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: Julia Doe
  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: Georg Friedrich Haas
  Info
• Instructor: Elaine Sisman
  CULPA: 13 Wikipedia Info
• Instructor: Benjamin A Steege
  Info
• Instructor: Ruth Opara
  Info
• Instructor: Marcos Balter
  Wikipedia 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: Martina Pavlicova
  h-index: 30 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
• MW 11:40 a.m.-12:55 p.m.
• MW 2:40 p.m.-3:55 p.m.
• Instructor: Lily Xu
  Info
• Instructor: Daniel Lacker
  CULPA: 4 h-index: 13 Info
• 3 points

ADVANCED TOPICS IN IEOR

High-dimensional Prob w/Applic

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

• Department: Industrial Engineering and Operations Research
• TR 4:10 p.m.-5:25 p.m.
• Instructor: Kaizheng Wang
  CULPA: 1 h-index: 8 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
• MW 10:10 a.m.-11:25 a.m.
• TR 11:40 a.m.-12:55 p.m.
• Instructor: Vineet Goyal
  h-index: 21 Info
• Instructor: Wenpin Tang
  h-index: 6 Info
• 3 points

Introduction to Health Data Science

Intro to Health Data Scie

This course will introduce students to core data science skills and concepts through the exploration of applied biostatistics. The course will begin with an introduction to the R programming language and the RStudio IDE, focusing on contemporary tidyverse functions and reproducible programming methods. Then, the course will instruct students in contemporary data manipulation and visualization tools while systematically covering core applied biostatistics topics, including confidence intervals, hypothesis testing, permutation tests, and logistic and linear regression. Finally, the semester will end with an introduction to machine learning concepts, including terminology, best practices in test/training sets, cross-validation, and a survey of contemporary classification and regression algorithms.

• Department: Biostatistics
• MW 4 p.m.-5:20 p.m.
• 3 points

Ex. In-House Counsel

• Department: Law
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

Ex. In-House Counsel

• Department: Law
• 2 points

TOPICS IN APPLIED STATISTICS

Applied Causality

.

• Department: Statistics
• T 10:10 a.m.-noon
• Instructor: David Blei
  Wikipedia h-index: 98 Info
• 3 points

Process Improvement and Growth

This class brings business operations and management science classes to the field with real-world experience. Through experiential learning, we will bridge the gap between theory and practice with international case discussions, conversations with guest speakers and hands-on company sponsored projects. Different to most classes in the school, in this class students will be exposed to a series of international cases and examples based on medium-sized, fast-growing entrepreneurial ventures. Each session will also include a guest speaker, often times the protagonist of the case studied, giving the students the opportunity to learn directly from successful entrepreneurs and senior executives.
Additionally, students will put into practice the concept of process improvement by working on a company-sponsored applied project. Teams of 4-5 people, 3-4 MBA/EMBA students and 1-2 engineering (SEAS) students, will work hand in hand with the instructors and company representatives to achieve company goals. For example, teams may be tasked with re-designing the logistical strategy of distribution of the company to get rid of inefficiencies, or identify and find strategies to eliminate areas of waste within the companies’ processes, or analyze customer feedback and design operational solutions to increase customer satisfaction, etc.
Enrollment in this course is by application only. To apply, please follow this link: https://forms.gle/EG6buNZqQYEgN2EH9

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

VISUAL ART LAB, SPRING

VISUAL ART LAB SPRING

Students meet with the professor and pave the transition from graduate students to seeing themselves as artists with a long term working creative perspective beyond academia. The professor will work to contextualize the students body of work in the arena of an international art conversation. VISUAL ART LAB will be led by Sarah Sze in the Spring.

Schedule:
Priority will be given to all second-year students who submit a short presentation of their work. Should there be remaining room for first year students they will be admitted upon review. To apply please submit a brief description of work, current research and interest in taking the seminar, along with 5 - 10 images. There will be one half hour meeting for each student with professor Sze throughout the Spring Semester.

Requirements:
Rigorous development of students' own body of work.

• Department: Visual Arts
• Instructor: Sarah M Sze
  CULPA: 1 Wikipedia 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

Business Analytics II: Foundations of AI

Business Analytics II: AI

Business analytics refers to the ways in which enterprises such as businesses, non-profits, and governments use data to gain insights and make better decisions. Business analytics is applied in operations, marketing, finance, and strategic planning among other functions. Modern data collection methods – arising in bioinformatics, mobile platforms, and previously unanalyzable data like text and images – are leading an explosive growth in the volume of data available for decision making. The ability to use data effectively to drive rapid, precise, and profitable decisions has been a critical strategic advantage for companies as diverse as Walmart, Google, Capital One, and Disney. Many startups are based on the application of AI & analytics to large databases. With the increasing availability of broad and deep sources of information – so-called “Big Data” – business analytics are becoming an even more critical capability for enterprises of all types and all sizes. AI is beginning to impact every dimension of business and society. In many industries, you will need to be literate in AI to be a successful business leader. The Business Analytics sequence is designed to prepare you to play an active role in shaping the future of AI and business. You will develop a critical understanding of modern analytics methodology, studying its foundations, potential applications, and – perhaps most importantly – limitations.

• Department: Decision, Risk and Operations
• T 2:20 p.m.-5:35 p.m.
• W 9 a.m.-12:15 p.m.
• W 2:20 p.m.-5:35 p.m.
• Instructor: Hongseok Namkoong
  h-index: 11 Info
• 1.5 points

Data Science II

DATA SCIENCE II

With the explosion of “Big Data” problems, statistical learning has become a very hot field in many scientific areas. The goal of this course is to provide the training in practical statistical learning. It is targeted to MS students with some data analysis experience.

• Department: Biostatistics
• TR 4 p.m.-5:20 p.m.
• Instructor: Yifei Sun
  h-index: 7 Info
• 3 points

Operations Strategy

A firm's operations encompass all the activities that are performed in order to produce and deliver a product or a service. An operations strategy refers to a set of operational decisions that a firm makes to achieve a long-term competitive advantage. These decisions may be about the firms facilities, its technology/process choices, its relationships with both upstream and downstream business partners etc. The goal of this course is to provide students with an understanding of how and why operational decisions are integral to a firms success. The course builds on concepts from the core Operations Management course and the core Strategy Formulation course. It is highly relevant to anyone whose work requires the strategic analysis of a firms operations, including those interested in consulting, entrepreneurship, mergers and acquisitions, private equity, investment analysis, and general management. The course consists of four modules. The first module, Strategic Alignment," explores the question of how a firms operations should be structured so as to be consistent with the firms chosen way to compete. The second module, "Firm Boundaries," considers the question of what operational activities should remain in house and what should be done by a business partner and the long-term implications of these decisions on competitive advantage. This module also addresses the issue of managing the business relationships with supply chain partners. The third module, "Internal Operations," considers key decision categories in operations, e.g., capacity decisions, process choices, IT implementation, and managing networks, and shows how these decisions can lead to distinctive capabilities. The final module, "New Challenges," is set aside to address new topics that reflect the current trends in the business environment."

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

Statistical Inference

STATISTICAL INFERENCE

This course covers a review of mathematical statistics and probability theory at the Masters level. Students will be exposed to theory of estimation and hypothesis testing, confidence intervals and Bayesian inference. Topics include population parameters, sufficient statistics, basic distribution theory, point and interval estimation, introduction to the theory of hypothesis testing, and nonparametric procedures.

• Department: Biostatistics
• F 1 p.m.-3:50 p.m.
• Instructor: Bin Cheng
  h-index: 32 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
• MW 8:40 a.m.-9:55 a.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
• M 8:30 a.m.-11:20 a.m.
• 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 2 p.m.-5 p.m.
• T 2 p.m.-5 p.m.
• R 2 p.m.-5 p.m.
• Instructor: David Klass
  Info
• Instructor: Blair Singer
  Info
• 3 points

SCENE STUDY

SELF TO PERFORMANCE

• Department: Theatre Arts
• F 10 a.m.-12:50 p.m.
• Instructor: Peter Fernandez
  Wikipedia Info
• 3 points

SCENE STUDY

SCENE STUDY: THE GREEKS

• Department: Theatre Arts
• F 10 a.m.-12:50 p.m.
• Instructor: Brian Kulick
  Info
• 3 points

Sustainable Operations

The importance of designing, building, and leading sustainable organizations is indisputable. Sustainability encompasses not only the environmental footprint of an organization but also the way in which firms treat workers and customers both within their firm and supply chain network. Understanding the role of operational excellence and strategic supply chain management in achieving sustainability is critical for effective leadership.

This course examines a variety of approaches to designing sustainability into an organization’s operations and how to measure and reduce a firm’s operational environmental impact. We also explore themes of risk, accountability, and sustainability within global supply chains. What challenges do firms face in being socially responsible when managing globally distributed supply chains? Three themes comprise this course: (1) designing sustainable operations, (2) drivers and consequences of sustainability, and (3) global sourcing and social responsibility.

• Designing Sustainable Operations. Sample cases include – REI Rentals, All Birds, IndigoAg, Supply Chain Hubs in Humanitarian Logistics.
• Drivers and Consequences of Sustainability. Sample cases include – Fiji Water, Aspen Ski Company.
• Global Sourcing & Social Responsibility. Sample cases include – IKEA, Ready Made Garment Industry, Roche & Tamiflu.

• Department: Decision, Risk and Operations
• T 2:20 p.m.-5:35 p.m.
• Instructor: Nicole Dehoratius
  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
• Instructor: Mavis K Fowler
  Info
• Instructor: Jay A Hewlin
  Info
• Instructor: Katerina M Yiannibas
  Info
• 3 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
• Instructor: Katerina M Yiannibas
  Info
• Instructor: Edward C Frischling
  Info
• 3 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
• 3 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
• Instructor: Avery W Katz
  Info
• 3 points

Risk Management

• Department: Decision, Risk and Operations
• MW 6 p.m.-7:30 p.m.
• Instructor: Evan C Picoult
  Info
• 3 points

S ADV NEGIATION WORKSHOP

course decription

• Department: Law
• Instructor: David S Ross
  Info
• 3 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 9 a.m.-11:50 a.m.
• Instructor: Iuliana Ionita-Laza
  h-index: 31 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
• MR 4 p.m.-5:20 p.m.
• 3 points

Accounting for Consultants

This elective course covers accounting tools useful to consultants, as well as for students with an interest in a firm’s finance function, general management, or private equity.
There will be a particular focus on performance measurement and management.
Performance measurement is a key determinant of success for today’s companies that sell a wide range of products and services to a wide range of customers differentiated in their needs. While financial accounting (GAAP) information is a useful shortcut toward gaining some understanding of a firm’s financial health, consultants and managers need a more solid understanding of the firm’s strategy and mission, as well as disaggregated information that helps assess how the firm is performing along its strategic objectives.
There is overlap between this course and the half-semester course “Financial Planning & Analysis (FP&A)” course. This course expands on many of the concepts taught in FP&A and supplements them with industry insights and guest speakers. For this reason, this course is mutually exclusive with the elective course “B8007 – Financial Planning & Analysis”. If you have taken FP&A, you will not be able to enroll in this course for credit. Please contact me immediately in case of such a conflict.
The following specific topics will be addressed:
• Profitability analysis to assess individual products
• Customer relationship management using customer lifetime value (CLV)
• Budgeting and variances
• Performance evaluation for profit centers and investment centers
• Performance-based pay: team incentives, relative performance evaluation, etc.
• Corporate governance: the C-suite and the role of compensation consultants
• The “War of Metrics”: Cash Flow, EVA, Balanced Scorecards, etc.
• Innovative ways to deviate from GAAP rules to better measure value creation
• Issues specific to multinational enterprises (MNEs), e.g., taxation
• Industry-specific insights: performance measurement in key industries

• Department: Accounting (ACCT)
• MW 9 a.m.-12:15 p.m.
• Instructor: Tim Baldenius
  Info
• 3 points

Intro to Biology Course (T3ST)

Test Course for Vergil Launch Demonstration

• Department: OFFC OF THE REGISTRAR
• 3 points

DIGITAL LITERACY-DECISION MKRS

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
• T 9 a.m.-12:15 p.m.
• Instructor: Marco A Morales Barba
  Info
• 1.5 points

Programming Generative AI Applications

Generative Artificial Intelligence is a type of AI that learns patterns from data to create new content in various types of media (text, images, audio, video). At its heart a generative AI system has a large language model (LLM) that is essentially a large (trillions of parameters) neural network that has been trained on a mix of vast amounts of data as well as human input. Applying generative AI to actual problems in business often requires that the LLM underlying the AI be customized to the business problem, either by attaching a data source (e.g., operating procedures, 10k reports, marketing plans, balance sheets, etc.) to the LLM (a process known as Retrieval-Augmented Generation or RAG) or by retraining the neural net with additional data (a process known as fine tuning). adjusting the parameters of the underlying LLM. Embedding generative AI into organizational processes requires
that we gather appropriate data and reprogram the LLM to use the data either through RAG or fine tuning.

The focus of this course is to give you a working knowledge of what it takes to customize and assemble a customized generative AI application. We will use OpenAI’s GPT as our base model and learn how to build a RAG and how to customize using simple fine tuning. About 50% of the class time will be devoted to a group project where you will, in small groups, build your own customized AI application. All programming will be in Python and we will use libraries like tensorflow, langchain and faiss.

STUDENTS WILL NEED TO COMPLETE AN INTRODUCTORY PYTHON CLASS (https://courseworks2.columbia.edu/courses/152704) OR PASS THE BASIC PYTHON QUALIFICATION EXAM (https://cbs-python.com/) BEFORE THE FIRST DAY OF CLASS. SEE https://academics.gsb.columbia.edu/python FOR DETAILS

• Department: Decision, Risk and Operations
• TWRFS 9 a.m.-5 p.m.
• Instructor: Hardeep Johar
  Info
• 3 points

Healthcare Venture Capital And Private E

Healthcare Venture Cap &

This course analyzes the unique characteristics and strategies of investing in the healthcare sector from the perspectives of venture capital firms investing in early-stage healthcare enterprises, entrepreneurs creating and managing such business entities, and private equity firms seeking to build value-creating health care platforms. The course is focused on innovative business models of early to mid-stage healthcare services companies (payers, providers, HCIT firms) that improve quality of patient care, lower costs, and facilitate access to such services, as well as the opportunities and challenges of early-stage biotechnology companies discovering and developing novel compounds. It considers how investors and entrepreneurs can assess, value and manage the inherent risks to succeed in this large, complex, and dynamic sector. This course will address these issues through a mixture of lectures, case studies, and guest speakers (investors and entrepreneurs) from the healthcare sector. Note: Some understanding and prior experience in the healthcare/pharma industry will be highly useful. Students need to attend the first class session to understand material covered later in the course. Evaluation is 25% class participation, 25% mid-term assignment (short paper on questions or case study), and 50% final (individual) paper. "

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

Biostatistical Methods II

BIOSTATISTICAL METHODS II

Regression analysis is widely used in biomedical research. Non-continuous (e.g., binary or count-valued) responses, correlated observations, and censored data are frequently encountered in regression analysis. This course will introduce advanced statistical methods to address these practical problems. Topics include generalized linear models (GLM) for non-Gaussian response, mixed-effects models and generalized estimating equations (GEE) for correlated observations, and Cox proportional hazards models for survival data analysis. Examples are drawn from biomedical sciences.

• Department: Biostatistics
• WF 10 a.m.-11:20 a.m.
• Instructor: Bin Cheng
  h-index: 32 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
• T 6 p.m.-9 p.m.
• Instructor: Luke C Beasley
  Info
• 3 points

THE JEWISH BOOK IN EARLY MOD WORLD

This course will situate the Jewish book within the context of the theoretical and historical literature on the history of the book: notions of orality and literacy, text and material platform, authors and readers, print and manuscript, language and gender, the book trade and its role in the circulation of people and ideas in the early age of print.

• Department: History
• M 12:10 p.m.-2 p.m.
• Instructor: Elisheva Carlebach
  CULPA: 6 Wikipedia Info
• 4 points

S ADV INTER COMM ARBITRATION

• Department: Law
• Instructor: Robert H Smit
  CULPA: 13 Info
• 2 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 10:50 a.m.-12:20 p.m.
• Instructor: Mattan Griffel
  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 10:50 a.m.-12:20 p.m.
• Instructor: Mattan Griffel
  Info
• 1.5 points

Statistical Genetic Modeling

STATISTICAL GENETIC MODEL

This course explores the theoretical foundations underlying the models and techniques used in mathematical genetics and genetic epidemiology. Topics include use and interpretation of likelihood methods, formulation of mathematical models, segregation analysis, ascertainment bias, linkage analysis, genetic heterogeneity, and complex genetic models. The course includes lectures, discussions, homework problems, and a final exam. My single most important objective for this course is for students to be able to break down any mathematical modeling problem logically into all its component parts, to express each part" accurately, and to know how to "add" all the pieces back up and to check the accuracy of their result."

• Department: Biostatistics
• T 1 p.m.-3:50 p.m.
• Instructor: Shuang Wang
  Info
• 3 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

ACTING IV

TBD

• Department: Theatre Arts
• MT 2:10 p.m.-5 p.m.
• Instructor: Peter Fernandez
  Wikipedia Info
• 3 points

Pharmaceutical Statistics

PHARMACEUTICAL STATISTICS

The drug development from compound discovery to marketing and commercialization registration is a lengthy and complex process in which statisticians play an important role from the beginning to the end. The main objective of this course is to provide students with working knowledge of methodological and operational issues that arise in different stages of the drug development that involve statistical contributions.
Topics include: Introduction of drug development; design and analysis of non-clinical studies (toxicology, pharmacokinetics and pharmacodynamics) and Phase I/II/III studies; issues in clinical studies including non-inferiority, meta-analysis, and endpoint selection; overview of safety reporting systems such as MedDRA (Medical Dictionary for Regulatory Activities), CTC version 3 (Common Terminology Criteria for Adverse Events), and preparation for the FDA advisory committee drug approval process. In addition, the views and positions of different regulatory bodies, such as the FDA or EMEA, on design and analysis issues will be discussed.

• Department: Biostatistics
• T 2:30 p.m.-5:20 p.m.
• Instructor: Naitee Ting
  Info
• 3 points

Complex Care Seminar I

This course is designed to expand the clinical reasoning, diagnostic acumen, and management skills of Family Nurse Practitioner (FNP) students when dealing with complex patient cases. Emphasizing multifaceted conditions, comorbidities, and intricate care coordination, students will analyze patient cases requiring advanced critical thinking and interprofessional collaboration.

• Department: Nursing
• 2 points

Applying Digital Health

• Department: Decision, Risk and Operations
• TR 2:20 p.m.-3:50 p.m.
• Instructor: Raymond G Falci
  Info
• 1.5 points

Complex Care Practicum I

This clinical course is designed to further develop the role of the student to provide care to individuals with complex, comorbid, advanced, or terminal illness and their families.

• Department: Nursing
• Instructor: Ellen M Fahey
  Info
• 3 points

The Analytics Advantage

This class will focus on how analytics have generated value in a broad range of industries. Each class will be taught by a different faculty member with specific subject matter expertise and will focus on one specific industry and on how it has been transformed through the use of analytics.
DROMB8152

• Department: Decision, Risk and Operations
• R 9 a.m.-12:15 p.m.
• R 2:20 p.m.-5:35 p.m.
• 1.5 points

ACTING TECHNIQUES

ACTING FOR CAMERA

• Department: Theatre Arts
• F 2 p.m.-6 p.m.
• Instructor: James Calleri
  Info
• 3 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
• T 6 p.m.-9:15 p.m.
• R 2:20 p.m.-5:35 p.m.
• Instructor: David L Bendes
  Info
• Instructor: Mattan Griffel
  Info
• 1.5 points

Knowledge and Materiality in the Early M

Knowledge/Materiality Ear

Intended for advanced graduate students, this course considers classic and recent works in materiality and material culture in the early modern period (ca. 1400-1700), especially as they are fruitful for the history of science and knowledge. Class sessions will include discussion, museum visits, and hands-on work in the Making and Knowing Lab. Topics to be considered: embodied knowledge, material complexes, materialized concepts and identities, agentive matter, human-environment relations, and material imaginaries.

• Department: History
• MW 10:10 a.m.-noon
• Instructor: Pamela Smith
  CULPA: 4 Info
• 4 points

Latent Variable and Structural Equation

LATENT-VAR-STRUC EQUATION

This course is designed for those students (or any researchers) who want to gain a significant familiarity with a collection of statistical techniques that target the measurement of latent variables (i.e. variables that cannot be measured directly) as well as methods for estimating relationships among variables within causal systems. This course covers: both continuous and categorical latent variable measurement models (i.e. exploratory and confirmatory factor analysis, item response theory models, latent class and finite mixture models), as well as estimation of relationships in hypothesized causal systems using structural equation modeling. Data analysis examples will come from health science applications and practical implementation of all methods will be demonstrated using predominately the Mplus software, but also the R software.

• Department: Biostatistics
• W 8:30 a.m.-11:20 a.m.
• Instructor: Melanie Wall
  Wikipedia h-index: 101 Info
• 3 points

Digital Marketplaces

This course will cover some of the fundamental product decisions together with the basic analytic and data science tools to support them that are currently being used to run the most exciting online marketplaces in the world. More specifically, among others, we will address the following questions: How does Uber or Lyft match drivers to passengers? How does Airbnb select the set of listings to show to a guest in a search? How can we build an algorithmic, scalable reputation and trust system in an e-commerce platform such as Amazon? How should advertisers optimize their decisions in today’s online advertising marketplaces run by Google and others?
Verticals of interest include the following:
• Matching platforms like those for ride-hailing, lodging, dating, labor, and food delivery.
• Internet advertising platforms including search engine advertising, display advertising, and sponsored products.
• Retail platforms including those for physical goods like Amazon, Etsy, and possibly also those for virtual goods like the App Store/Play Store and gaming platforms.

• Department: Decision, Risk and Operations
• MW 10:50 a.m.-12:20 p.m.
• Instructor: Yashodhan Kanoria
  h-index: 18 Info
• 1.5 points

Topics in Advanced Statistical Computing

TPCS IN ADVNCD STAT COMPU

As statistical models become increasingly complex, it is often the case that exact or even asymptotic distributions of estimators and test statistics are intractable. With the continuing improvement of processor speed, computationally intensive methods have become invaluable tools for statisticians to use in practice. This course will cover the basic modern statistical computing techniques and how they are applied in a variety of practical situations. Topics to be covered include numerical optimization, random number generation, simulation, Monte Carlo integration, permutation tests, jackknife and bootstrap procedures, Markov chain Monte Carlo methods in Bayesian settings, and the EM algorithm.

• Department: Biostatistics
• M 8:30 a.m.-11:20 a.m.
• Instructor: Ying Wei
  Wikipedia Info
• 3 points

Intro to Machine Learning and AI

Intro to ML / AI

This course will provide students with the applied skills and conceptual understandings necessary to reason about, critique, conceptualize and apply key artificial intelligence (AI) technologies to their domain. Specifically, this course will provide students with a high-level understanding of the essential algorithmic, logical, statistical and computing principles that drive the systems currently described as "artificial intelligence," including linear and logistic regression, penalized regression, random forests, support vector machines (SVMs), deep learning, natural language processing (NLP) and large-language models (LLMs). The approach of this course is interdisciplinary, and we will approach interacting with these tools on two levels. The first is to understand the basic principles, assumptions and tradeoffs that each system leverages to achieve its results. The second is a "use-modify-create" approach to interacting with these technologies in the Python programming language. To achieve this, a large portion of early assignments will be focused on building your applied Python programming skills so that they can be leveraged towards domain-relevant examples and problems in the latter half of the term.

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

Integrative Capstone Experience

INTEGRATIVE CAPSTONE EXPE

In this course students will synthetize knowledge from the core with knowledge from both specific department required courses and from certificate required courses. The course deliverable is a written paper combining analyses of a student’s selected data set that uses two of the following methods: (linear regression, logistic regression, nonlinear modeling, mixed effect modeling, machine learning, survival analyses). Students will demonstrate understanding of summarizing (numerically and graphically) data for purposes of specific analyses, presenting results, and interpreting them in the context of public health. Finally, students will also demonstrate the ability to present various stages of the analyses, to ask questions in large collaborative settings, and to troubleshoot their work.

• Department: Biostatistics
• W 2 p.m.-3:50 p.m.
• Instructor: Martina Pavlicova
  h-index: 30 Info
• 2 points

S CORPORATE GOVERNANCE

• Department: Law
• Instructor: Zohar Goshen
  h-index: 12 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
• T 5:30 p.m.-8:20 p.m.
• 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
• T 5:30 p.m.-8:20 p.m.
• 3 points

Empire and Political Thought

This seminar examines the relationship between the history and theory of empire and the development of modern political thought.  The course is structured around the work of canonical thinkers, politicians, and writers, and considers how they understood and debated the nature and consequences of the imperial experience, from overseas trade and commercial expansion, imperial conquest, competition, and rivalry, to theories of world order and federation.  We will focus on how the history of empire shaped central concepts of political theory such as the state, sovereignty, the nation, property, liberty, and progress.  What were the moral, political, and economic arguments offered for and against empire?  How were domestic politics seen as connected with and constrained by global political interactions and global political structures?  How did imperial powers imagine conditions of stability, progress, and peace?  What was imperial liberalism as a domestic and international ideal?

• Department: Political Science
• F 10:10 a.m.-noon
• 4 points

Capstone Consulting Seminar

CAPSTONE CONSULTING SEMIN

The Capstone Consulting Seminar is a required course for the M.S. Theory and Methods track and M.P.H. students in Biostatistics. It provides experience in the art of consulting and in the proper application of statistical techniques to public health and medical research problems. Students will bring together the skills they have acquired in previous coursework and apply them to the consulting experience. Learning will take place by doing. Over the course of the semester students will attend consultation sessions of the department's Biostatistics Consultation Service. Students will participate in the consultation interaction and will present their report in class for discussion or comment on another student's presentation.

• Department: Biostatistics
• T 1 p.m.-1:50 p.m.
• T 2 p.m.-2:50 p.m.
• W 1 p.m.-1:50 p.m.
• W 2 p.m.-2:50 p.m.
• Instructor: Shing M Lee
  Info
• 1 points

Diagnosis and Management of Acute & Crit

DX & MGMT ACUT CRIT ILLNE

This is the last of three Diagnosis and Management courses designed to educate students on the assessment, diagnosis, treatment and evaluation of common acute and critical illnesses via a systems-based approach. Pathophysiologic alterations, assessment, diagnostic findings, and multimodal management will be discussed.  The course will examine social determinants of health and health disparities that may impact patients and family outcomes. Focus will be on the differential diagnosis and comprehensive healthcare management of commonly encountered acute and chronic physical illnesses using didactic lectures, case studies and simulation.

• Department: Nursing
• M 9 a.m.-11:50 a.m.
• Instructor: Leon Chen
  Info
• 3 points

ECONOMIC SOCIOLOGY

• Department: Sociology
• M 10:10 a.m.-noon
• Instructor: Joshua Whitford
  h-index: 14 Info
• 3 points

Financial Management

Lecture

The course introduces students to budgeting and financial control as a means of influencing the behavior of organizations. Concepts include the budget process and taxation, intergovernmental revenues, municipal finance, bonds, control of expenditures, purchasing, debt management, productivity enhancement, and nonprofit finance. Students learn about the fiscal problems that managers typically face, and how they seek to address them. Students also gain experience in conducting financial analysis and facility with spreadsheet programs. Case materials utilize earth systems issues and other policy issues. A computer lab section is an essential aspect of the course, as it teaches students to use spreadsheet software to perform practical exercises in budgeting and financial management.

• Department: MPA-ENVIRONMENTAL SCIENCE POL
• M 5:10 p.m.-7 p.m.
• Instructor: Rebecca E Koike
  Info
• 3 points

Financial Management

Recitation

The course introduces students to budgeting and financial control as a means of influencing the behavior of organizations. Concepts include the budget process and taxation, intergovernmental revenues, municipal finance, bonds, control of expenditures, purchasing, debt management, productivity enhancement, and nonprofit finance. Students learn about the fiscal problems that managers typically face, and how they seek to address them. Students also gain experience in conducting financial analysis and facility with spreadsheet programs. Case materials utilize earth systems issues and other policy issues. A computer lab section is an essential aspect of the course, as it teaches students to use spreadsheet software to perform practical exercises in budgeting and financial management.

• Department: MPA-ENVIRONMENTAL SCIENCE POL
• T 3:10 p.m.-5 p.m.
• 0 points