ADV ANALYTIC TECHNIQUES
This course is meant to train students in advanced quantitative techniques in the social sciences. Statistical computing will be carried out in R. Topics include: review of multiple/linear regression, review of logistic regression, generalized linear models, models with limited dependent variables, first differences analysis, fixed effects, random effects, lagged dependent variables, growth curve analysis, instrumental variable and two stage least squares, natural experiments, regression discontinuity, propensity score matching, multilevel models or hierarchical linear models, and text-based quantitative analysis.
- Department: Graduate School of Arts and Sciences
- F 10:10 a.m.-noon
-
Instructor:
Gregory M Eirich
CULPA: 1 Info - 4 points
INTRODUCTION TO MARKETING
INTRO TO MARKETING
Students will learn fundamental marketing concepts and their application. By the end of this class you will know: the elements of a market, company strategy, how to identify customers and competition, the fundamental elements of the marketing mix (product, price, placement and promotion) how to research consumer behavior, and pricing strategies. Students will have extensive use of case study projects.Please note that tuition is the same for online and on-campus courses, there is an additional $85 course fee for online courses.
- Department: Business
- M 6:10 p.m.-8 p.m.
-
Instructor:
Beth Hirschhorn
Info - 3 points
INTRODUCTION TO MARKETING
INTRO TO MARKETING (ONLINE)
Students will learn fundamental marketing concepts and their application. By the end of this class you will know: the elements of a market, company strategy, how to identify customers and competition, the fundamental elements of the marketing mix (product, price, placement and promotion) how to research consumer behavior, and pricing strategies. Students will have extensive use of case study projects.Please note that tuition is the same for online and on-campus courses, there is an additional $85 course fee for online courses.
- Department: Business
- T 8:10 p.m.-10 p.m.
-
Instructor:
Lauri Harrison
Info - 3 points
STRATEGIC STORYTELLER
- Department: Strategic Communication
- MTWRF 9 a.m.-5 p.m.
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Instructor:
John D Schramm
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: Graduate School of Arts and Sciences
- W 8:10 p.m.-10 p.m.
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 2 points
RES SEM IN QUANT MTHS-SOC SCI II
- Department: Graduate School of Arts and Sciences
- W 8:10 p.m.-10 p.m.
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Instructor:
Gregory M Eirich
CULPA: 1 Info - 2 points
MARKETING STRATEGY
Prerequisites: BUSI PS5020 Introduction to Marketing/or Professor Approval is required
Students will develop analytical skills used to formulate and implement marketing driven strategies for an organization. Students will develop a deeper understanding of marketing strategies and how to implement tactics to achieve desired goals. Students will work on case study projects in both individual and a team based projects. By the end of this course you will be able to develop a marketing strategy based market assessments and company needs.
- Department: Business
- M 6:10 p.m.-8 p.m.
-
Instructor:
Sandy Becker
Info - 3 points
MARKETING STRATEGY
MARKETING STRATEGY (ONLINE)
Prerequisites: BUSI PS5020 Introduction to Marketing/or Professor Approval is required
Students will develop analytical skills used to formulate and implement marketing driven strategies for an organization. Students will develop a deeper understanding of marketing strategies and how to implement tactics to achieve desired goals. Students will work on case study projects in both individual and a team based projects. By the end of this course you will be able to develop a marketing strategy based market assessments and company needs.
- Department: Business
- R 8:10 p.m.-10 p.m.
-
Instructor:
Andrew Blum
Info - 3 points
CINEMA HIST IV:AFTER 1990
- Department: Film
- W 4:10 p.m.-5:25 p.m.
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Instructor:
Richard A Pena
CULPA: 16
Info
- 3 points
ILLNESS/DISABILITY NARRATIVES
- Department: Narrative Medicine
- T 6:10 p.m.-9 p.m.
- 4 points
BODIES, ILLNESS & CARE
- Department: Narrative Medicine
- W 2:10 p.m.-5 p.m.
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Instructor:
Craig Irvine
Info - 4 points
DEVELOP/IMPLEMENTING NEW IDEAS
DEVELOPING & IMPLEMENTING NEW
Interested in starting your own company? Do you have an idea for a new product or service? Have you come up with a way to improve something that already exists? This course tackles the central business concept of how one creates, builds and leads companies. It looks at aspects of entrepreneurship and leadership for both individuals and teams in the face of complex situations. Using the case study method as taught in business school, also known as "participant-centered learning," this course puts students in the role of an entrepreneur facing critical business decisions. A selection of guest speakers will offer firsthand experience on innovation and entrepreneurship.
- Department: Business
- W 6:10 p.m.-8 p.m.
-
Instructor:
Michael E Jr McGuire
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
- MW 7:40 p.m.-8:55 p.m.
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Instructor:
Tat Sang Fung
Info - 3 points
SECURITY ANALYSIS
Prerequisites: BUSI PS5001 Intro to Finance and BUSI PS5003 Corporate Finance or Professor Approval required. If you have not taken PS5001 or PS5003 at Columbia University, please contact phb2120@columbia.edu for professor approval.
Students will learn about the valuation of publicly traded equity securities. By the end of the semester students will be able to perform fundamental analysis ("bottoms-up," firm-level, business and financial analysis), prepare pro forma financial statements, estimate free cash flows and apply valuation models.
- Department: Business
- R 6:10 p.m.-8 p.m.
-
Instructor:
Guy M Miller
Info - 3 points
TOPICS IN AMERICAN CINEMA: FILM NOIR
TPCS IN AMER CINEMA
- Department: Film
- W 1:10 p.m.-3:55 p.m.
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Instructor:
Robert King
CULPA: 7
Info
- 3 points
MATH FINANCE PRACTITIONERS SEM
Prerequisites:
MATH W5010
or knowledge of J. Hull's book Options, futures.
Prerequisites: Math GR5010 or knowledge of J. Hull's book Options, futures.
Seminar consists of presentations and mini-courses by leading industry specialists in quantitative finance. Topics 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 general audience.
- Department: Mathematics
- TR 7:40 p.m.-8:55 p.m.
-
Instructor:
Lars T Nielsen
h-index: 17 Info - 3 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: Graduate School of Arts and Sciences
-
Instructor:
Gregory M Eirich
CULPA: 1 Info - 2 points
INTERNSHIP IN SOCIAL RESEARCH
- Department: Graduate School of Arts and Sciences
-
Instructor:
Gregory M Eirich
CULPA: 1 Info - 2 points
PRACTICUM IN DATA ANALYSIS
PRACTICUM IN LARGE-SCALE DATA
This practicum course is meant to offer valuable training to students. Specifically, 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: (1) Developing the intuition and
skills to properly scope ambiguous project ideas; (2) practicing organizing and accessing a variety of
large-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, and
how it would best translate into corporate, non-profit, start-up and other contexts.
- Department: Graduate School of Arts and Sciences
- R 8:10 p.m.-10 p.m.
-
Instructor:
Aracelis Z Torres
Info - 2 points
METHODS WORKSHOP
Introducing students to a series of methods, methodological discussions, and questions relevant to the focus of the Masters program: urban sociology and the public interest. Three methodological perspectives will frame discussions: analytical sociology, small-n methods, and actor-network theory.
- Department: Sociology
- M 4 p.m.-6 p.m.
-
Instructor:
Hebatalla Y Gowayed
Info - 4 points
PRACTICUM IN DATA ANALYSIS II
- Department: Graduate School of Arts and Sciences
- R 8:10 p.m.-10 p.m.
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Instructor:
Aracelis Z Torres
Info - 2 points
SOCIAL NETWORK ANALYSIS
The course is designed to teach students the foundations of network analysis including how to manipulate, analyze and visualize network data themselves using statistical software. We will focus on using the statistical program R for most of the work. Topics will include measures of network size, density, and tie strength, measures of network diversity, sampling issues, making ego-nets from whole networks, distance, dyads, homophily, balance and transitivity, structural holes, brokerage, measures of centrality (degree, betweenness, closeness, eigenvector, beta/Bonacich), statistical inference using network data, community detection, affiliation/bipartite networks, clustering and small worlds; positions, roles and equivalence; visualization, simulation, and network evolution over time.
- Department: Graduate School of Arts and Sciences
- T 10:10 a.m.-noon
-
Instructor:
Gregory M Eirich
CULPA: 1 Info - 4 points
DATA VISUALIZATION
This course is designed to the interdisciplinary and emerging field of data science. It will cover techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science to enhance the understanding of complex data. Students will be required to complete several scripting, data analysis and visualization design assignments as well as a final project. Topics include: data and image models, social and interactive visualizations, principles and designs, perception and attention, mapping and cartography, network visualization. Computational methods are emphasized and students will be expected to program in R, Javascript, D3, HTML and CSS and will be expected to submit and peer review work through Github. Students will be expected to write up the results of the project in the form of a conference paper submission.
- Department: Graduate School of Arts and Sciences
- M 6:10 p.m.-8 p.m.
-
Instructor:
Thomas Brambor
h-index: 9 Info - 4 points
PROSEMINAR II
- Department: Sociology
- T 2 p.m.-4 p.m.
-
Instructor:
Denise Milstein
CULPA: 9
h-index: 5
Info
- 1 points
PO SIE ET POETIQUE
- Department: French and Romance Philology
-
Instructor:
Christine Valero
Info - 3 points
BAYESIAN STATS FOR THE SOC SCI
An introduction to Bayesian statistical methods with applications to the social sciences. Considerable emphasis will be placed on regression modeling and model checking. The primary software used will be Stan, which students do not need to be familiar with in advance. Students in the course will access the Stan library via R, so some experience with R is necessary. Any QMSS student is presumed to have sufficient background. Any non-QMSS students interested in taking this course should have a comparable background to a QMSS student in basic probability. Topics to be covered are a review of calculus and probability, Bayesian principles, prediction and model checking, linear regression models, Bayesian calculations with Stan, hierarchical linear models, nonlinear regression models, missing data, and decision theory.
- Department: Graduate School of Arts and Sciences
- TR 4:10 p.m.-5:25 p.m.
-
Instructor:
Benjamin K Goodrich
Info - 4 points
FIELD WORK II
- Department: Sociology
- T 9 a.m.-10 a.m.
-
Instructor:
Denise Milstein
CULPA: 9
h-index: 5
Info
- 1 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: Graduate School of Arts and Sciences
- M 8:10 p.m.-10 p.m.
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Instructor:
Wayne Lee
CULPA: 7 Info - 4 points
THESIS SEMINAR II
- Department: Sociology
- T 10:10 a.m.-noon
-
Instructor:
Denise Milstein
CULPA: 9
h-index: 5
Info
- 3 points
APPLIED DATA SCI FOR SOC SCIENTISTS
APPLIED DATA SCI-SOC SCIENTIST
- Department: Graduate School of Arts and Sciences
- W 6:10 p.m.-8 p.m.
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Instructor:
Marco A Morales Barba
Info - 4 points
THE COMPELLING COMMUNICATOR
- Department: Strategic Communication
- M 6:10 p.m.-8 p.m.
- W 8:30 p.m.-10 p.m.
-
Instructor:
Biswagit T Mazumdar
Info -
Instructor:
John D Schramm
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: Graduate School of Arts and Sciences
- W 6:10 p.m.-8 p.m.
-
Instructor:
Michael Parrott
Info - 4 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: Graduate School of Arts and Sciences
- R 4:10 p.m.-6 p.m.
-
Instructor:
Michael Parrott
Info - 4 points
THE REFLECTIVE LEADER
- Department: Strategic Communication
- T 10:10 a.m.-noon
- T 2:10 p.m.-4 p.m.
- None None-None
- M 8:30 p.m.-10 p.m.
-
Instructor:
Kevin Visconti
Info - 3 points
THE DOCUMENTARY TRADITION
- Department: Film
- R 10 a.m.-1:45 p.m.
-
Instructor:
Nico Baumbach
Info - 3 points
DIGITAL MEDIA & ANALYTICS
- Department: Strategic Communication
- M 4:10 p.m.-6 p.m.
- M 2:10 p.m.-4 p.m.
- None None-None
-
Instructor:
Nicole Alexander
Info - 3 points
APPL ANALYTICS IN ORGANIZATIONAL CONTEXT
APP AN IN ORG CONTEXT
- Department: School of Professional Studies
- T 8:10 p.m.-10 p.m.
- M 8:30 p.m.-10 p.m.
-
Instructor:
Cory E Labanow
Info -
Instructor:
David Kreutter
Info - 3 points
GLOBAL EMERGING MARKETS
- Department: Business
- T 6:10 p.m.-8 p.m.
-
Instructor:
Panos Mourdoukoutas
Wikipedia Info - 3 points
ESTIMATING PROJECT COSTS
- Department: Construction Administration
- M 6:10 p.m.-8 p.m.
- 3 points
VALUE-BASED ENTERPRISE RISK MGMT
VALUE-BASED ENT RISK MGMT
- Department: Enterprise Risk Management
- R 8:10 p.m.-10 p.m.
- W 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
-
Instructor:
Sim Segal
Info -
Instructor:
Daniella E Uribe
Info -
Instructor:
Rajeev Dave
Info - 3 points
VALUE-BASED ENTERPRISE RISK MGMT
VALUE-BASED ERM (ONLINE)
- Department: Enterprise Risk Management
- T 8:10 p.m.-10 p.m.
-
Instructor:
David A. Bartholomew
Info - 3 points
SELF & OTHER IN CLIN ENCOUNTER
- Department: Narrative Medicine
- R 2:10 p.m.-5 p.m.
-
Instructor:
Craig Irvine
Info - 4 points
FIN & ACCOUNTNG IN CONSTRUCTION INDUSTRY
FIN & ACCTG CONSTRCTN INDUSTRY
- Department: Construction Administration
- T 6:10 p.m.-8 p.m.
-
Instructor:
Anthony Breu
Info - 3 points
SAFETY IN THE CONSTR INDUSTRY
- Department: Construction Administration
- W 6:10 p.m.-8 p.m.
-
Instructor:
Joseph Schwed
Info - 3 points
LEGAL ISSUES IN BUILDING CONSTRUCTION
LEGAL ISSUES-BLDG CONSTR
- Department: Construction Administration
- M 6:10 p.m.-8 p.m.
-
Instructor:
Dana E Gold
Info - 3 points
FRENCH AMERICA,1534-1804
- Department: French and Romance Philology
-
Instructor:
Pierre Force
CULPA: 12 h-index: 11 Info - 3 points
ELEMENTAL TOPICS IN CNSTR ADMN
- Department: Construction Administration
- M 6:10 p.m.-8 p.m.
-
Instructor:
Courtney L Canfield
Info - 3 points
HISTORY OF THE MODERN SELF: MONTAIGNE
HIST OF SELF: MONTAIGNE
This seminar will focus particularly on the contrast between Greek and Christian conceptions of the soul, and Montaigne’s conception of the (very) self,
le moi
– and all the implications of that shift for the history of Western thought.
- Department: French and Romance Philology
- M 2:10 p.m.-4 p.m.
-
Instructor:
Mark Lilla
CULPA: 57
Wikipedia
Info
- 3 points
The I of the story
- Department: French and Romance Philology
-
Instructor:
Thomas Dodman
CULPA: 2 Info - 3 points
METHODS IN NARRATIVE MEDICINE
- Department: Narrative Medicine
- T 3:10 p.m.-6 p.m.
- R 6:10 p.m.-9 p.m.
- M 3:10 p.m.-6 p.m.
- 6 points
SCHEDULNG FOR MODERN CONSTRUCTION
SCHED FOR MODERN CONSTR
- Department: Construction Administration
- T 6:10 p.m.-8 p.m.
-
Instructor:
Sadia I Janjua
Info - 3 points
SCREENWRITING II
- Department: Film
- M 10 a.m.-1 p.m.
- W 10 a.m.-1 p.m.
- R 10 a.m.-1 p.m.
- T 6 p.m.-9 p.m.
- W 2 p.m.-5 p.m.
- M 10 a.m.-1 p.m.
-
Instructor:
Ramin C Serry
Info -
Instructor:
Richard Dresser
Info -
Instructor:
Courtney C Hunt
Wikipedia Info -
Instructor:
Milena Jelinek
CULPA: 6 Info -
Instructor:
David B Schwab
Info -
Instructor:
Rachel Israel
Wikipedia Info - 3 points
SCRIPT TO SCREEN
- Department: Film
- W 6 p.m.-9 p.m.
- R 6 p.m.-9 p.m.
- M 2 p.m.-5 p.m.
- W 2 p.m.-5 p.m.
- W 6 p.m.-9 p.m.
- M 2 p.m.-5 p.m.
-
Instructor:
Jessica L Keyt
Info -
Instructor:
John Walsh
Info -
Instructor:
Tom Kalin
Wikipedia Info -
Instructor:
Katherine Dieckmann
Info - 1.5 points
HR IN CONSTRUCTION MGMT
- Department: Construction Administration
- F 4:10 p.m.-6 p.m.
-
Instructor:
Robert Yardis
Info - 3 points
IT ADVANTAGES IN CONSTRUCTION
- Department: Construction Administration
- R 6:10 p.m.-8 p.m.
-
Instructor:
Rawle Sawh
Info - 3 points
CONSTRUCTION PROJECT MANAGEMENT
CONSTRUCTION PROJ MGMT
- Department: Construction Administration
- W 4:10 p.m.-6 p.m.
- 3 points
ELEMENTS OF GREEK PROSE STYLE
Prerequisites: at least four terms of Greek, or the equivalent.
An intensive review of Greek syntax with translation of English sentences and paragraphs into Attic Greek.
- Department: Classics
- TR 11:40 a.m.-12:55 p.m.
-
Instructor:
Elizabeth W Scharffenberger
CULPA: 20
Info
- 4 points
SOCIAL PURPOSE BUSINESSES
- Department: School of Professional Studies
- M 6:30 p.m.-8 p.m.
-
Instructor:
John Tyler
h-index: 3 Info - 3 points
ADVANCED PROJECT MANAGEMENT
- Department: Construction Administration
- R 6:10 p.m.-8 p.m.
- 3 points
BECOMING A TELEVISION WRITER: A Q&A; SERI
- Department: Film
- T 10 a.m.-1 p.m.
-
Instructor:
Blair Singer
Info - 0 points
INTEGRATED TALENT MGMT STRATEGIES
INT TALENT MGMT STRAT
- Department: Human Capital Management
- R 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
-
Instructor:
Steven I Safier
Info -
Instructor:
Cindy R Pace
h-index: 1 Info - 3 points
NONPROFIT GOVERNANCE
- Department: School of Professional Studies
- M 6:10 p.m.-8 p.m.
- F noon-1:30 p.m.
-
Instructor:
Timothy Higdon
Info -
Instructor:
Cindy Lott
Info - 3 points
DATA MODELING
- Department: Applied Analytics
- M 6:30 p.m.-8 p.m.
-
Instructor:
Steven Hoberman
Info - 3 points
POLITICAL COMMUNICATIONS
- Department: Strategic Communication
- W 6:10 p.m.-8 p.m.
-
Instructor:
Biswagit T Mazumdar
Info - 3 points
PEOPLE ANALYTICS & DECISION MAKING
PEOPLE ANALYTICS & DECSN MKNG
- Department: Human Capital Management
- T 6:10 p.m.-8 p.m.
-
Instructor:
Ashley K Buonasera
Info - 3 points