APPLIED ANALYTICS FRAMEWORKS & METHDS II

APP AN FRAMEWORKS & METHODS II

• Department: Applied Analytics
• W 8:10 p.m.-10 p.m.
• Instructor: Robert Simione
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• 3 points

ADVANCED NEGOTIATION

• Department: Negotiation & Conflict Resolution
• U 9 a.m.-5 p.m.
• Instructor: Sequoia Stalder
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• 3 points

NARRATIVE ETHICS

• Department: Narrative Medicine
• M 3:10 p.m.-5 p.m.
• 3 points

LINEAR REGRESSION MODELS

Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

• Department: Statistics
• MW 7:10 p.m.-8:25 p.m.
• Instructor: Haiyuan Wang
  Info
• 3 points

STAT COMP & INTRO DATA SCIENCE

STAT COMP & INTRO TO DATA SCI

Corequisites: STAT GR5204 and GR5205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

• Department: Statistics
• F 10:10 a.m.-12:40 p.m.
• Instructor: Wayne Lee
  CULPA: 7 Info
• 3 points

Foundations of Clinical Medicine: Tutori

FNDATION CLINICAL MED: TU

• Department: Medicine
• 0 points

ELEMENTARY STOCHASTIC PROCESSE

Corequisites: GR5203 or the equivalent. Review of elements of probability theory. Poisson processes. Renewal theory. Walds equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.

• Department: Statistics
• MW 11:40 a.m.-12:55 p.m.
• Instructor: Anne Van Delft
  CULPA: 2 h-index: 5 Info
• 3 points

PYTHON FOR DATA ANALYSIS

PYTHON FOR DATA ANALYSIS ONLNE

• Department: Applied Analytics
• T 8:10 p.m.-10 p.m.
• Instructor: Max Vilenchik
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• 3 points

PYTHON FOR DATA ANALYSIS

• Department: Applied Analytics
• S 10:10 a.m.-noon
• R 6:10 p.m.-8 p.m.
• Instructor: Helga Hartelt Baez
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• Instructor: Guilherme de Oliveira
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• 3 points

CONFLICT RESOLUTION AND DYNAMICAL SYSTEM

CONF RESLTN & DYNAMICAL SYSTMS

• Department: Negotiation & Conflict Resolution
• UUSS 9 a.m.-5 p.m.
• 3 points

DISASTER PHILANTHROPY

This course analyzes the ways in which philanthropists and nonprofit organizations plan for and respond to disasters. Disasters create immense need quickly. People have responded generously to many natural and human-created disasters that have led to thousands of victims either domestically or globally. The nonprofit sector has often played a leading role, functioning both on the front-lines with first responders and creating a second response that bridges the period of relief and rebuilding. New technologies have often been deployed to improve fundraising as well as disaster relief. Disasters create both a sense of community born of the common experience of suffering and exacerbate differences within communities as those of lowest means struggle the most to recover. Disaster relief and recovery is ripe with questions about who to help and how to best help, presenting ethical dilemmas for the best intentioned of nonprofit leaders. The course will focus on the United States but both readings and assignments include some international comparisons.

• Department: Auditing
• W 4:10 p.m.-6 p.m.
• Instructor: Gregory Witkowski
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• 0 points

SOLID WASTE MANAGEMENT

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

ENV SUST INDICATORS: CONSTRUCTION & USE

This course will present students with the architecture, data, methods, and use cases of environmental indicators, from national-level indices to spatial indices. The course will draw on the instructor’s experience in developing environmental sustainability, vulnerability and risk indicators for the Yale/Columbia EPI as well as for a diverse range of clients including the Global Environmental Facility, UN Environment, and the US Agency for International Development. Guest lecturers will provide exposure to Lamont experience in monitoring the ecological and health impacts of environmental pollution and the use of environmental indicators in New York City government. Beyond lecture and discussion, classroom activities will include learning games, role play and case study methods.   The course will explore alternative framings of sustainability, vulnerability and performance, as well as design approaches and aggregation techniques for creating composite indicators (e.g., hierarchical approaches vs. data reduction methods such as principal components analysis). The course will examine data sources from both in-situ monitoring and satellite remote sensing, and issues with their evaluation and appropriateness for use cases and end users. In lab sessions, the students will use pre-packaged data and basic statistical packages to understand the factors that influence index and ranking results, and will construct their own simple comparative index for a thematic area and region or country of their choice. They will learn to critically assess existing indicators and indices, and to construct their own. In addition, students will assess the impacts of environmental performance in several developing and developed countries using available data (e.g., pollutant levels in soils and air in Beijing and NYC), and project future changes based on the trends they see in their assessments. The course will also examine theories that describe the role of scientific information in decision-making processes, and factors that influence the uptake of information in those processes. The course will present best practices for designing effective indicators that can drive policy decisions.
Advising Note:
Students are required to have had prior coursework in descriptive and inferential statistics, and knowledge of at least one statistical package, R or Python is preferred.

• Department: Sustainability Technology
• M 6:10 p.m.-8 p.m.
• Instructor: Alexander M Desherbinin
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• 3 points

MICROECONOMIC ANALYSIS II

• Department: Economics
• TR 10:45 a.m.-noon
• Instructor: Elliot B Lipnowski
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• 3 points

Secrets of the Short Film

How much story can fit into a five-minute film? How do films which are that short satisfy an audience? How big or small can a story be in ten, fifteen, or twenty minutes? Are there any rules to keep in mind when writing a short film? Is it useful to think of a short as a miniature feature, with the same structural elements? You have spent much of your life watching and thinking about feature films, but at Columbia you will be asked – at least in the first year – to make shorts. These lectures are an introduction to the structure of short narrative films. Each week, outstanding shorts from Sundance, Cannes, Tribeca, Aspen, and other international festivals will be screened and discussed. (You might see a few duds as well, for comparison purposes.) The emphasis in the first two weeks will be on shorts under six minutes, in preparation for the “3-to-5” project. The second two weeks will be devoted to films between 8 and 12 minutes long, in preparation for the “8-to-12”. The final weeks will include a variety of narratives the size of Columbia thesis films. Altogether, over forty films will be shown and discussed.

• Department: Film
• M 2 p.m.-5 p.m.
• Instructor: Daniel Kleinman
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• 1.5 points

MACROECONOMICS ANALYSIS II

• Department: Economics
• MW 11:40 a.m.-12:55 p.m.
• Instructor: Irasema Alonso
  CULPA: 12 Info
• 3 points

DIRECTING II

• Department: Film
• R 2 p.m.-5 p.m.
• R 9 a.m.-noon
• R 2 p.m.-5 p.m.
• Instructor: Bette Gordon
  Wikipedia Info
• Instructor: Hilary Brougher
  Wikipedia Info
• Instructor: Rania Attieh
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• Instructor: Ian W Olds
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• Instructor: Bette Gordon
  Wikipedia Info
• Instructor: Bogdan G Apetri
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• 3 points

FOUNDATIONS OF SPORTS MANAGEMENT

FOUNDATIONS OF SPORTS MGM

• Department: Sports Management
• M 8:10 p.m.-10 p.m.
• Instructor: Michael Dittelman
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• 3 points

TIME SERIES ANALYSIS

Prerequisites: STAT GR5205 Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Rongning Wu
  CULPA: 1 Info
• 3 points

MULTIVARIATE STAT INFERENCE

Prerequisites: STAT GR5205. Multivariate normal distribution, multivariate regression and classification; canonical correlation; graphical models and Bayesian networks; principal components and other models for factor analysis; SVD; discriminant analysis; cluster analysis.

• Department: Statistics
• TR 1:10 p.m.-2:25 p.m.
• Instructor: Cynthia Rush
  CULPA: 1 h-index: 12 Info
• 3 points

BAYESIAN STATISTICS

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.   Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Ronald Neath
  CULPA: 13 Info
• 3 points

DELIVERING THE STRAT MESSAGE

• Department: School of Professional Studies (DVSP)
• W 6:10 p.m.-8 p.m.
• Instructor: Matthew Frederick
  Info
• 3 points

CHANGE MANAGEMENT

Change is a necessary and constant part of any organization. The change may be expected, or it may be in reaction to unanticipated external and/or internal factors. In fact, organizations that do not change do not last. Change initiatives can be exceedingly complex and disorienting, however. The success of a given changeinitiative often rests on the clarity of vision of an organization’s leaders; an accurate and sensitiveunderstanding of the organization’s culture; the involvement, input and buy-in of multiple internal andexternal stakeholders to the change objectives and process; leaders’ ability to leverage technology tocommunicate and drive change; and an organization’s analytical capabilities to document and measureprogress, and continue to iterate and improve. In light of these requirements, this course seeks to ask: What is the role of the HCM leader in facilitatingchange within an organization? The aims of this course are not abstract. This course will help studentsdevelop skills to support actual organizations (their own and/or others) through change. Lectures, readings,videos, in-class and asynchronous discussions, and assignments will all focus on the practical application ofchange theory and empirical research to real-world organizational contexts. This course is an advanced elective within the Master of Science in Human Capital Management program.Prerequisites include “HCMPS5100: Introduction to Human Capital Management,” and “HCMPS5150:Integrated Talent Management Strategies.” Some familiarity with people analytics and digital approaches toHuman Capital Management will also be helpful.

• Department: Human Capital Management
• W 8:30 p.m.-10 p.m.
• Instructor: Lee Miller
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• 3 points

THE EARTH'S CLIMATE SYSTEM

• Department: Sustainability Management
• T 6 p.m.-8 p.m.
• Instructor: Benjamin I Cook
  CULPA: 3 h-index: 56 Info
• 3 points

SURVIVAL ANALYSIS

Prerequisites: STAT GR5205 Survival distributions, types of censored data, estimation for various survival models, nonparametric estimation of survival distributions, the proportional hazard and accelerated lifetime models for regression analysis with failure-time data. Extensive use of the computer.

• Department: Statistics
• MW 7:40 p.m.-8:55 p.m.
• Instructor: Irene Hueter
  CULPA: 9 Info
• 3 points

GENERALIZED LINEAR MODELS

Prerequisites: STAT GR5205 Statistical methods for rates and proportions, ordered and nominal categorical responses, contingency tables, odds-ratios, exact inference, logistic regression, Poisson regression, generalized linear models.

• Department: Statistics
• MW 7:40 p.m.-8:55 p.m.
• Instructor: Michael Sobel
  CULPA: 5 Wikipedia h-index: 35 Info
• 3 points

DIVERSITY, EQUITY & INCLUSION IN PHILANT

DIV, EQUITY & INCLUSN IN

• Department: Non Profit Management
• T 6:10 p.m.-8 p.m.
• Instructor: Christopher Cardona
  h-index: 3 Info
• 3 points

DIRECTING ACTORS II

• Department: Film
• T 2 p.m.-5 p.m.
• T 9 a.m.-noon
• Instructor: Adrienne Weiss
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• Instructor: June Stein
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• 3 points

BUSINESS INTELLIGENCE

BUSINESS INTELLIGENCE IN SPRTS

• Department: Sports Management
• W 6:10 p.m.-8 p.m.
• Instructor: Russell Scibetti
  Info
• 3 points

STATISTICAL MACHINE LEARNING

Prerequisites: STAT GR5206 or the equivalent. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

• Department: Statistics
• F 2:40 p.m.-5:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 3 points

STATISTICAL MACHINE LEARNING

Prerequisites: STAT GR5206 or the equivalent. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

• Department: Statistics
• F 2:40 p.m.-5:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 3 points

STATISTICAL MACHINE LEARNING

Prerequisites: STAT GR5206 or the equivalent. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

• Department: Statistics
• F 2:40 p.m.-5:25 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• 3 points

STATISTICAL MACHINE LEARNING

Prerequisites: STAT GR5206 or the equivalent. The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

• Department: Statistics
• F 2:40 p.m.-5:25 p.m.
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Gabriel Young
  CULPA: 7 Info
• Instructor: Xiaofei Shi
  h-index: 4 Info
• 3 points

APPLIED DATA SCIENCE

Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

• Department: Statistics
• W 6:10 p.m.-8:55 p.m.
• Instructor: Ying Liu
  CULPA: 2 h-index: 12 Info
• 3 points

EXTERNAL STAKEHOLDER REQUIREMENTS

EXT STAKEHOLDER REQ (ONLINE)

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

EXTERNAL STAKEHOLDER REQUIREMENTS

• Department: Enterprise Risk Management
• T 8:10 p.m.-10 p.m.
• W 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: R S Forston
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• Instructor: Violet Diamant
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• Instructor: Alan Cheung
  h-index: 5 Info
• 3 points

FOUNDATIONS OF LABOR & EMPLOYMENT LAW

• Department: Human Capital Management
• M 6:10 p.m.-8 p.m.
• Instructor: Lori B Rassas
  Info
• 3 points

CONFLICT RESOLUTION FIELDWORK

NECR FIELDWORK

• Department: Negotiation & Conflict Resolution
• S 10 a.m.-12:15 p.m.
• Instructor: Nikolas Katsimpras
  Info
• 1 points

DATA ANALYTICS/METRICS IN THE NONPROFIT

Data Analytics

• Department: Auditing
• Instructor: Erwin Vincent de Leon
  Info
• 0 points

DATA ANALYTICS/METRICS IN THE NONPROFIT

DATA ANL/MTRC IN NONPROF

Data Analytics

• Department: Non Profit Management
• M 6:10 p.m.-8 p.m.
• Instructor: Erwin Vincent de Leon
  Info
• 3 points

BUILDING RESILIENCE IN 21ST CENTURY DETR

BLDG RESILIENCE IN DETROI

Detroit is well-recognized as the Blackest big city in America within one of the most segregated metropolitan areas. This preeminence collapsed over the span of the past 40 years under the weight of racist public policy, public and private malfeasance, financial disinvestment, and the temporary usurpation of Black political power. In an effort to better understand current conflicts between Black citizens and their government, this class will examine the role of race in public policy formation, institutional systems, and government. We will examine the thesis that sustainability and racism cannot co-exist; that sustainability is rooted in inclusive social well-being now and in future generations, whereas racism is rooted in hoarding of power and resources for one dominant group. We will explore grass-roots efforts to address root causes, community development efforts to build sustainable communities, and alternative approaches to restructuring local economies.

• Department: Sustainability Management
• W 6:10 p.m.-8 p.m.
• Instructor: Donna Givens Davidson
  Info
• 3 points

CRAFTING THE LANGUAGE OF NONPROFIT LEADE

CRAFTNG LANG OF NONPRFT L

Whether through a speech to key donors, a published op-ed column, an annual report or media interview, leaders of nonprofit organizations must be compelling storytellers. Although other courses in the Nonprofit Management M.S. program helpfully concentrate on the strategy and tactics of communications outreach and social media engagement, the intensive focus of this elective will be on developing students’ capacity for literate writing and speaking across a range of public forums and institutional challenges that face nonprofit organizations. Through a combination of readings, discussion and, most importantly, a diverse range of writing assignments and creative exercises, students will emerge with a new level of editorial proficiency in creating the kind of written and spoken communications that support a nonprofit’s development, promote its public service mission, and manage difficult political, legal and institutional issues that distract organizations from achieving their mission-driven goals. Students will learn best practices for crafting persuasive communications in an increasingly complex and time-sensitive media environment.

• Department: Auditing
• T 6:10 p.m.-8 p.m.
• Instructor: David Stone
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• 0 points

DATA ANALYSIS AND VISUALIZATION IN SUSTA

DATA ANALYSIS & VIS IN SU

• Department: Auditing
• T 6:10 p.m.-8 p.m.
• Instructor: Gregory G Yetman
  Info
• 0 points

ORGANIZATIONAL STRATEGY AND LEARNING

ORG STRAT AND LEARNING

• Department: Human Capital Management
• W 6:10 p.m.-8 p.m.
• Instructor: Sara Daly-Padron
  Info
• 3 points

PROG FOR QUANT & COMP FINANCE

This course covers programming with applications to finance. The applications may include such topics as yield curve building and calibration, short rate models, Libor market models, Monte Carlo simulation, valuation of financial instruments such as options, swaptions and variance swaps, and risk measurement and management, among others. Students will learn about the underlying theory, learn coding techniques, and get hands-on experience in implementing financial models and systems.

• Department: Mathematics
• F 8:10 p.m.-10 p.m.
• Instructor: Ka-Yi Ng
  Info
• 3 points

STATISTICAL METHODS IN FINANCE

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended. A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

• Department: Statistics
• S 9:10 a.m.-11:40 a.m.
• Instructor: Zhiliang Ying
  CULPA: 1 Wikipedia h-index: 62 Info
• 3 points

MANAGING THE MISSION-BASED ORGANIZATION

• Department: Auditing
• Instructor: Cindy Lott
  Info
• 0 points

MANAGING THE MISSION-BASED ORGANIZATION

• Department: Non Profit Management
• W 2:10 p.m.-4 p.m.
• W 6:10 p.m.-8 p.m.
• Instructor: Cindy Lott
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• Instructor: Carlos Ponce
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• 3 points

STOCHASTIC METHODS IN FINANCE

Prerequisites: STAT GR5264 Available to SSP, SMP. Mathematical theory and probabilistic tools for modeling and analyzing security markets are developed. Pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, term structure of interest rates.

• Department: Statistics
• TR 6:10 p.m.-7:25 p.m.
• Instructor: Cristian G Pasarica
  Info
• 3 points

TRANSFORMING TOTAL REWARDS

• Department: Human Capital Management
• T 8:10 p.m.-10 p.m.
• Instructor: Hassan Abdulhaqq
  Info
• 3 points

PUBLIC WRITING AND PUBLIC HEALTH

PUBLIC WRITING & PUBLIC H

The recent racial and economic disparities of the COVID-19 pandemic and the framing of violent policing in the United States as a “public health crisis,” have sharpened the need for informed activist writing in the public sphere. This course will train student to participate in these public conversations. Although the constraints of writing for public-facing venues are many (time, tone, length, presentation of findings), the need for writers with cultural and narrative fluency in medicine is difficult to overstate. Building on the skills developed in the first term of the narrative medicine program, students in this one-credit elective course will study and practice how to write from qualitative research for a public audience. Over three weeks, we will delve into the scholarly and public writing of academics whose work moves between the social and the scientific, studying the rhetorical choices in both kinds of writing, and observing effective techniques for deep engagement and strategies for of-the-moment occasional pieces, fiction, documentary, and long-form journalism. A visit with a climate change and health reporter will provide an opportunity to ask questions about the journalistic practices that link these fields. The class will consider writerly decision-making in times of urgency and crisis, and help students build sustainable research practices that can be called upon in such times. We will also discuss systems for self-editing and responding to editorial comments and pressures. The final project for the course will be a researched op-ed or short essay on a current health-related matter and an accompanying pitch to a national magazine or newspaper. This is a 1-credit Special Topics elective designed for any interested Narrative Medicine M.S. students as well as graduate students from programs such as SPS Bioethics and others, space permitting. It fulfills the core Narrative Medicine program objective of bridging the scholarly/academic realm and public discourse in regard to medicine and public health, while addressing pressing contemporary issues. While the M.S. degree requirements include a creative writing core course, there are currently no offerings which build public-facing journalistic writing skills. It thus addresses a gap in the curriculum, advancing program learning outcome #5: “Graduates will demonstrate comprehension of local, national, or global application of narrative medicine theories and practices.” Such application and implementation is a core mandate of Narrati

• Department: Narrative Medicine
• F 3:10 p.m.-6 p.m.
• Instructor: Anjuli R Kolb
  CULPA: 1 Info
• 1 points

DIGITAL HR AND THE DIGITAL WORKPLACE

DIGITAL HR & DIGITAL WORK

• Department: Human Capital Management
• T 8:30 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• Instructor: Jill Larsen
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• Instructor: Nabeel Ahmad
  Info
• 3 points

COMMUNICATION RESEARCH & INSIGHTS

COMM RESEARCH & INSIGHTS

To make informed decisions about communication, we need a clear understanding of our audience and its motivations. We begin by asking the right questions and interpreting the results. This course covers essential market research methods, including quantitative and qualitative techniques. Students gain direct experience in collecting and analyzing data, developing insights and choosing research-driven communication strategies that meet client objectives.

• Department: Strategic Communication
• W 10:10 a.m.-noon
• W 6:10 p.m.-8 p.m.
• Instructor: Mark Truss
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• Instructor: Alexandra M Merceron
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• Instructor: Ethan Pew
  h-index: 0 Info
• 3 points

ROLE & UNIQUE NATURE OF NONPROFIT SECTOR

• Department: Non Profit Management
• T 6:30 p.m.-8 p.m.
• M 2:10 p.m.-4 p.m.
• Instructor: Gregory Witkowski
  Info
• 3 points

ADVANCED DATA ANALYSIS

Prerequisites: W4315 and either another statistics course numbered above the 4200 or permission of instructor. Required for the major in statistics. Data analysis using a computer statistical package and selected exploratory data analysis subroutines. Topics include editing of data for errors, exploratory and standard techniques for one-way analysis of variance, linear regression, and two-way analysis of variance. Material is presented in case-study format.

• Department: Statistics
• F 10:10 a.m.-12:40 p.m.
• Instructor: Hammou El Barmi
  CULPA: 8 Info
• 3 points

TOPICS IN MODERN STATISTICS

ELEMENTS OF DATA SCI W. PYTHON

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

• Department: Statistics
• M 6:10 p.m.-8:55 p.m.
• Instructor: Bryan Gibson
  Info
• 3 points

RESEARCH DESIGN

RESEARCH DESIGN ONLINE

• Department: Applied Analytics
• W 8:10 p.m.-10 p.m.
• Instructor: Natasa Rajicic
  Info
• 3 points

RESEARCH DESIGN

• Department: Applied Analytics
• F 2:10 p.m.-4 p.m.
• Instructor: David Shilane
  Info
• 3 points

STRATEGIC COMM FOR RISK PROFESSIONALS

STRAT COMM FOR RISK PROF (ONL)

• Department: Enterprise Risk Management
• W 8:10 p.m.-10 p.m.
• Instructor: Saba Zahid
  Info
• 3 points

STRATEGIC COMM FOR RISK PROFESSIONALS

STRAT COMM FOR RISK PROF

• Department: Enterprise Risk Management
• W 6:10 p.m.-8 p.m.
• T 6:10 p.m.-8 p.m.
• R 8:10 p.m.-10 p.m.
• M 6:10 p.m.-8 p.m.
• T 8:10 p.m.-10 p.m.
• Instructor: Sim Segal
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• Instructor: Peter Barnes
  Info
• Instructor: Jennifer C Hoffman
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• Instructor: Penny Cagan
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• Instructor: Daniel D Walker
  Info
• 3 points

Corporate Finance

• Department: Finance
• Instructor: Jay Dahya
  Info
• 3 points

Corporate Finance

• Department: Finance
• Instructor: Jay Dahya
  Info
• 3 points

QUALITATIVE METHODS & RESEARCH

• Department: Narrative Medicine
• R 2:10 p.m.-5 p.m.
• Instructor: Edgar Rivera Colon
  Info
• 4 points

INVESTMENT PLANNING

The Investment Planning course explores the essential principles of investing and how to apply them wisely as
wealth advisors. Students will examine how investment wisdom and theory has evolved – from the insights of
Benjamin Graham to Modern Portfolio Theory, the Capital Asset Pricing Model, factor-based investing and more --
and identify how these theories can be utilized as a framework for understanding and using investments of the present and future. Students will calculate and apply mathematical formulas to learn how to manage risk and return in investment portfolios. This course will compare and contrast each of the major asset classes, ranging from cash and near-cash investments to public and private equity, debt and alternative investments. Students will learn how to apply investment skills to deliver and demonstrate value to clients, net of fees and adjusted risk. In addition, this course will emphasize the parallel development of investment knowledge and communication and counseling skills to conduct investment relationships with clients effectively.

• Department: Wealth Management
• Instructor: Stephen Bodurtha
  Info
• 3 points

POETRY WORKSHOP

• Department: Writing
• F 2 p.m.-5 p.m.
• T 10 a.m.-1 p.m.
• W 9:30 a.m.-12:30 p.m.
• Instructor: Jay A Deshpande
  CULPA: 2 Info
• Instructor: Lynn Melnick
  Info
• Instructor: Deborah Paredez
  CULPA: 3 Wikipedia Info
• 6 points

INTERNATIONAL ENVIRONMENTAL LAW

INTL ENVIRONMENTAL LAW

...

• Department: Sustainability Management
• R 6:10 p.m.-8 p.m.
• Instructor: Richard Horsch
  CULPA: 2 Info
• 3 points

DIGITAL WORKPLACE AND DIGITAL TRANSFORMA

DIGITAL WRKPLC & DGTL TRANSF I

TBA

• Department: Information & Knowledge Strat
• R 8 p.m.-10 p.m.
• Instructor: Benjamin P Royce
  Info
• 1.5 points

Sculpture II: Props, propositions, and p

Working with limited tools in this course, students will explore sculptural objects that can be “activated” in some way -for example, props for performance and video, sets, public interventions; etc.   We will look beyond simple understandings of what sculptures are to ask questions about what sculptures can do and what sculpture's relationship is to extended media and new art-making genres. A strong emphasis will be placed on interdisciplinary practices.     Students will be asked to push their foundational ideas about sculpture further while gaining new vocabularies through an introduction to performance art and ephemerality.   We will take a problem-based, concept-driven approach to sculptural production, grappling with some of the most pressing theoretical concerns surrounding art today. Artists are always working under some form of constraint; students in this course will use common materials without the use of a shop.   Across three projects and several shorter assignments and exercises, we will be focusing on notions of participation, site-specificity, and mode of address, in relation to wider material conditions. Students will be tasked with formulating social, theoretical, and material propositions, and identifying their best possible mode of articulation. In this way students will be supported in pursuing hybrid sculptural practices that point beyond traditional art techniques.

• Department: Visual Arts
• T 10 a.m.-4 p.m.
• Instructor: Sable Smith
  Wikipedia Info
• 3 points

Sculpture III: Props, propositions, and

Working with limited tools in this course, students will explore sculptural objects that can be “activated” in some way -for example, props for performance and video, sets, public interventions; etc. We will look beyond simple understandings of what sculptures are to ask questions about what sculptures can do and what sculpture's relationship is to extended media and new art-making genres. A strong emphasis will be placed on interdisciplinary practices.   Students will be asked to push their foundational ideas about sculpture further while gaining new vocabularies through an introduction to performance art and ephemerality. We will take a problem-based, concept-driven approach to sculptural production, grappling with some of the most pressing theoretical concerns surrounding art today. Artists are always working under some form of constraint; students in this course will use common materials without the use of a shop. Across three projects and several shorter assignments and exercises, we will be focusing on notions of participation, site-specificity, and mode of address, in relation to wider material conditions. Students will be tasked with formulating social, theoretical, and material propositions, and identifying their best possible mode of articulation. In this way students will be supported in pursuing hybrid sculptural practices that point beyond traditional art techniques.

• Department: Visual Arts
• T 10 a.m.-4 p.m.
• Instructor: Sable Smith
  Wikipedia Info
• 3 points

Sculpture 1 - limited tools

The fundamentals of sculpture are investigated through a series of conceptual and technical projects. How do you make sculpture without access to traditional shop equipment? The practice of sculpture is that you master the tools you DO have access to! The essentials of creating 3-dimensional artwork remains the same. This class will guide you through four projects that will challenge your critical thinking, imagination, and notions about art making.  Issues pertinent to contemporary sculpture are introduced through lectures, group critiques, and discussions that accompany class assignments. If the class is full, please visit 
http://arts.columbia.edu/undergraduate-visual-arts-program
.

• Department: Visual Arts
• F 10 a.m.-4 p.m.
• Instructor: Jon Kessler
  CULPA: 5 Wikipedia Info
• 3 points

SQL

SQL & RELATIONAL DB ONLINE

• Department: Applied Analytics
• R 8:10 p.m.-10 p.m.
• Instructor: Michael J Coakley
  Info
• 3 points

SQL

SQL & RELATIONAL DB

• Department: Applied Analytics
• W 8:10 p.m.-10 p.m.
• Instructor: Day Yi
  Info
• 3 points

ETHICAL DECISION MAKING FOR COMMUNICATOR

• Department: Auditing
• T 6:10 p.m.-8 p.m.
• Instructor: Frank J Oswald
  Info
• 0 points

ETHICAL DECISION MAKING FOR COMMUNICATOR

ETHICAL DECISION MAKING - CEU

• Department: Strategic Communication
• T 6:10 p.m.-8 p.m.
• Instructor: Frank J Oswald
  Info
• 3 points

STRATEGIC WORKFORCE PLANNING

• Department: Human Capital Management
• M 8:30 p.m.-10 p.m.
• Instructor: Mark S Baker
  Info
• 3 points