VISITING ARTISTS

• Department: Theatre Arts
• Instructor: Anne Bogart
  Wikipedia Info
• 0-3 points

Scales of History

Every student of history must eventually reckon with questions of scale: What temporal and geographical boundaries are epistemologically defensible in a study? How long or short is the historical arc? How wide or narrow is the geographical scope? Does one approach one’s object of study from above or from below? What are the epistemic implications of working with anecdotes or cases, with planetary time or world-systems, with series, multiples, and sequences? This seminar is structured thematically around the problems of scaling historiography. It brings key texts from the historical disciplines (with special attention to art history and architectural history) to bear on such problematics as: the case study, the period, microhistory, world-systems, the 
longue durée
, the anecdote, global and world history, seriality, and comparison. It is meant to be both a theoretical and a practical resource for those tackling the challenges of writing history. Weekly discussions will be driven by close readings of assigned texts.

• Department: Art History and Archaeology
• Instructor: Zeynep Celik Alexander
  Info
• 4 points

QUANTUM FIELD THEORY II

Prerequisites: PHYS G6037-G6038. Relativistic quantum mechanics and quantum field theory.

• Department: Physics
• Instructor: Frederik Denef
  CULPA: 1 h-index: 36 Info
• 4.5 points

THEATRE PRACTICUM II

IMPULSE/STIMULUS/SPECIFICITY

TBD

• Department: Theatre Arts
• Instructor: Tina Benko
  Info
• 3 points

THEATRE PRACTICUM III

SELF TO PERFORMANCE

• Department: Theatre Arts
• 0-3 points

THEATRE PRACTICUM III

EARLY CAREER PREP

• Department: Theatre Arts
• Instructor: Scott V Whitehurst
  Info
• 0-3 points

PRODUCTION & TECHNICAL MANAGEMENT

PRODUCTION & TECH. MANAGE

• Department: Theatre Arts
• Instructor: Ryan Murphy
  Info
• 3 points

ADVANCED SCIENTIFIC COMPUTING

• Department: Physics
• Instructor: Robert D Mawhinney
  CULPA: 6 h-index: 49 Info
• 3 points

CONDENSED MATTER PHYSICS II

• Department: Physics
• Instructor: Yasutomo Uemura
  Info
• 3 points

Internship Class - Fall Session

This seminar is designed to provide an in-depth experiential learning experience concurrent with students’ public health or healthcare management internship. The seminar provides a supportive framework designed to enhance students’ professional and leadership experience by exploring common themes encountered in the fieldwork setting. The seminar will address the public health core competencies of leadership, communications, cultural competence, and professionalism.

The semester will begin with discussion of students’ project sites including project overview, goals for the field work experience and anticipated challenges. Focusing on professionalism in the workplace, students will assess how the internship aligns with their overarching learning goals. Students will also gain insights into successful leadership styles and skills through the design and implementation of an in-depth interview of a professional in their chosen field, a panel discussion of alumni in the field, and by developing and practicing oral and written communication and negotiation skills.

Throughout the course, students will develop a presentation that both demonstrates and reflects upon knowledge acquired through the internship experience. The semester culminates with students presenting the highlights of their project work.

This course is only open to students who either: (a) are required to complete a practicum as part of their degree, and have already completed their required practicum experience, and who have an internship (in a different setting or with different learning goals than their practicum) during the fall semester of their final year of school; OR (b) are enrolled in a Master’s program which does not require a practicum, and would like to take part in an optional internship during the fall semester of their final year of school."

Course pre-requisites: Completion of APeX (if required) and have an internship (in a different setting or with different learning goals than their practicum or APeX) during the semester.

Required permissions: This course is only open to students who have already completed their required practicum/APeX experience (if required), and who have an internship (in a different setting or with different learning goals than their practicum) during the fall semester. Students must submit a letter from their employer to join the waitlist for this course.

• Department: Public Health
• Instructor: Heather Krasna
  h-index: 5 Info
• 0.5 points

M.A. ESSAY DIRECTION

• Department: French
• 6 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: 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: Christopher J Washburne
  CULPA: 31 Info
• 3 points

INDIV PROJECTS IN MUSICOLOGY

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

• Department: Music
• Instructor: Ruth Opara
  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
• 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
• 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
• Instructor: Dan Liao
  Info
• 3 points

ADVANCED TOPICS IN IEOR

Diffusion Models AI & RL

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

• Department: Industrial Engineering and Operations Research
• Instructor: Xunyu Zhou
  h-index: 54 Info
• 3 points

ADVANCED TOPICS IN IEOR

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

• Department: Industrial Engineering and Operations Research
• Instructor: Clifford Stein
  CULPA: 31 Wikipedia h-index: 53 Info
• Instructor: Yuri Faenza
  h-index: 11 Info
• Instructor: Jay Sethuraman
  CULPA: 10 Info
• Instructor: Antonius B Dieker
  CULPA: 10 h-index: 19 Info
• 3 points

VISUAL ART LAB

• Department: Visual Arts
• Instructor: Rirkrit Tiravanija
  Wikipedia Info
• 3 points

Business Analytics I (Full Term)

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
• Instructor: Charles R Guetta
  Info
• 3 points

Modern Middle East: Themes in Current Re

This course is open to Ph.D. students and advanced M.A. students conducting research on
aspects of the modern, culture, politics, and history of the Middle East and adjacent regions. Its
temporal focus is the three centuries from roughly the mid-eighteenth to the mid-twentieth
century, but those whose research deals with other periods are welcome to participate.
The course has three aims. The first is to provide an opportunity to read and engage with some
of the more recent scholarship in the field, especially work published in the last ten years,
organized around several current academic debates. The second is to provide a seminar in
which those preparing a master’s paper, M.Phil. examination list, or Ph.D. prospectus, or a term
paper intended for conference presentation or publication, can develop and present a draft of
their work. We will choose readings to accompany each paper, focusing on recent scholarship
that informs or extends the issues addressed in the research. The course will enable students to
clarify and test the questions that shape their work and better situate them within current
scholarship. The third aim is to train students in the art of framing questions and shaping
debate for an advanced, reading-intensive graduate-level seminar.
The course is intended primarily for MESAAS students. Those from other departments are
welcome but require the permission of the instructor to enroll.

• Department: Middle Eastern, South Asian and African Studies
• Instructor: Timothy Mitchell
  Wikipedia h-index: 34 Info
• 4 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
• Instructor: Marisa N Spann
  Info
• 0.5 points

Business Analytics II: Foundations of AI

Business Analytics II: AI

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
• Instructor: Charles R Guetta
  Info
• 1.5 points

Business Analytics II: Foundations of AI

Business Analytics II: AI

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
• Instructor: Charles R Guetta
  Info
• 1.5 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
• Instructor: Zhezhen Jin
  h-index: 51 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
• Instructor: Arthur Jeff Goldsmith
  Info
• 3 points

SEM-HISTROCL MUSICLGY-BAROQUE

• Department: Music
• Instructor: Giuseppe Gerbino
  CULPA: 12 Info
• 3 points

Operations Strategy

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Operations Strategy

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 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
• Instructor: Todd Ogden
  Info
• 3 points

Service Operations Management

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Service Operations Management

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  Info
• 3 points

Service Operations Management

• Department: Decision, Risk and Operations
• Instructor: Medini Singh
  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
• 3 points

GROUP THEORY

• Department: Chemistry
• Instructor: Jack R Norton
  CULPA: 6 Wikipedia Info
• 2.5 points

Supply Chain Management

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
  Info
• 1.5 points

Supply Chain Management

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
  Info
• 1.5 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
• 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
• Instructor: David Klass
  Info
• Instructor: Blair Singer
  Info
• Instructor: Carter Bays
  Info
• Instructor: Blair Singer
  Info
• 3 points

SCENE STUDY

SCENE STUDY: THE GREEKS

• Department: Theatre Arts
• Instructor: Brian Kulick
  Info
• 3 points

Commercial Society

Before capitalism, there was commercial society. This course examines European debates about commerce, luxury, and social organization from the late seventeenth through the late eighteenth centuries. We will survey a range of theoretical perspectives on the new forms of commercial sociability and political life emerging in Europe, whether triumphant, despairing, or ambivalent.

• Department: Political Science
• Instructor: Daniel Luban
  Info
• 4 points

Applied Regression Analysis

• Department: Decision, Risk and Operations
• Instructor: Paul Glasserman
  Info
• 1.5 points

Leading Sustainable Operations

• Department: Decision, Risk and Operations
• Instructor: Nicole Dehoratius
  Info
• 1.5 points

Design of Medical Experiments

DESIGN OF MEDICAL EXPERIM

This course covers the fundamental principles and techniques of experimental designs in clinical studies. This is a required course for MS, DrPH and Ph.D. in Biostatistics. Topics include reliability of measurement, linear regression analysis, parallel groups design, analysis of variance (ANOVA), multiple comparison, blocking, stratification, analysis of covariance (ANCOVA), repeated measures studies; Latin squares design, crossover study, randomized incomplete block design, and factorial design.

• Department: Biostatistics
• Instructor: Helen Li
  Info
• 3 points

Business Analytics III: Modern AI, Deep

Business Analytics III: A

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 1.5 points

Business Analytics III: Modern AI, Deep

Business Analytics III: A

• Department: Decision, Risk and Operations
• Instructor: Charles R Guetta
  Info
• 1.5 points

Transforming the Delivery of H

• Department: Decision, Risk and Operations
• Instructor: Brian Regan
  Info
• 1.5 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
• 3 points

ANALYSIS OF CATEGORICAL DATA

• Department: ONLINE MS IN EPIDEMIOLOGY
• Instructor: Tonguc Yaman
  Info
• 3 points

Humans, Animals and Machines

Political theory in the 21st century must address the role of non-humans and examine the traditional priority of human beings.  What rights, if any, should animals and AI have and on what basis?  Can we even say what it means to be distinctively human?  Can that distinctiveness support consequential moral or political conclusions?  Is there a clear basis for human or non-human equality which can ground rights or democratic institutions?  Or must we find a post-humanist politics of some kind?

• Department: Political Science
• 4 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
• 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
• Instructor: Linda Valeri
  h-index: 24 Info
• 3 points

Intro to Biology Course (T3ST)

Test Course for Vergil Launch Demonstration

• Department: OFFC OF THE REGISTRAR
• Instructor: Faculty
  h-index: 37 Info
• 2 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
• Instructor: Daniel S Malinsky
  Info
• 3 points

DIGITAL LITERACY-DECISION MKRS

• Department: Decision, Risk and Operations
• Instructor: Marco A Morales Barba
  Info
• 1.5 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
• 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
• Instructor: Peter Fernandez
  Wikipedia Info
• 3 points

Sports Analytics

• Department: Decision, Risk and Operations
• Instructor: Mark N Broadie
  Info
• 3 points

Digital Health Startups

• Department: Decision, Risk and Operations
• Instructor: Halle Tecco
  Wikipedia Info
• 1.5 points

Intro to Databases for Busines

• Department: Decision, Risk and Operations
• Instructor: Mattan Griffel
  Info
• 1.5 points

Intro to Databases for Busines

• Department: Decision, Risk and Operations
• Instructor: Mattan Griffel
  Info
• 1.5 points

Antiquity in the Museum: Archaeology, Cu

The seminar introduces graduate students to works of ancient art and architecture held in museum collections. It explores the modern history of their study as antiquities, a category which required a detailed connoisseurship set within a framework of newly arising aesthetic and racial theories and classifications that accompanied imperial archaeological endeavour. The seminar’s focus is on Mesopotamian, Anatolian, Egyptian and Greek antiquities, as ancient works in their original context and as extracted objects that mark an imperial trail. Students will also be introduced to the development of archaeological field methods within the colonial context, and archaeology’s varied forms of visual documentation which became instrumental to imperial knowledge production: architectural and scientific illustrations, excavation images, and archaeological photography, and by the early twentieth century, the introduction of aerial photography as a way of visualizing sites and ruins. Taking ancient works and their display as a starting point, the seminar also explores the ways in which archaeology and the collecting of antiquities were inextricably linked to the technologies and economies of empire and colonialism. Reading and discussions include museum histories and theories of collecting, as well as the history and theories of archaeology and ancient art. Permission of the instructor is required before registration. Please submit a seminar application to the Department of Art History and Archaeology.

• Department: Art History and Archaeology
• Instructor: Zainab Bahrani
  CULPA: 11 Wikipedia Info
• 4 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
• 3 points

Strategic Negotiations

• Department: Decision, Risk and Operations
• Instructor: Harborne Stuart
  h-index: 10 Info
• 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
• Instructor: Cheng-Shiun Leu
  h-index: 28 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
• 3 points

PRACTICE MANAGEMENT

• Department: Dental and Oral Surgery
• 0 points

Python for MBAs

• Department: Decision, Risk and Operations
• Instructor: Sunny Israni
  Info
• 1.5 points

Analysis of Longitudinal Data

ANALYSIS OF LONGITUDINAL

The course will introduce students to statistical models and mthods for longitudinal data, i.e., repeatedly measured data over time or under different conditions. The topics will include design and sample size calculation, Hotelling's T^2, multivariate analysis of variance, multivariate linear regression (Generalized linear models), models for correlation, unbalanced repeated measurements, Mixed effects models, EM algorithm, methods for non-normally distributed data, Generalized estimating equations, Generalized linear mixed models, and Missing data.

• Department: Biostatistics
• Instructor: Zhonghua Liu
  Info
• 3 points

Vibe Coding

• Department: Decision, Risk and Operations
• Instructor: Mattan Griffel
  Info
• 1.5 points

Management of Patients with Special Need

MGMT OF PTS WITH SPEC NDS

• Department: Dental and Oral Surgery
• Instructor: Marc W Michalowicz
  Info
• 0 points

AI Solutions: From Diagnostics to Deploy

• Department: Decision, Risk and Operations
• 1.5 points

Computing for Investment Analysis

This course is a medium-level introduction to Python and its applications in fundamental analysis, especially estimating the value of companies from fundamental analysis and SEC filings. (If you need a complete introduction, please follow the online "CBS Python Level 1," a prerequisite to this course that I assume all of you have taken. To quote Warren Buffett, we want to automate the work of finding "outstanding companies at sensible prices."

In the first half of the course, we build a quantitative discounted cash flow model in Python to estimate a company's value. We start with a simple one-line formula for the net present value from free cash flow and the average cost of capital. We then add refinements, such as a continuation value, revenue growth predicted from GDP growth, and sensitivity analysis. At the end, we have a tool that takes a stock ticker like "MSFT" and automatically computes a range of intrinsic values for the share price. We apply this tool to US public companies and obtain a list of the most undervalued and overvalued companies.

In the second half of the course, we consider one possible reason for this discrepancy. If a company is under-valued or over-valued, what could the market know that we do not? Is there information that we are missing? Yes, indeed: so far, we have used a company's public disclosures to provide numbers for our quantitative model, but one important piece of information we have not considered is the text. We therefore build a qualitative model from the textual information in a company's public filings. For example, a company may be under federal investigation, and investors are therefore justifiably pessimistic about its future, which could explain a low share price. We therefore apply "text mining" to the risk disclosures in a company's 10-K filing. Our qualitative model identifies new risks within the company and issues a recommendation: "buy" or "sell."

If our quantitative model predicts that a stock is undervalued (the current share price is low relative to fundamentals) and our qualitative model finds no red flags in the risk disclosures, we can recommend the company with reasonable confidence as a "buy." Conversely, if our quantitative model predicts that a company is overvalued and has risk disclosures, we can recommend that the company is a "sell." We then form a long-short portfolio.

Both of these models, quantitative and qualitative, fit into the general 3-part chain below:

- Data (What?): Data gathering,

• Department: Decision, Risk and Operations
• Instructor: Miguel Morin
  Info
• 3 points

COLLOQ IN ATLANTIC HISTORY

This colloquium provides an intensive exploration of the Atlantic World during the early modern era. Readings will attend to the sequence of contact, conquest, and dispossession that enabled the several European empires to gain political and economic power. In this regard, particular attention will be given to the role of commerce and merchant capitalism in the formation of the Atlantic World. The course will focus also, however, on the dynamics of cultural exchange, on the two-way influences that pushed the varied peoples living along the Atlantic to develop new practices, new customs, and new tastes. Creative adaptations in the face of rapid social and cultural change will figure prominently in the readings. Students may expect to give sustained attention the worlds Africans, Amerindians, and Europeans both made together and made apart.

• Department: History
• Instructor: Christopher L Brown
  CULPA: 5 Info
• 4 points

Relational Databases and SQL Programming

RELATIONAL DATABASES AND

In this course, you will learn to design and build relational databases in MySQL and to write and optimize queries using the SQL programming language. Application of skills learned in this course will be geared toward research and data science settings in the healthcare field; however, these skills are transferable to many industries and application areas. You will begin the course examining the pitfalls of using Excel spreadsheets as a data storage tool and then learn how to build properly-designed relational databases to eliminate the issues related to spreadsheets and maintain data integrity when storing and modifying data. You will then learn two aspects of the SQL programming language: 1) the data manipulation language (DML), which allows you to retrieve data from and populate data into database tables (e.g., SELECT, INSERT INTO, DELETE, UPDATE, etc.), and 2) the data definition language (DDL), which allows you to create and modify tables in a database (e.g., CREATE, ALTER, DROP, etc.). You will additionally learn how to optimize SQL queries for best performance, use advanced SQL functions, and utilize SQL within common statistical software programs: R and SAS.

• Department: Biostatistics
• Instructor: Zoe Verzani
  Info
• 3 points

Relational Databases and SQL Programming

RELATIONAL DATABASES AND

In this course, you will learn to design and build relational databases in MySQL and to write and optimize queries using the SQL programming language. Application of skills learned in this course will be geared toward research and data science settings in the healthcare field; however, these skills are transferable to many industries and application areas. You will begin the course examining the pitfalls of using Excel spreadsheets as a data storage tool and then learn how to build properly-designed relational databases to eliminate the issues related to spreadsheets and maintain data integrity when storing and modifying data. You will then learn two aspects of the SQL programming language: 1) the data manipulation language (DML), which allows you to retrieve data from and populate data into database tables (e.g., SELECT, INSERT INTO, DELETE, UPDATE, etc.), and 2) the data definition language (DDL), which allows you to create and modify tables in a database (e.g., CREATE, ALTER, DROP, etc.). You will additionally learn how to optimize SQL queries for best performance, use advanced SQL functions, and utilize SQL within common statistical software programs: R and SAS.

• Department: Biostatistics
• 3 points

Effective Management in the Public Servi

Management in Public Serv

This course equips mid-career professionals with actionable frameworks, tools, and insights to lead organizational change, drive performance, and manage complex challenges in the public and nonprofit sectors. Across 12 highly interactive sessions, students examine case-based scenarios that explore how managers conceive and implement value-driven strategies, navigate organizational dynamics, and deliver measurable results.

Key themes include strategic planning, performance management, people development, operational platforms, and using power and persuasion to implement change. Through analysis of real-world cases, structured reflection, and applied learning, students will strengthen their ability to frame policy decisions, mobilize resources, and lead through uncertainty.

This course builds leadership capacity for professionals seeking to lift performance, transform services, and create public value. Students will leave with a set of durable management tools and lessons applicable across roles, organizations, and stages of their careers.

• Department: EXECUTIVE MPA
• Instructor: Robert Z Tumin
  Info
• 3 points

Phys & Nutrition Through the Lifecycle

General aspects of normal human growth and development from viewpoints of physical growth, cellular growth and maturation, and adjustments made at birth; the impact of altered nutrition on these processes. Prenatal and postnatal malnutrition, the role of hormones in growth; relationships between nutrition and disease in such areas as anemia, obesity, infection, and carbohydrate absorption.

• Department: Human Nutrition, Institute of
• Instructor: Moneek Madra
  Info
• 3 points

Graduate Seminar in Photo & Related Medi

Grad Sem Photo & Related

Visual Ecologies: Photography, Visual Justice and Environmental Activism. 
Graduate Seminar in Photo & Related Media

This course will explore the intersection of photography, nature, ecology, social and environmental activism, examining art’s role as a catalyst for change. Drawing from critical and historical approaches in photography, landscape studies, architecture, human rights, and interdisciplinary environmental studies, among others, this course offers an exploration of how images can address pressing environmental and social issues while revealing optimism, hope, and collective action in response to our present ecological condition, illuminating the geographies, histories, and ecologies of transformation, liberation, and everyday resistance.

• Department: Visual Arts
• Instructor: Julia Pontes
  Info
• 3 points

NONFICTION THESIS WORKSHOP

• Department: Writing
• Instructor: Chloe Jones
  Info
• Instructor: Jaquira Diaz
  Wikipedia Info
• Instructor: Leslie S Jamison
  Wikipedia Info
• Instructor: Wendy Walters
  Info
• 9 points

SAS Insights for CEOR

Data is most useful when it can tell a story. Health analytics merges technologies and skills used to deliver business, clinical and programmatic insights into the complex components that drive medical outcomes, costs and oversight. By focusing on business intelligence and developing tools to evaluate clinical procedures, devices, and programs, organizations can use comparative and outcomes data to strengthen financial performance. This information can improve the way healthcare is evaluatedand delivered for better outcomes across the spectrum of health industries.

 

In this course, students will learn SAS as a tool to manipulate and analyze healthcare data and begin to understand what clinical and public health interventions work best for improving health, for example. Students will learn how to organize and analyze data to inform the practices of healthcare providers and policymakers to make evidence-based resource allocation decisions.Comparative & Effectiveness Outcomes Research (CEOR) certificate students will take this course inpreparation for the capstone class.SAS basics (e.g., creating SAS datasets and new variables, sorting, merging, reporting) and advanced statistics (e.g., using a logistical regression to create propensity scores for matched cohort analyses) will be covered.

• Department: Health Policy & Management
• Instructor: Yongmei Huang
  h-index: 22 Info
• 1.5 points

Critical Read of Biological Clinical Lit

Crit Read of Bio Clin Lit

Fall: Review of current literature providing complementary information pertinent to other nutrition areas, with a view to developing a critical approach to the assimilation of scientific information. Spring: Obesity: Etiology, Prevention, and Treatment. Controversies involving regulation of weight and energy balance. Interaction between genetics and the environment are considered as well as clinical implications of our current knowledge.

• Department: Human Nutrition, Institute of
• 3 points

NEW DIRECTION IN ECON SOCIOLOGY

NEW DIRECTIONS IN ECONOMIC SOC

This is an advanced graduate seminar in Economic Sociology looking at new developments in this field. It addresses the disciplinary division of labor in which economists study value and sociologists study values; and it rejects the pact whereby economists study the economy and sociologists study social relations in which they are embedded.

• Department: Sociology
• Instructor: David Stark
  CULPA: 15 Wikipedia h-index: 48 Info
• 3 points

Theoretical Statistics

STAT ANALYSIS-NEURAL DATA

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• Department: Statistics
• Instructor: Liam M Paninski
  CULPA: 4 Info
• 3 points

Behavioral Policy & Public Health

Applications of behavioral insights are expanding rapidly across civic, medical, social, corporate, educational, and economic professions. This class covers the underlying theories for behavioral insights, using scientific and real-world examples of applications from multiple disciplines and locations. The course will also cover methods for behavioral implementation and evaluation, focusing particularly on healthcare policy perspectives. Students will learn a broad range of strategies through a highly interactive format, taught partially in a classroom setting in addition to remote asynchronous and synchronous sessions. Students will gain experience designing and developing their own evidence- based behavioral interventions as a part of a semester-long project.

The course is taught in three phases. The first phase will introduce fundamentals of behavioral science and evidence-based policy. Students will then spend the majority of the course on examples of behavioral insights such as nudges in practice, in a healthcare context and beyond. The course will end with sessions on practical applications, where students will learn to identify appropriate situations for behavioral interventions and produce a final project in a chosen context.

• Department: Health Policy & Management
• Instructor: Kai Ruggeri
  h-index: 22 Info
• 1.5 points