Managerial Statistics

• MW 9 a.m.-10:30 a.m.
• MW 4:10 p.m.-5:40 p.m.
• Instructor: Boaz Galinson
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• Instructor: Jing Dong
  h-index: 11 Info
• Instructor: Alex Mills
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• Instructor: Boaz Galinson
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• Instructor: Jing Dong
  h-index: 11 Info
• 1.5 points

Business Analytics

• MW 9 a.m.-10:30 a.m.
• MW 4:10 p.m.-5:40 p.m.
• Instructor: Gustavo J Vulcano
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• 1.5 points

Statistics & Business Analytics

Stats & Business Analytic

• W 2:20 p.m.-5:35 p.m.
• Instructor: Charles R Guetta
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• 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

• T 6 p.m.-9:15 p.m.
• 3 points

Business Analytics II (Full Term)

Business Analytics II (FT

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

• T 2:20 p.m.-5:35 p.m.
• Instructor: Charles R Guetta
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• 3 points

Technology Breakthroughs

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

• MF 2:20 p.m.-5:35 p.m.
• 1.5 points

Service Operations Management

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

• W 9 a.m.-12:15 p.m.
• W 2:20 p.m.-5:35 p.m.
• R 2:20 p.m.-5:35 p.m.
• R 9 a.m.-12:15 p.m.
• Instructor: Medini Singh
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• 3 points

Applied Regression Analysis

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

• TR 10:50 a.m.-12:20 p.m.
• T 9 a.m.-12:15 p.m.
• Instructor: Paul Glasserman
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• Instructor: David Juran
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• 1.5 points

Transforming the Delivery of H

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

• T 9 a.m.-12:15 p.m.
• Instructor: Catherine Foster
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• 1.5 points

Demand Analytics

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

• R 9 a.m.-12:15 p.m.
• Instructor: Awi Federgruen
  Wikipedia Info
• 3 points

DIGITAL LITERACY-DECISION MKRS

Digital Literacy for Decision

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

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

• R 6 p.m.-9:15 p.m.
• Instructor: Desmond F McGowan
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• 3 points

Digital Health Startups

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

• MTWRF 9 a.m.-1 p.m.
• Instructor: Halle Tecco
  Wikipedia Info
• 1.5 points

Intro to Databases for Busines

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

• R 4:10 p.m.-5:40 p.m.
• Instructor: Mattan Griffel
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• 1.5 points

Introduction to Programming in

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

• F 9 a.m.-12:15 p.m.
• Instructor: Jared P Lander
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• 1.5 points

Analytics in Action (Master Cl

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

• T 6 p.m.-9:15 p.m.
• 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.

• R 2:20 p.m.-5:35 p.m.
• W 6 p.m.-9:15 p.m.
• Instructor: Mattan Griffel
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• Instructor: Jason Prestinario
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• 1.5 points

MANAGERIAL NEGOTIATIONS

• MF 6 p.m.-9:15 p.m.
• Instructor: Harborne Stuart
  h-index: 10 Info
• 3 points

The U.S. Healthcare Industry

The U.S. healthcare system is an enormously complex, trillion-dollar industry. It includes thousands of hospitals, nursing homes, specialized care facilities, independent practices and partnerships, web-based and IT supported service companies, managed care organizations, and major manufacturing corporations. Healthcare is the fastest growing component of many consulting practices and investment portfolios. In dollar terms, it accounts for over 18% of GDP and is larger than the total economy of Italy. It continues to grow in size and complexity, complicating the long-standing problems of increasing costs, limited consumer access, and inconsistent quality. And, the historic Affordable Care Act has resulted in significant changes throughout the entire industry and will have major implications for years to come. This tremendous dynamism is unmatched by any other industry and offers incredible opportunities for new business endeavors."

• TR 10:50 a.m.-12:20 p.m.
• Instructor: Carri Chan
  h-index: 17 Info
• 1.5 points

(PhD) Advanced Marketing Analytics

(PhD) Applied Multivariat

Multivariate statistical techniques are important tools of analysis in all fields of management. This course is designed to provide students with a working knowledge of the basic concepts underlying the most important multivariate techniques, an overview of actual applications in various fields and experience in actually using such techniques on a problem of their own choosing. The course addresses both the underlying mathematics and problems of applications. As such, a reasonable level of competence in both statistics and mathematics is needed to take this course.

• TR 6 p.m.-7:30 p.m.
• Instructor: Kamel Jedidi
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• 3 points

(PhD) Foundations of Optimizat

The course is designed for entering doctoral students and provides a rigorous introduction to the fundamental theory of optimization. It examines optimization theory in continuous, deterministic settings, including optimization in Euclidean as well as in more general, infinite-dimensional vector spaces. The course emphasizes unifying themes (such as optimality conditions, Lagrange multipliers, convexity, duality) that are common to all of these areas of mathematical optimization. Applications across a range of problem areas serve to illustrate and motivate the theory that is developed. Additionally, review sessions explaining how to solve complex optimization problems using CVX and Python are offered.

• R 2:20 p.m.-5:35 p.m.
• Instructor: Santiago R Balseiro
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• 3 points

(PhD) Dynamic Programming

This course offers an advanced introduction Markov Decision Processes (MDPs)-a formalization of the problem of optimal sequential decision making under uncertainty-and Reinforcement Learning (RL)-a paradigm for learning from data to make near optimal sequential decisions. The first part of the course will cover foundational material on MDPs. We'll then look at the problem of estimating long run value from data, including popular RL algorithms like temporal difference learning and Q-learning. The final part of the course looks at the design and analysis of efficient exploration algorithms, i.e. those that intelligently probe the environment to collect data that improves decision quality. This a doctoral level course. Students should have experience with mathematical proofs, coding for numerical computation, and the basics of statistics, optimization, and stochastic processes.
 

• R 9 a.m.-12:15 p.m.
• Instructor: Daniel J Russo
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• 3 points

COMPUTING FOR BUSINESS RESRCH

• W 2:20 p.m.-5:35 p.m.
• Instructor: Kriste Krstovski
  h-index: 9 Info
• 3 points