CONDENSED MATTER PHYSICS II

• Department: Physics
• MW 11:40 a.m.-12:55 p.m.
• Instructor: Raquel Quieroz
  Info
• 3 points

Topics in Corporate Finance

Prerequisites: (INAF U6301 or INAF U6022 or INAF U6045) and SIPA U6200. This course considers Corporate Finance topics from a strategic and tactical perspective. It builds on the accounting and valuation techniques introduced in U6301 by providing insights into global capital markets, the application of forecasting and valuation and consideration of ESG and Investor Activism. The course will combine lecture time and in-class case analysis and discussions. The course’s goal is to provide students with an understanding of how corporates and investors forecast cash-flow and effectively and responsibly build and allocate capital.

• Department: International and Public Affairs
• T 4:10 p.m.-6 p.m.
• Instructor: Michael N Eastwood
  Info
• 3 points

PUBLIC HEALTH PRACTICE SEMINAR

• Department: Public Health
• F 11:30 a.m.-12:50 p.m.
• Instructor: Heather Krasna
  h-index: 5 Info
• 0.5 points

Tutorial in general public health

TUTORIAL GENERAL PUBLIC H

Tailored to the particular interests and needs of individual students, the tutorials take many forms-literature reviews, research projects, field trips, and other special studies or learning experiences. Their objective is to enrich the student’s program. General public health subject areas for tutorials might include dental public health, health education, international health, nutrition, drug abuse, and other topical concerns not specifically dealt with in formal courses or in departmental or other study programs.

• Department: General Public Health
• Instructor: Stephen P Kachur
  Info
• 0-6 points

The Transatlantic Economy: US-EU Relatio

A course on contemporary transatlantic economic relations with particular emphasis on the US-EU dimension. Topics include: macroeconomic responses to the Covid-19 crisis and lessons learned from the financial crises of 2008 and 2010; US-EU differences in relations with China; transatlantic implications of Brexit; prospects for US-UK and US-EU trade agreements; the digital economy and data privacy; European competition and tax policy toward U.S. high tech firms; dollar-euro diplomacy and the international roles of the dollar and euro; the economic dimension to transatlantic security.

• Department: International and Public Affairs
• R 4:10 p.m.-6 p.m.
• Instructor: Seamus C O'Cleireacain
  Info
• 3 points

INDIV PROJECTS IN MUSICOLOGY

INDIV PROJECT IN MUSICOLOGY

Sec. 1: Ethnomusicology; Sec. 2: Historical Musicology; Sec. 3: Music Theory; Sec. 4: Music Cognition; Sec. 5: Music Philosophy.

• Department: Music
• Instructor: Susan Boynton
  CULPA: 18 Info
• Instructor: Alessandra M Ciucci
  CULPA: 1 Info
• Instructor: Julia Doe
  Info
• Instructor: Joseph P Dubiel
  CULPA: 3 Info
• Instructor: Kevin Fellezs
  CULPA: 4 Info
• Instructor: Aaron A Fox
  CULPA: 7 Info
• Instructor: Giuseppe Gerbino
  CULPA: 12 Info
• Instructor: Knar Abrahamyan
  Info
• Instructor: Mariusz Kozak
  CULPA: 4 h-index: 5 Info
• Instructor: George Lewis
  CULPA: 10 Wikipedia h-index: 18 Info
• Instructor: Elaine Sisman
  CULPA: 13 Wikipedia Info
• Instructor: Christopher J Washburne
  CULPA: 31 Info
• 3 points

ADV GRAD SEMINAR-RSCH PROBLEMS

• Department: Physics
• MW 10:10 a.m.-noon
• Instructor: Lam Hui
  CULPA: 3 Info
• 3 points

Applied Regression I

APPLIED REGRESSION I

This course will provide an introduction to the basics of regression analysis. The class will proceed systematically from the examination of the distributional qualities of the measures of interest, to assessing the appropriateness of the assumption of linearity, to issues related to variable inclusion, model fit, interpretation, and regression diagnostics. We will primarily use scalar notation (i.e. we will use limited matrix notation, and will only briefly present the use of matrix algebra).

• Department: Biostatistics
• R 1 p.m.-3:50 p.m.
• Instructor: Bo Ji
  Info
• 3 points

Healthcare Management, Design, and Strat

Healthcare Mgmt Design &

COURSE DESCRIPTION AND LEARNING OBJECTIVES
The U.S. healthcare system is an enormously complex, trillion-dollar industry, accounting for approximately 18% of GDP. The healthcare sector is vast and covers multiple different players from patients, providers, payors, to bio/pharma developers and innovators. Each part of the healthcare sector brings a different set of business challenges that touch on aspects from Finance, Marketing, Operations, Accounting, and more. The healthcare industry is going through a transformation with the development of new technologies, increased sophistication and adoption of electronic medical records systems and data collection architectures, and new models of the delivery of care and payment systems. This tremendous dynamism is unmatched by any other industry and offers incredible opportunities for new business endeavors. This course provides students the opportunity to learn about i) approaches to doing consulting; ii) key considerations diving strategic decision-making in the healthcare industry; and iii) the chance to put these concepts to practice by working on a set of company-sponsored applied projects. Student teams of 5-6 people, with 3-4 MBA (CBS) students and 1-2 medical (CUIMC) students, will work hand in hand with the instructors and company representatives to achieve company goals through the practical application of fundamental core business practices. Through these projects, students will be exposed to the unique challenges and opportunities in the healthcare sector. Some examples of potential projects include:

For a pharmaceutical company, evaluate the commercial potential of a new therapeutic class.
Evaluate and identify improvement opportunities in the patient evaluation process of a clinical unit at CUIMC. Redesign the standard workflow ad evaluate the financial and operational impact of these changes.
Utilize consumer predictive analytics to guide marketing strategies for a biotech device.
The scope of sponsoring companies spans large firms in biotech and pharmaceuticals, smaller startups in healthcare analytics and/or biotech, large provider systems, as well as smaller clinics. Companies provide the project scope and relevant data, faculty provides guidance on best practices, and your team will provide the answers.

Throughout this course, students will execute on a healthcare project to:
Use tools and ideas from operations, business analytics, finance, marketing, and strategy to solve interesting and exciting business proble

• Department: Decision, Risk and Operations
• T 6 p.m.-9:15 p.m.
• 3 points

APPLIED REGRESSION I

• Department: ONLINE MS IN EPIDEMIOLOGY
• S 8:30 a.m.-5 p.m.
• Instructor: Tonguc Yaman
  Info
• 3 points

Applied Regression

This course will provide students with a thorough introduction to applied regression analysis, which has been a commonly used and almost standard method for analyzing continuous response data in Public Health research. Topics covered include simple linear regression, multiple linear regression, analysis of variance, parameter estimation, hypothesis testing, interpretation of estimates, interaction terms, variable recoding, examination of validity of underlying assumptions, regression diagnostics, model selection, logistic regression analysis, generalized linear models as well as discussions on relationships of variables in research and using regression results for either prediction or estimation purposes. Real data are emphasized and analyzed using SAS.

• Department: General Public Health
• W 7 p.m.-8 p.m.
• Instructor: Dan Liao
  Info
• 3 points

ADVANCED TOPICS IN IEOR

Reinforcmnt Lrning Cont Time

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• Department: Industrial Engineering and Operations Research
• W 4:10 p.m.-6:40 p.m.
• Instructor: Xunyu Zhou
  h-index: 54 Info
• 3 points

ADVANCED TOPICS IN IEOR

Foundations of Data Privacy

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• Department: Industrial Engineering and Operations Research
• F 10:10 a.m.-12:40 p.m.
• Instructor: Rachel Cummings
  h-index: 16 Info
• 3 points

ADVANCED TOPICS IN IEOR

Comp Stochastic Modeling

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• Department: Industrial Engineering and Operations Research
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• 3 points

ADVANCED TOPICS IN IEOR

Asymp for Stoch Serv Syst

Selected topics in IEOR. Content varies from year to year. May be repeated for credit.

• Department: Industrial Engineering and Operations Research
• T 2:40 p.m.-5:10 p.m.
• Instructor: Martin Reiman
  Wikipedia Info
• 3 points

ADVANCED APPLICATION OF THEORY TO PRACT

• Department: Rehabilitation Medicine (RMED)
• Instructor: Glen Gillen
  Info
• 3 points

Gross Anatomy

GROSS ANATOMY

This intensive 15-week course during the first term of the DPT curriculum provides students with detailed coverage of human anatomy through lecture and cadaver dissection. The focus of the course is on structure and the integral relationship between structure and function. A comprehensive understanding of normal structure and function provides the foundation for understanding abnormal structure and function. Both the lecture and laboratory components of the course are critical to success in the program and as a competent entry-level clinician.

This course uses a regional approach to study the gross anatomical structures of the human body, with emphasis on the musculoskeletal system and its associated vascular and neural elements. The structure of synovial joints and their soft tissue support systems will be addressed. The thoracic, abdominal, and pelvic cavities will be explored. Aspects of structure and function as they relate to clinical correlates will be highlighted throughout the course.

• Department: Physical Therapy
• 7 points

VISUAL ART LAB

• Department: Visual Arts
• F 1 p.m.-4 p.m.
• Instructor: Rirkrit Tiravanija
  Wikipedia Info
• 3 points

Business Analytics II (Full Term)

Business Analytics II (FT

This course extends and deepens the material you learned in business analytics. We will apply these methods in more unstructured and diverse situations, introduce new analytics tools and methods (including Tableau Visualization, text mining, and random forests), and study a modern framework for overfitting reduction called regularization that underlies much of modern machine learning. This course does not require coding or knowledge beyond Business Analytics, but the mathematical sophistication level will be somewhat more advanced.

• Department: Decision, Risk and Operations
• T 2:20 p.m.-5:35 p.m.
• Instructor: Charles R Guetta
  Info
• 3 points

Ex. In-House Counsel

• Department: Law
• W 4:20 p.m.-6:10 p.m.
• None None-None
• W 4:20 p.m.-6:10 p.m.
• None None-None
• Instructor: Mark Nielsen
  Info
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

TOPICS IN APPLIED STATISTICS

.

• Department: Statistics
• R 10 a.m.-noon
• Instructor: John P Cunningham
  CULPA: 7 Info
• 3 points

Patient Oriented Research Career Develop

POR CAREER DEVELOPMENT CO

The main objective of this course is to provide Columbia University's Clinical & Translational Science award trainees, students, and scholars with skills and knowledge that will optimize their chances of entering into a satisfying academic career. The course will emphasize several methodological and practical issues related to the development of a science career. The course will also offer support and incentives by facilitating timely use of CTSA resources, obtaining expert reviews on writing and curriculum vitae, and providing knowledge and resources for the successful achievement of career goals.

• Department: Biostatistics
• T 3 p.m.-3:50 p.m.
• Instructor: Marisa N Spann
  Info
• 0.5 points

NEUROSCIENCE AND COGNITION

• Department: Rehabilitation Medicine (RMED)
• Instructor: Katherine Dimitropoulou
  Info
• 3 points

Probability

PROBABILITY

The course aims to present the fundamental principles behind probability theory and lay the foundations for various kinds of statistical/biostatistical courses such as statistical inference, multivariate analysis, regression analysis, clinical trials, asymptotics, and so on. Students will learn how to implement probability methods in various types of applications.

• Department: Biostatistics
• MW 9:50 a.m.-11:20 a.m.
• Instructor: Xiaoyu Che
  h-index: 17 Info
• 3 points

Technology Breakthroughs

Technological breakthroughs have relentlessly driven change and disruption, affected every aspect of our lives, transformed traditional industries and created new ones for digital goods and services, and ushered a time of opportunity and change across the business spectrum and society.
In this course we will review six such areas – deep learning and neural networks; AI and robotics; imaging and vision; photonics; blockchain; smart / digital cities. In each area, we will get an overview of the state of the art, gain understanding of the problems being addressed, what are important open questions to be addressed in the future; we will review successful applications, and try to glean insight as to areas where successful implementation is still more elusive; and finally, discuss the investment and entrepreneurial landscape in each such area.

• Department: Decision, Risk and Operations
• MF 2:20 p.m.-5:35 p.m.
• 1.5 points

Banking and Sustainable Development

Banking & Sustainable Develop

With an orientation towards practical application and real life experience, this course examines the historical behavior, impact and potential of the local financial sector in developing countries as one of, if not, the critical factor in achieving sustainable growth. Ineffective banks and financial institutions, underdeveloped capital markets, and inadequate or incomplete regulation create major obstacles to sustainable development. Via case studies, propriety materials and guest lectures, we will examine and discuss (1) the latest enhancements in banking techniques and analytics, (2) the increasing use of modeling at both the transaction and portfolio/sectoral level, and (3) the improvements to regulatory oversight that are buttressing developing nations’ abilities to grow sustainably and constrain adverse economic shocks in the aftermath of the 2008 Global Financial crisis. We also will evaluate the proper role of the State in Financial activities and the intersect between the efforts of International Financial Institutions (Development Banks & the IMF) and local financial institutions in achieving sustainable development, alleviating extreme poverty and the broader achievement of the Sustainable Development Goals.

• Department: International and Public Affairs
• T 6:10 p.m.-8 p.m.
• Instructor: Robert Kopech
  Info
• 3 points

Data Science I

Contemporary biostatistics and data analysis depends on the mastery of tools for computation, visualization, dissemination, and reproducibility in addition to proficiency in traditional statistical techniques. The goal of this course is to provide training in the elements of a complete pipeline for data analysis. It is targeted to MS, MPH, and PhD students with some data analysis experience.

• Department: Biostatistics
• TR 10 a.m.-11:20 a.m.
• Instructor: Arthur Jeff Goldsmith
  Info
• 3 points

Theory of Democracy Revisited

This colloquium will study democracy in its most representative contemporary interpretations and its challenges in comparative theoretical perspective. Starting with democracy’s procedures and institutions (the “rules of the game”) the colloquium will examine their main interpretations and most recent variations; it will end with a discussion of plebiscitary leadership, populism and lottocracy. The aim of the colloquium is to give students of political theory and political science some basic theoretical tools for analyzing, understanding and evaluating contemporary mutations in democratic visions and practices in several western countries.

• Department: Political Science
• T 6:10 p.m.-8 p.m.
• Instructor: Nadia Urbinati
  CULPA: 29 Wikipedia Info
• 4 points

The PhD Dissertation

This course provides a forum for advanced PhD students at all stages of dissertation work, from designing the dissertation prospectus and grant proposals to organizing and writing the dissertation.  Emphasis is on clear formulation of a research project, sources and methodology, the mechanics of research, and strategies for turning research into prose with a clear argument and well-designed structure. Class requirements include reading and responding to other students’ work and presenting one’s own work (however fragmentary) in a workshop setting. Throughout the course, you should also be in regular communication with your dissertation supervisor and other committee members as appropriate. Registration is by permission of the instructor only.

• Department: Religion
• W 7 p.m.-9 p.m.
• Instructor: Katherine Ewing
  CULPA: 1 h-index: 22 Info
• 4 points

Introduction to Mathematical Statistics

Intro Mathematical Statis

The first portion of this course provides an introductory-level mathematical treatment of the fundamental principles of probability theory, providing the foundations for statistical inference.  Students will learn how to apply these principles to solve a range of applications.  The second portion of this course provides a mathematical treatment of (a) point estimation, including evaluation of estimators and methods of estimation; (b) interval estimation; and (c) hypothesis testing, including power calculations and likelihood ratio testing.

• Department: Biostatistics
• MW 10 a.m.-11:20 a.m.
• Instructor: Todd Ogden
  Info
• 3 points

Service Operations Management

This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.

• Department: Decision, Risk and Operations
• W 9 a.m.-12:15 p.m.
• W 2:20 p.m.-5:35 p.m.
• R 2:20 p.m.-5:35 p.m.
• R 9 a.m.-12:15 p.m.
• Instructor: Medini Singh
  Info
• 3 points

Capstone One Portfolio

• Department: Rehabilitation Medicine (RMED)
• Instructor: Amanda M Lange
  Info
• 3 points

Survival Analysis

SURVIVAL ANALYSIS

This course focuses on methods for the analysis of survival data, or time-to-event data. Survival analysis is a method for analyzing survival data or failure (death) time data, that is time-to-event data, which arises in a number of applied fields, such as medicine, biology, public health, epidemiology, engineering, economics, and demography. A special course of difficulty in the analysis of survival data is the possibility that some individual may not be observed for the full time to failure. Instead of knowing the failure time t, all we know about these individuals is that their time-to-failure exceeds some value y where y is the follow-up time of these individuals in the study. Students in this class will learn how to make inference for the event times with censored. Topics to be covered include survivor functions and hazard rates, parametric inference, life-table analysis, the Kaplan-Meier estimator, k-sample nonparametric test for the equality of survivor distributions, the proportional hazards regression model, analysis of competing risks and bivariate failure-time data.

• Department: Biostatistics
• M 1 p.m.-3:50 p.m.
• Instructor: Helen Li
  Info
• 3 points

SEM-HISTORCL MUSICLGY-CLASSIC

Topic to be announced.

• Department: Music
• T 4:10 p.m.-6 p.m.
• Instructor: Julia Doe
  Info
• 3 points

CONCISE ORGANOMETALLIC CHEM

Main group and transition metal organometallic chemistry: bonding, structure, reactions, kinetics, and mechanisms.

The only prerequisites needed include General Chemistry II Lectures (specifically, kinetics, and at the level of UN1404 or UN1604) and Organic Chemistry II Lectures (at the level of UN2046 or UN2444).  Advanced knowledge from classes, including but not limited to physical chemistry, inorganic chemistry, advanced organic chemistry, and synthetic methods, is NOT required.

• Department: Chemistry
• TR 1:10 p.m.-2:25 p.m.
• Instructor: Jack R Norton
  CULPA: 6 Wikipedia Info
• 2.5 points

Applied Regression II

APPLIED REGRESSION II

This course will introduce the statistical methods for analyzing censored data, non-normally distributed response data, and repeated measurements data that are commonly encountered in medical and public health research. Topics include estimation and comparison of survival curves, regression models for survival data, logit models, log-linear models, and generalized estimating equations. Examples are drawn from the health sciences.

• Department: Biostatistics
• R 1 p.m.-3:50 p.m.
• Instructor: Xin Ma
  Info
• 3 points

TV PILOT: COMEDY & DRAMA

With the pilot as a focal point, this course explores the opportunities and challenges of telling and sustaining a serialized story over a protracted period of time with an emphasis on the creation, borne out of character, of the quintessential premise and the ongoing conflict, be it thematic or literal, behind a successful series.

Early in the semester, students may be required to present/pitch their series idea. During the subsequent weeks, students will learn the process of pitching, outlining, and writing a television pilot, that may include story breaking, beat-sheets or story outline, full outlines, and the execution of either a thirty-minute or hour-long teleplay. This seminar may include reading pages and giving notes based on the instructor but may also solely focus on the individual process of the writer.  


Students may only enroll in one TV Writing workshop per semester.

• Department: Film
• M 10 a.m.-1 p.m.
• F 10 a.m.-1 p.m.
• M 10 a.m.-1 p.m.
• R 6 p.m.-9 p.m.
• Instructor: David Klass
  Info
• Instructor: Alice O'Neill
  Info
• Instructor: James M Tyler
  Info
• Instructor: Benjamin Robbins
  Info
• 3 points

SCENE STUDY

SCENE STUDY: DIRECTORS

• Department: Theatre Arts
• F 2:10 p.m.-5 p.m.
• Instructor: Brian Kulick
  Info
• 3 points

Pharmacology for Anesthesia and Critical

PHARM FOR ANES & CRITICAL

This course provides an overview of anesthetics, adjuvants and critical care medications commonly used in anesthesia practice with an emphasis on the application of theoretical foundations as it applies to the patient. Cultural humility will be incorporated when developing medication management individualized to patient identities and cultures while including an emphasis on social and cultural health disparities. The course will also provide a systems approach to pathophysiology and the pharmacotherapeutic agents used to treat specific disease states with an emphasis on their impact in anesthesia practice.

• Department: Nursing
• 4 points

WRITING FOR PUBLICATION

• Department: Rehabilitation Medicine (RMED)
• Instructor: Danielle J Mahoney
  Info
• 1 points

Applied Regression Analysis

The goal of this course is to provide students with practical experience in building and analyzing regression models to address business problems.
The course picks up where the core course in Managerial Statistics left off. We will begin with a brief review of regression analysis as covered in the core and then move on to new topics, including model selection, interaction effects, nonlinear effects, classification problems, and forecasting.
All material will be covered through examples, exercises, and cases. In addition, students will work in groups on a final project of their choosing. The goal of the project is to address a specific business problem through statistical analysis.

• Department: Decision, Risk and Operations
• TR 10:50 a.m.-12:20 p.m.
• T 9 a.m.-12:15 p.m.
• Instructor: Paul Glasserman
  Info
• Instructor: David Juran
  Info
• 1.5 points

SEM-NEGOTIATION WORKSHOP

This is a Law School course. For more detailed course information, please go to the Law School Curriculum Guide at: http://www.law.columbia.edu/courses/search

• Department: Law
• R 6:20 p.m.-9:10 p.m.
• F 10 a.m.-1 p.m.
• Instructor: Mavis K Fowler
  Info
• Instructor: Michelle A Greenberg-Kobrin
  Info
• Instructor: Steven M Rabinowitz
  Info
• Instructor: Mavis K Fowler
  Info
• 3 points

Applied Physiology

APPLIED EXERCISE PHYSIOLO

This 13-week course during the first term of the DPT curriculum provides students with a theoretical basis for understanding the body's physiological responses to exercise. Emphasis will be placed upon the practical application of exercise physiology principles in physical therapy practice.

This course is designed to provide an integrative view of human exercise physiology. This class will cover the acute and chronic adaptations to exercise including the cardiovascular, respiratory, neuromuscular and metabolic systems in relation to acute and chronic exercise.

• Department: Physical Therapy
• 2 points

Transforming the Delivery of H

This course provides an advanced, critical analysis of the delivery and payment of healthcare services in the U.S. It will analyze the attractiveness and feasibility of new approaches to address the challenges facing providers, payors and patients operating in an inefficient, misaligned, and fragmented healthcare system. Particular focus will be given to the impact of the 2009 HITECH Act as well as the Affordable Care Act (ACA) of 2010. There will be guest lectures by some of a variety of major leaders in healthcare business and policy. Students with limited knowledge of healthcare payment and delivery systems are unlikely to benefit from the course given the advanced nature of the material that will be covered."

• Department: Decision, Risk and Operations
• T 9 a.m.-12:15 p.m.
• 1.5 points

Advanced Statistical and Computational M

ADV STAT/COMPUT MTHD-GEN/

This course introduces students to advanced computational and statistical methods used in the design and analysis of high-dimensional genetic data, an area of critical importance in the current era of BIG DATA. The course starts with a brief background in genetics, followed by in depth discussion of topics in genome-wide linkage and association studies, and next-generation sequencing studies. Additional topics such as network genetics will also be covered. Examples from recent and ongoing applications to complex traits will be used to illustrate methods and concepts. Students are required to read relevant papers as assigned by the instructor, and each student is required to present a paper during class. Students are also required to work on a project related to the course material, with midterm evaluation of the progress.
We will use one main textbook: The fundamentals of Modern Statistical Genetics by Laird and Lange (Springer, 2012). For further reading, an excellent book is also Handbook of Statistical Genetics, Volume 1 (Wiley, 2007). Another good book is Mathematical and Statistical Methods for Genetic Analysis by Ken Lange (Springer 2002).

• Department: Biostatistics
• F 1 p.m.-3:50 p.m.
• Instructor: Annie Lee
  Info
• 3 points

Analysis of Categorical Data

ANALYSIS OF CATEGORICAL D

A comprehensive overview of methods of analysis for binary and other discrete response data, with applications to epidemiological and clinical studies. It is a second level course that presumes some knowledge of applied statistics and epidemiology. Topics discussed include 2 × 2 tables, m × 2 tables, tests of independence, measures of association, power and sample size determination, stratification and matching in design and analysis, interrater agreement, logistic regression analysis.

• Department: Biostatistics
• TR 6 p.m.-7:20 p.m.
• Instructor: Dacheng Liu
  Info
• 3 points

DIALECTS & ACCENTS

This course continues the actor’s work of experiencing voice and text in a free body as a means to develop versatile and transformative speech.  Students will deepen and refine their knowledge of the phonemes of the International Phonetic Alphabet (IPA), as well as the ability to categorize and utilize Lexical Sets in pursuit of a dialect/accent.  Students will demonstrate their ability to notate texts and transcribe dialects and accents into both IPA and practically apply the framework of the Four Pillars and the Voice Recipe.

The student will use these tools, supplemented by handouts, video & audio resources and independent research, to study several accents/dialects in class as well as at least one additional independently researched accent/dialect. The goal of the class is to expand upon the actor’s choices of speech and vocal expression and to acquaint her/him with the resources necessary to truthfully portray an individual utilizing a dialect/accent on stage or screen.

Students will develop their own unique process for learning accents and dialects
, as well as efficiently and effectively applying their progression to texts via a combination of practice sentences, scene work, conversation, improvisation, cold readings, and a prepared monologue.  Students will complete the course having created a personal, in-depth method for researching and performing a role in which an accent or dialect is required.

Students will do self-directed and supported research as part of their study.  They will consciously and intelligently assimilate this contextual research into their embodiment choices.  The final project is a presentation of their research and the sharing of a monologue that is ideally
written in the student’s selected dialect or accent
.

• Department: Theatre Arts
• W 9 a.m.-12:20 p.m.
• Instructor: Elizabeth Hayes
  Info
• 2 points

Statistical Methods for Causal Inference

Stat Mthds for Causal Inf

Substantive questions in empirical scientific and policy research are often causal. This class will introduce students to both statistical theory and practice of causal inference. As theoretical frameworks, we will discuss potential outcomes, causal graphs, randomization and model-based inference, causal mediation, and sufficient component causes. We will cover various methodological tools including randomized experiments, matching, inverse probability weighting, instrumental variable approaches, dynamic causal models, sensitivity analysis, statistical methods for mediation and interaction. We will analyze the strengths and weaknesses of these methods. The course will draw upon examples from social sciences, public health, and other disciplines. The instructor will illustrate application of the approaches using R/SAS/STATA software. Students will be evaluated and will deepen the understanding of the statistical principles underlying the approaches as well as their application in homework assignments, a take home midterm, and final take home practicum.

• Department: Biostatistics
• TR 10 a.m.-11:20 a.m.
• Instructor: Linda Valeri
  h-index: 24 Info
• 3 points

PROSEMINAR: GERMAN LITERATURE

• Department: Germanic Languages
• W 4 p.m.-6 p.m.
• Instructor: Claudia Breger
  CULPA: 2 Info
• 3 points

Analysis of Health Surveys

ANALYSIS OF HLTH SURVEYS

This is an applied statistical methods course. The course will introduce main techniques used in sampling practice, including simple random sampling, stratification, systematic sampling, cluster sampling, probability proportional to size sampling, and multistage sampling. Using national health surveys as examples, the course will introduce and demonstrate the application of statistical methods in analysing across-sectional surveys and repeated and longitudinal surveys, and conducting multiple imputation for missing data in large surveys. Other topics will include methods for variance estimation, weighting, post-stratification, and non-sampling errors. If time allows, new developments in small area estimation and in the era of data science will also be discussed.

• Department: Biostatistics
• F 8:30 a.m.-11:20 a.m.
• Instructor: Qixuan Chen
  h-index: 27 Info
• 3 points

Demand Analytics

How do airlines decide when to increase ticket prices? Should a hotel charge less per night for a long stay than a short one? Why do some software companies bundle very different products together? How should a fashion retailer decide when do start discounting clothes? Why do so many discounted rates end in .99"? How should companies estimate future demand for their products?"

• Department: Decision, Risk and Operations
• R 9 a.m.-12:15 p.m.
• Instructor: Awi Federgruen
  Wikipedia Info
• 3 points

S. Advising Complex Corporations

S ADVISING COMPLEX CORP

• Department: Law
• T 6:20 p.m.-8:10 p.m.
• Instructor: David Marriott
  Info
• 2 points

All - Test

Test Title

Testing what happens when the course descrition is updated.

• Department: OFFC OF THE REGISTRAR
• 0 points

Graphical Models for Complex Health Data

GRAPHICAL MODELS COMPLEX

This is a course at the intersection of statistics and machine learning, focusing on graphical models. In complex systems with many (perhaps hundreds or thousands) of variables, the formalism of graphical models can make representation more compact, inference more tractable, and intelligent data-driven decision-making more feasible. We will focus on representational schemes based on directed and undirected graphical models and discuss statistical inference, prediction, and structure learning. We will emphasize applications of graph-based methods in areas relevant to health: genetics, neuroscience, epidemiology, image analysis, clinical support systems, and more. We will draw connections in lecture between theory and these application areas. The final project will be entirely “hands on,” where students will apply techniques discussed in class to real data and write up the results.

• Department: Biostatistics
• TR 4 p.m.-5:20 p.m.
• Instructor: Daniel S Malinsky
  Info
• 3 points

DIGITAL LITERACY-DECISION MKRS

Digital Literacy for Decision

COURSE DESCRIPTION
Unrelenting technological progress demands entrepreneurs, executives, and managers to continually upgrade their skills in the pursuit of emerging opportunities. As “software eats the world”, executives from all industries are increasingly called upon to be “Full Stack”: capable of making competent decisions across domains as diverse as digital technology, design, product, and marketing.
In this course, we begin with primers on code, design, and product management. Once the foundation is laid, we examine the best practices for building great products and exceptional teams. We conclude with an overview of how technology is changing the way products are marketed, distributed, and monetized. Our goal is to equip “non-technical” executives with the terminology, tools, and context required to effect change in a software­ and internet-driven world.
COURSE LEARNING OBJECTIVES
To provide an understanding of the technologies that we encounter everyday, and how history can inform the technology decisions executives face today.
To become familiar the concepts that underpin modern computer programming, empowering managers to engage with engineers credibly and confidently.
To shed light on the processes and tools designers use to solve user-facing design and architecture challenges.
To clarify what product managers do, walk through the nitty-gritty of managing software development, and equip executives with the best practices for evaluating and improving their products.
To prepare managers to identify, recruit, and nurture the technical talent they will need to succeed in today’s highly competitive labor market.
To familiarize students with the dynamic context in which technology products live, ensuring the profitable and widespread delivery of those products.

• Department: Decision, Risk and Operations
• R 9 a.m.-12:15 p.m.
• Instructor: Marco A Morales Barba
  Info
• 1.5 points

Kinesiology & Biomechanics I

KINESIOLOGY & BIOMECHANIC

This 16-week course during the first term of the DPT curriculum is the first of a 2-part series. This is a comprehensive lecture/laboratory course in the first semester of the DPT curriculum, which establishes foundational knowledge of normal human movement and an introduction to aberrant human movement. Fundamental biomechanical and kinesiological principles, including kinematics and kinetics, of human movement are integrated with knowledge of anatomical structures under normal and pathological conditions. Each joint complex of the human body is scrutinized and integrated with a regional interdependence approach to human movement.

This course begins with an introduction to the biomechanical properties of connective tissue and muscle mechanics, followed by a discussion of the integral principles of biomechanics (i.e., gravity, friction, leverage, composition, and resolution of internal and external forces in producing movement). These topics are integrated throughout the kinesiology analyses of the human body, organized by anatomical region. Specific attention will be given to the relationship between anatomical structure and kinesiological function, joint classification, osteokinematics, arthrokinematics, muscle and ligament function, kinematic chains, and alignment. There is an emphasis on kinematics and muscle function in normal functional movements, while pathological movement is introduced. The laboratory component highlights surface anatomy palpation with emphasis on structure identification, positioning, body mechanics and hand placement. Additionally, the laboratory component will emphasize the identification of osteokinematics, arthrokinematics, and muscle actions during simple and multiple-joint movement assessments. Both lecture and laboratory incorporate observation and analysis of normal movement of the limbs and trunk, utilizing patient-specific case studies and selected examples. Optional open lab and lecture review sessions are small group review sessions and/or case discussions, organized by 3rd year DPT teaching practicum students. First year DPT students, who wish to attend, may utilize this time to review their lab/lecture material with their peers and 3rd year DPT students, while asking questions pertaining to the course material.

• Department: Physical Therapy
• 5 points

Biostatistical Methods I

BIOSTATISTICAL METHODS I

This one-semester course introduces basic applied descriptive and inferential statistics. The first part of the course includes elementary probability theory, an introduction to statistical distributions, principles of estimation and hypothesis testing, methods for comparison of discrete and continuous data including chi-squared test of independence, t-test, analysis of variance (ANOVA), and their non-parametric equivalents. The second part of the course focuses on linear models (regression) theory and their practical implementation.

• Department: Biostatistics
• F 2:30 p.m.-3:50 p.m.
• Instructor: Molei Liu
  Info
• 3 points

Acute and Critical Care Pharmacology

ACUTE & CRIT CARE PHARMAC

The purpose of this course is to provide a comprehensive and in-depth background in acute and critical care pharmacotherapy. This course will address the pharmacology and appropriate clinical use of agents used in the treatment of selected acute disorders found in acutely/critically ill patients. Recent advances in pharmacotherapy, personalized management strategies, and controversial issues will be included and emphasized.

• Department: Nursing
• M 5 p.m.-6:50 p.m.
• Instructor: Amy Dzierba
  Info
• 3 points

ACTING III

MFA acting students will tackle verse drama and heightened language. We will spend much of our time investigating Shakespeare’s writing, with a focus on King Lear and Much Ado about Nothing, and will weave in contemporary heightened language texts throughout the semester. 

Goals



To develop students into keen interpreters of heightened theatrical language, both classical and contemporary



To enable students to express their instinctive emotional responses to the rhythms, sounds and the mysteries contained in great language texts



To bring character and the specific imaginative world of each play alive thru the language



To foster each actor’s unique voice

• Department: Theatre Arts
• F 9:30 a.m.-12:20 p.m.
• Instructor: Peter Fernandez
  Wikipedia Info
• 3 points

Sports Analytics

Sports analytics refers to the use of data and quantitative methods to measure performance and make decisions to gain advantage in the competitive sports arena. This course builds on the Business Analytics core course and is designed to help students to develop and apply analytical skills that are useful in business, using sports as the application area. These skills include critical thinking, mathematical modeling, statistical analysis, predictive analytics, game theory, optimization and simulation. These skills will be applied to sports in this course, but are equally useful in many areas of business.There will be three main topics in the course: (1) measuring and predicting player and team performance, (2) decision-making and strategy in sports, and (3) fantasy sports and sports betting. Typical questions addressed in sports analytics include: How to rank players or teams? How to predict future performance of players or teams? How much is a player on a team worth? How likely are extreme performances, i.e., streaks? Are there hot-hands in sports performances? Which decision is more likely to lead to a win (e.g., attempt a stolen base or not in baseball, punt or go for it on fourth down in football, dump and chase or not in hockey, pull the goalie or not in hockey)? How to form lineups in daily fantasy sports? How to manage money in sports betting? How to analyze various ``prop'' bets?The main sports discussed in the course will be baseball, football, basketball, hockey, and golf. Soccer, tennis, and other sports will be briefly discussed.
Students are welcome to pursue any sport in more detail (e.g., cricket, rugby, auto racing, horse racing, Australian rules football, skiiing, track and field, or even card games such as blackjack, poker, etc.) in a project. Class sessions will involve a mixture of current events, lecture, discussion, and hands-on analysis with computers in class. Each session will typically address a question from a sport using an important analytical idea (e.g., mean reversion) together with a mathematical technique (e.g., regression). Because of the "laboratory" nature of part of the sessions, students should bring their laptops to each class.

• Department: Decision, Risk and Operations
• R 6 p.m.-9:15 p.m.
• Instructor: Desmond McGowan
  Info
• 3 points

TV PILOT - COMEDY

FILM AF 8131 Intro to Pilot: Comedy

 

With the comedy pilot as a focal point, (and by comedy, that could also include dramedies like
Transparent
and
Insecure
) this course explores the opportunities and challenges of telling and sustaining a serialized half-hour story over a protracted period of time with an emphasis on the creation, borne out of character, of the quintessential premise and the ongoing conflict, be it thematic or literal, behind a successful series.

Early in the semester, students may be required to present/pitch their series idea. During the subsequent weeks, students will learn the process of pitching, outlining, and writing a television pilot, that may include story breaking, beat-sheets or story outline, full outlines, and the execution of a thirty-minute teleplay. This seminar may include reading pages and giving notes based on the instructor but may also solely focus on the individual process of the writer.  

Students may only enroll in one TV Writing workshop per semester.

• Department: Film
• M 2 p.m.-5 p.m.
• Instructor: Mark Williams
  Info
• 3 points

Digital Health Startups

Healthcare represents 18% of the U.S. economy, yet it is one of the last sectors to undergo technology-based transformation. Digital health represents the convergence of healthcare and technology, with the aim to improve access to care, reduce inefficiencies in healthcare delivery, lower costs, enhance the quality of patient care, make treatments more targeted and personalized, and empower consumers to better manage their own health and well-being.
In recent years there has been an explosion of new digital health startups focused on these key objectives. Digital health has become the bellwether of venture funding, outgrowing both traditional healthcare and technology sectors. Venture funding in this category exceeded $29 billion in 2021, double the previous year and a 2,325% increase from 2011.
This course will analyze the unique characteristics and strategies of digital health companies as students form groups to act as venture capitalists and develop investment memos for real companies that are pitched by their founders. Past companies that have been pitched in this course— Maven Clinic, Grand Rounds (Included Health), and Simple Health— have gone on to become high-growth, billion-dollar companies.
Students will analyze key objectives of new businesses and determinants of success including unit economics, product differentiation, go-to market strategies, customer acquisition, marketing tactics, scale-up/growth opportunities, and other business optimization approaches. The course will allow students to hone their investment skills including questions to ask during an entrepreneur’s pitch, developing an investment thesis, and how to structure and write an investment memo. This course will address these issues through a mixture of lectures, case studies, and guest speakers (entrepreneurs and investors) from the digital health sector.

• Department: Decision, Risk and Operations
• MTWRF 9 a.m.-1 p.m.
• Instructor: Halle Tecco
  Wikipedia Info
• 1.5 points

Intro to Databases for Busines

We don’t think about databases much, right? At least not when they’re working right. But they’re all around us. They’re in every product we use. And when they don’t work (think about the iCloud, LinkedIn, or Ashley Madison data breaches in which hundreds of millions of emails and passwords were exposed) the consequences can be extreme.
Every modern company stores their data in a database (it’s like a really big version of Excel), and if you want to analyze the data, you may be expected to know how to access it yourself. In fact, at many companies are requiring even their business leaders to have an understanding of databases. At the very least, knowing how to set up and interact with databases will improve your ability to GSD (get stuff done), strengthen your understanding of how technology works, and make you less of a pain for developers to work with.
In this class, we’ll explore basic SQL (the most common database language) for business analytics. At the end of the course, students should have a deeper understanding of how databases work, how they fit into the general technology stack, how to connect to databases, and know how to browse and exporting data from databases.

• Department: Decision, Risk and Operations
• R 4:10 p.m.-5:40 p.m.
• Instructor: Mattan Griffel
  Info
• 1.5 points

Introduction to Randomized Clinical Tria

INTRO TO RAND CLINICAL TR

Students in this course will learn and practice the fundamental methods and concepts of the randomized clinical trial: protocol development, randomization, blindedness, patient recruitment, informed consent, compliance, sample size determination, crossovers, collaborative trials. Each student prepares and submits the protocol for a real or hypothetical clinical trial.

• Department: Biostatistics
• W 1 p.m.-3:50 p.m.
• 3 points

Clinical Trial Methodology

CLINICAL TRIAL METHODOLOG

Clinical trials are the pilars of clinical research. The main objective of this course is to prepare researchers to design and conduct complex clinical trials that yield valid and reliable results. The course emphasizes on several methodological and practical issues related to the design and analysis of clinical experiments. The course builds on the knowledge and skills gained in the course Randomized Clinical Trial (P8140). The objective of this course is to provide students with working knowledge of certain methodological issues that arise in designing a Clinical Trial. Topics include: Design of small studies (Phase I and II studies), Interim analyses and group sequential methods, Design of survival studies, Multiple outcome measures, Equivalency Trials, Multi-center studies, and trials with multiple outcome measures.

• Department: Biostatistics
• M 8:30 a.m.-11:20 a.m.
• Instructor: Cheng-Shiun Leu
  h-index: 28 Info
• 3 points

Introduction to Programming in

This class is an intensive introduction to R. It starts with the very basics of assigning variables and reading data. It then progresses to using RMarkdown for document and presentation creation. - Week 1 - Introduction to R - RMarkdown - Week 2 - Data Manipulation with dplyr - Creating Visualizations - Week 3 - Reading Data - Iterate Over Lists with purrr - Reshaping Data - Week 4 - Linear Models - Generalized Linear Models - Assessing Model Quality - Week 5 - Cross-Validation - Penalized Regression - Boosted Trees - Week 6 - Shiny Basics - Shiny Dashboard
DROMB8145

• Department: Decision, Risk and Operations
• F 9 a.m.-12:15 p.m.
• Instructor: Jared P Lander
  Info
• 1.5 points

Analytics in Action (Master Cl

This course provides students the opportunity to learn business analytics and data science by working on a set of company sponsored applied projects. Students teams of 5-6 people, with 3-4 MBA students and 1-2 engineering (SEAs) students, will work hand in hand with the instructors and company representatives to achieve company goals through the practical application of data analytics.
It is highly recommended that before taking this class, students take the basic python qualification exam (see gsb.columbia.edu/courses/python). It is also highly recommended that students take DROMB8101, Business Analytics II, as a co-requisite.
Prerequisites listed below.

• Department: Decision, Risk and Operations
• T 6 p.m.-9:15 p.m.
• 3 points

Navigating China: The World's Second Lar

The course will introduce to the students a series of frameworks for understanding the working of the Chinese economy. China is on course to become the world’s largest economy in terms of GDP, but it remains an enigma for global investors and firms. It presents both ignore-at-your-peril opportunities given its high growth rates and large market size, and at the same time enormous challenges in terms of risk management given its domestic complexities and evolving geopolitics. While the country of focus is China, we will discuss a set of topics on the interactions among business, government, and society that are essential in all emerging market economies, including industrial policy, trade policy, digital economy, and financial liberalization. A key feature of the course is that it will be data-driven and fact-driven. The aim is to separate fundamental factors from noises.  Some of the sessions will feature guest speakers. By the end of the course, you will gain a new perspective on the Chinese economy, and recognize many flaws in conventional wisdoms, media labels, and official pronouncements. The new knowledge will give you a set of tools to navigate China and the global economy.

• Department: International and Public Affairs
• T 9 a.m.-12:15 p.m.
• Instructor: Shang-Jin Wei
  Wikipedia h-index: 96 Info
• 3 points

Human Population Genetics

HUMAN POPULATION GENETICS

A good grasp of the fundamentals of Population Genetics is crucial for an understanding of any field of human genetics. This is precisely the aim of this course: to provide to students the key elements of Population Genetics with a view to equip them with the right tools to understand the field of genetics in general and to pursue further studies in human genetics. The course uses various evolutionary principles to explain key population genetics concepts.

• Department: Biostatistics
• M 1 p.m.-3:50 p.m.
• Instructor: Prakash Gorroochurn
  Info
• 3 points

Swahili Coast: Issues, Methods, Historio

This course is designed to introduce graduate students to some of the key issues and terrains of scholarship in Swahili Coastal historiographies. Students will become familiar with some of the central intellectual debates and cornerstone texts, although the reading list is far from exhaustive. We will examine how some of the key issues and locations of research have shifted over time and discuss how and why national politics have shaped regional historiographies in the ways that they have. Of equal, or likely greater, importance is attention to how changes in intellectual theory and methods have impacted the form that scholarship has taken.

• Department: Middle Eastern, South Asian and African Studies
• T 2:10 p.m.-4 p.m.
• Instructor: Laura Fair
  h-index: 15 Info
• 4 points

ACTING TECHNIQUES

ACTING THROUGH SONG

• Department: Theatre Arts
• T 2:10 p.m.-5 p.m.
• Instructor: Jenn Colella
  Wikipedia Info
• 3 points

PRACTICE MANAGEMENT

• Department: Dental and Oral Surgery
• 0 points

Python for MBAs

From the ads that track us to the maps that guide us, the twenty-first century runs on code. The business world is no different. Programming has become one of the fastest-growing topics at business schools around the world. This course is an introduction to business uses of Python for MBA students. In this course, well be learning how to write Python code that automates tedious tasks, parses and analyzes large data sets, interact with APIs, and scrapes websites. This might be one of the most useful classes you ever take. Required Course Material Students must have a laptop that they can bring to class - Mac or PC is fine, as long as your operating system is up to date (at least Windows 10 and Mac OS 11). This course does not require a textbook. (Optional Reading: Python for MBAs, Griffel and Guetta) Any required readings will be provided via Canvas. Slides and files will be uploaded to Canvas after each class.
Students will need to complete an introductory Python class (https://courseworks2.columbia.edu/courses/152704) and pass the Basic Python Qualification exam (https://www8.gsb.columbia.edu/courses/python#basic_qual) before the first day of classes.

• Department: Decision, Risk and Operations
• R 2:20 p.m.-5:35 p.m.
• W 6 p.m.-9:15 p.m.
• Instructor: Mattan Griffel
  Info
• Instructor: Jason Prestinario
  Info
• 1.5 points