Managerial Statistics
- T 8:30 a.m.-11:30 a.m.
-
Instructor:
Assaf Zeevi
h-index: 40 Info - 3 points
Managerial Statistics
- MW 9 a.m.-10:30 a.m.
- MW 2 p.m.-3:30 p.m.
-
Instructor:
Boaz Galinson
Info - 1.5 points
Business Analytics
- TR 2:20 p.m.-3:50 p.m.
- TR 9 a.m.-10:30 a.m.
- TR 4:10 p.m.-5:40 p.m.
- TR 10:50 a.m.-12:20 p.m.
-
Instructor:
Ciamac Moallemi
h-index: 24 Info -
Instructor:
Omar Besbes
h-index: 22 Info - 1.5 points
Statistics & Business Analytics
Stats & Business Analytic
- R 2:20 p.m.-5:35 p.m.
-
Instructor:
Charles R Guetta
Info - 3 points
Business Analytics II: Foundations of AI
Business Analytics 2
- S 8:30 a.m.-11:30 a.m.
-
Instructor:
Ashwin V Kamath
Info - 1.5 points
Service Operations Management
- F 12:30 p.m.-3:30 p.m.
-
Instructor:
Medini Singh
Info - 3 points
Business Analytics III: Modern AI, Deep
- S 3:45 p.m.-6:45 p.m.
-
Instructor:
Ashwin V Kamath
Info - 1.5 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 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.
- R 9 a.m.-noon
-
Instructor:
Charles R Guetta
Info - 3 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.
- W 2:20 p.m.-5:35 p.m.
- W 6 p.m.-9:15 p.m.
-
Instructor:
Charles R Guetta
Info - 1.5 points
Operations Strategy
- W 9 a.m.-12:15 p.m.
- W 2:20 p.m.-5:35 p.m.
-
Instructor:
Medini Singh
Info - 3 points
Service Operations Management
- T 9 a.m.-12:15 p.m.
- T 2:20 p.m.-5:35 p.m.
- R 9 a.m.-12:15 p.m.
-
Instructor:
Medini Singh
Info - 3 points
Supply Chain Management
- M 9 a.m.-12:15 p.m.
- M 2:20 p.m.-5:35 p.m.
-
Instructor:
Nicole Dehoratius
Info - 1.5 points
Applied Regression Analysis
- MW 10:50 a.m.-12:20 p.m.
-
Instructor:
Paul Glasserman
Info - 1.5 points
Leading Sustainable Operations
- M 6 p.m.-9:15 p.m.
-
Instructor:
Nicole Dehoratius
Info - 1.5 points
Business Analytics III: Modern AI, Deep
Business Analytics III: A
- W 2:20 p.m.-5:35 p.m.
- W 6 p.m.-9:15 p.m.
-
Instructor:
Charles R Guetta
Info - 1.5 points
Transforming the Delivery of H
- TR 10:50 a.m.-12:20 p.m.
-
Instructor:
Brian Regan
Info - 1.5 points
DIGITAL LITERACY-DECISION MKRS
- W 9 a.m.-12:15 p.m.
-
Instructor:
Marco A Morales Barba
Info - 1.5 points
Digital Health Startups
- MTWRF 9 a.m.-1 p.m.
-
Instructor:
Halle Tecco
Wikipedia Info - 1.5 points
Intro to Databases for Busines
- W 2:20 p.m.-3:50 p.m.
-
Instructor:
Mattan Griffel
Info - 1.5 points
Intro to Databases for Busines
- W 2:20 p.m.-3:50 p.m.
-
Instructor:
Mattan Griffel
Info - 1.5 points
Strategic Negotiations
- R 2:20 p.m.-5:35 p.m.
-
Instructor:
Harborne Stuart
h-index: 10 Info - 3 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,
- TR 10:50 a.m.-12:20 p.m.
-
Instructor:
Miguel Morin
Info - 3 points
The U.S. Healthcare Industry
- TR 10:50 a.m.-12:20 p.m.
-
Instructor:
Carri Chan
h-index: 17 Info - 1.5 points
(PhD) Foundations of Optimizat
- R 2:20 p.m.-5:35 p.m.
-
Instructor:
Santiago R Balseiro
Info - 3 points
Introduction to Econometrics and Statist
Intro to Econ and Stat In
- R 6 p.m.-9 p.m.
-
Instructor:
Loujaina Abdelwahed
Info - 3 points
Econometrics and Statistical Inference I
Econ and Stat Infer II
- W 6 p.m.-9 p.m.
-
Instructor:
Nicola Rosaia
Info - 3 points
Computing for Finance
This course is a medium-level introduction to Python and its applications in finance, especially "backtesting." 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.
When a finance practitioner discovers a new investment strategy (or "alpha signal"), they backtest it: they look at historical data, buy and sell at the posted prices according to the strategy, and then compute the portfolio's return. Back-testing is an essential procedure for both industry (traders back-test a strategy before deployment) and academia (research papers in finance that propose a new profitability factor usually contain a back-test and a measure of profitability).
Then introduce doc tests, unit tests, and object-oriented programming. Along the way, students will also develop a solid mastery of Python, which may be useful for interviewing for summer internships.
In the second half, we will use this backtesting framework to measure the profitability of several strategies that use data science techniques, for example:
- Bollinger bands (using linear regression);
- valuation using multiples (using k-nearest neighbors);
- buy-hold-sell recommendations (using logistic regression and neural networks).
(If ethically allowed:) The instructor will apply each of these strategies in a live portfolio so we can see its performance in the real world week over week.
- TR 9 a.m.-10:30 a.m.
-
Instructor:
Miguel Morin
Info - 3 points
Algorithms for Social Systems: Beyond i.
Algorithms and AI systems are increasingly deployed at scale in consequential social settings, e.g., online platforms, labor markets, healthcare systems, and educational institutions. The standard methods used to design and evaluate these algorithms were developed under assumptions of independent and identically distributed (i.i.d.) data, assumptions that are systematically violated when humans interact with each other and respond to the systems around them. Researchers working in these settings need to understand when these assumptions break down, what the resulting effects on system performance are, and how to design better systems.
This course examines the evaluation, deployment, and human response to algorithms in social systems where these i.i.d. assumptions fail. We study how interference between users, capacity constraints, and behavioral responses to algorithms undermine both classical machine learning methods and causal inference tools like A/B testing and RCTs — and for each failure mode, we examine recent methodological advances designed for these settings. Topics include experiment design under interference, recommendation systems and their downstream effects, human compliance and discretion in algorithmic decision-making, and the operational realities of deploying algorithms at scale. The course closes by applying this analytical lens to large language models, examining whether the same failure modes arise, and whether the same advances apply, as AI interventions scale across social systems. The class will draw on recent theory and methodological work in causal inference, operations research, and human-algorithm interaction to prepare students for research at this frontier.
This course is targeted at mathematically mature PhD students in operations research, statistics, computer science, economics, business, or quantitative fields interested in the intersection of algorithms and human behavior.
- T 2:20 p.m.-5:35 p.m.
-
Instructor:
Hannah Q Li
Info - 3 points