MEDIEVAL/RENAISSANCE PHILOLOGY

MEDIEVAL/RENAISSANCE PHILOLGY

• Department: Medieval and Renaissance Studies
• Instructor: Susan Boynton
  CULPA: 18 Info
• 3 points

MEDIEVAL & RENAISSANCE PHILOLOGY

Medieval and Renaissance Philology for MA students.

• Department: Medieval and Renaissance Studies
• Instructor: Susan Boynton
  CULPA: 18 Info
• 4 points

PRIMARY CARE MEDICINE IN DENTISTRY SEMIN

• Department: Dental and Oral Surgery
• 0 points

HLTH PROM/DISEASE PREVNT CLRKSHIP II

HPDP CLERKSHIPS II

• Department: Dental and Oral Surgery
• Instructor: Folarin Odusola
  h-index: 5 Info
• 0 points

Historical Approaches in the Social Scie

Historical Aprchs Scl Scn

This course introduces students to historical approaches in sociology and political science (and some economics). In the first part, the course surveys the major theoretical approaches and methodological traditions. Examples of the former are classic comparativist work (e.g. Skocpol’s study of revolutions), historist approaches (such as Sewell’s), or the historical institutionalist tradition (Mahoney, Thelen, Wimmer, etc.). In terms of methodological approaches, we will discuss classical Millean small-N comparisons, Qualitative Comparative Analysis, process tracing, actor-centered modeling, quantitative, large-N works, and causal inference type of research designs. In the second part, major topics in macro-comparative social sciences are examined, from world systems and empire to the origins of democracy.

• Department: Sociology
• M 4:10 p.m.-6 p.m.
• Instructor: Andreas Wimmer
  Wikipedia h-index: 41 Info
• 4 points

RED ELECTIVE INTERNSHIP

• Department: Architecture, Planning and Preservation
• 1.5 points

PSYCHOPHYSIOLGCL METH/ANALYSIS

• Department: Psychology
• MW 10:10 a.m.-noon
• Instructor: Niall Bolger
  CULPA: 4 h-index: 55 Info
• 4 points

Proseminar in Religion I

The Proseminar in Religion is designed to support PhD students within the department as they work on various aspects of professional development. Meeting three times per semester, the sessions will focus on both academic and non-academic career paths, coordinated by a member of the faculty and with guest speakers from both within and beyond the department. The emphasis will be on concrete outputs and skills training. The proseminar will require preparation and active participation from enrolled students, including background reading and writing assignments connected to the monthly topic. After each session focused on a piece of writing (fellowship applications; CVs and cover letters; publishing), students should come away from the proseminar with strong drafts of the relevant texts.
 

The proseminar is required for all ABD students in year 5 or 6 and can be taken sequentially or not. ABD students are encouraged to speak about the timing of enrollment with the DGS and their dissertation sponsor.

• Department: Religion
• W 7 p.m.-9:30 p.m.
• Instructor: Matthew Engelke
  CULPA: 3 Wikipedia h-index: 25 Info
• 1 points

SOCIOLOGICAL THEORY: THE ORIGINS

Prerequisites: this course is intended for sociology Ph.D. and SMS students. No others without the instructors written permission. Foundational sources and issues in sociological theory: Adam Smith, Marx, Durkheim, Weber, Simmel, Mead, Mauss, others; division of labor, individualism, exchange, class and its vicissitudes, social control, ideas and interests, contending criteria of explanation and interpretation.

• Department: Sociology
• T 9 a.m.-noon
• Instructor: Gil Eyal
  CULPA: 15 h-index: 26 Info
• 3 points

CARIOLOGY

• Department: Dental and Oral Surgery
• 0 points