Managerial Statistics
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Instructor:
Boaz Galinson
Info -
Instructor:
Daniel J Russo
Info -
Instructor:
Boaz Galinson
Info -
Instructor:
Mark Rodgers
Info - 1.5 points
Managerial Statistics
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Instructor:
Mark Rodgers
Info -
Instructor:
Daniel J Russo
Info -
Instructor:
Boaz Galinson
Info -
Instructor:
Mark Rodgers
Info -
Instructor:
Daniel J Russo
Info - 1.5 points
Business Analytics
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Instructor:
Ciamac Moallemi
h-index: 24 Info -
Instructor:
Omar Besbes
h-index: 22 Info -
Instructor:
Ciamac Moallemi
h-index: 24 Info -
Instructor:
Omar Besbes
h-index: 22 Info -
Instructor:
Ciamac Moallemi
h-index: 24 Info -
Instructor:
Omar Besbes
h-index: 22 Info - 1.5 points
Business Analytics
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Instructor:
Omar Besbes
h-index: 22 Info -
Instructor:
Vineel Chakradhar
Info - 1.5 points
Statistics & Business Analytics
Stats & Business Analytic
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Instructor:
Charles R Guetta
Info - 3 points
Business Analytics II: Foundations of AI
Business Analytics 2
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.
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Instructor:
Ashwin V Kamath
Info - 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.
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Instructor:
Medini Singh
Info - 3 points
Business Analytics III: Modern AI, Deep
Business analytics refers to the methods enterprises—such as businesses, non-profits, and governments—use to analyze data to gain insights and make better decisions. This discipline is applied across various functions including operations, marketing, finance, and strategic planning. The advent of modern data collection methods in fields like bioinformatics, mobile platforms, and previously unanalyzable data (such as text and images) has led to an explosive growth in the volume of data available for decision-making. Utilizing data effectively to drive rapid, precise, and profitable decisions has become a critical strategic advantage for diverse companies including Walmart, Google, Capital One, and Disney. Moreover, many startups are emerging based on the application of AI and analytics to large databases. With the increasing availability of broad and deep sources of information—often referred to as "Big Data"—business analytics is becoming an even more essential capability for enterprises of all types and sizes.
AI is starting to influence every dimension of business and society. In many industries, being literate in AI is becoming a prerequisite for successful business leadership. The Business Analytics sequence is designed to prepare you to take an active role in shaping the future of AI and business. You will develop a critical understanding of modern analytics methodologies, exploring their foundations, potential applications, and—perhaps most importantly—their limitations.
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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
- 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.
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Instructor:
Charles R Guetta
Info - 3 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.
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Instructor:
Charles R Guetta
Info - 1.5 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.
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Instructor:
Charles R Guetta
Info - 1.5 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."
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Instructor:
Medini Singh
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."
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Instructor:
Medini Singh
Info - 3 points
Service Operations Management
This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.
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Instructor:
Medini Singh
Info - 3 points
Service Operations Management
This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.
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Instructor:
Medini Singh
Info - 3 points
Service Operations Management
This course examines both traditional and new approaches for achieving operational competitiveness in service businesses. Major service sectors such as health care, repair / technical support services, banking and financial services, transportation, restaurants, hotels and resorts are examined. The course addresses strategic analysis and operational decision making, with emphasis on the latter. Its content also reflects results of a joint research project with the consulting firm Booz Allen Hamilton, which was initiated in 1996 to investigate next-generation service operations strategy and practices. Topics include the service concept and operations strategy, the design of effective service delivery systems, productivity and quality management, response time (queueing) analysis, capacity planning, yield management and the impact of information technology. This seminar is intended for students interested in consulting, entrepreneurship, venture capital or general management careers that will involve significant analysis of a service firms operations.
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Instructor:
Medini Singh
Info - 3 points
Supply Chain Management
Supply chain management entails managing the flow of goods and information through a production or distribution network to ensure that the right goods are delivered to the right place in the right quantity at the right time. Two primary objectives are to gain competitive edge via superior customer service and to reduce costs through efficient procurement, production and delivery systems. Supply chain management encompasses a wide range of activities — from strategic activities, such as capacity expansion or consolidation, make/buy decisions and initiation of supplier contracts, to tactical activities, such as production, procurement and logistics planning, to, finally, operational activities, such as operations scheduling and release decisions, batch sizing and issuing of purchase orders.
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Instructor:
Nicole Dehoratius
Info - 1.5 points
Supply Chain Management
Supply chain management entails managing the flow of goods and information through a production or distribution network to ensure that the right goods are delivered to the right place in the right quantity at the right time. Two primary objectives are to gain competitive edge via superior customer service and to reduce costs through efficient procurement, production and delivery systems. Supply chain management encompasses a wide range of activities — from strategic activities, such as capacity expansion or consolidation, make/buy decisions and initiation of supplier contracts, to tactical activities, such as production, procurement and logistics planning, to, finally, operational activities, such as operations scheduling and release decisions, batch sizing and issuing of purchase orders.
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Instructor:
Nicole Dehoratius
Info - 1.5 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.
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Instructor:
Paul Glasserman
Info - 1.5 points
Leading 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.
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Instructor:
Nicole Dehoratius
Info - 1.5 points
Business Analytics III: Modern AI, Deep
Business Analytics III: A
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.
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Instructor:
Charles R Guetta
Info - 1.5 points
Business Analytics III: Modern AI, Deep
Business Analytics III: A
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.
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Instructor:
Charles R Guetta
Info - 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."
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Instructor:
Brian Regan
Info - 1.5 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.
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Instructor:
Marco A Morales Barba
Info - 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.
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Instructor:
Mark N Broadie
Info - 3 points
Digital Health Startups
Healthcare represents 18% of the U.S. economy, yet it is one of the last sectors to undergo technology-based transformation. Digital health represents the convergence of healthcare and technology, with the aim to improve access to care, reduce inefficiencies in healthcare delivery, lower costs, enhance the quality of patient care, make treatments more targeted and personalized, and empower consumers to better manage their own health and well-being.
In recent years there has been an explosion of new digital health startups focused on these key objectives. Digital health has become the bellwether of venture funding, outgrowing both traditional healthcare and technology sectors. Venture funding in this category exceeded $29 billion in 2021, double the previous year and a 2,325% increase from 2011.
This course will analyze the unique characteristics and strategies of digital health companies as students form groups to act as venture capitalists and develop investment memos for real companies that are pitched by their founders. Past companies that have been pitched in this course— Maven Clinic, Grand Rounds (Included Health), and Simple Health— have gone on to become high-growth, billion-dollar companies.
Students will analyze key objectives of new businesses and determinants of success including unit economics, product differentiation, go-to market strategies, customer acquisition, marketing tactics, scale-up/growth opportunities, and other business optimization approaches. The course will allow students to hone their investment skills including questions to ask during an entrepreneur’s pitch, developing an investment thesis, and how to structure and write an investment memo. This course will address these issues through a mixture of lectures, case studies, and guest speakers (entrepreneurs and investors) from the digital health sector.
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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.
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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.
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Instructor:
Mattan Griffel
Info - 1.5 points
Strategic Negotiations
Our days are filled with negotiation and conflict, from everyday disputes to job negotiations, from coalition-building to boardroom bargaining. This course aims to help students improve their skills in two fundamental ways. One is knowledge-oriented: students learn concepts and frameworks for analyzing and preparing for bargaining. A second route is practice-oriented: students engage in a sequence of hands-on activities, practicing and reflecting, building self-awareness, and honing their skills for creating and claiming value.
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Instructor:
Harborne Stuart
h-index: 10 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.
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Instructor:
Sunny Israni
Info - 1.5 points
Vibe Coding
Vibe Coding for Business may be one of the most transformative courses you take in your career. Artificial intelligence is no longer a speculative technology – it is a permanent shift in how organizations operate and create value. Productivity gains from AI are already substantial for many organizations, and are projected to accelerate significantly over the next decade.
A new approach known as vibe coding – enabled by new AI-powdered coding tools such as Cursor, Bolt, and Lovable – has led to a 2-3x increase in the effectiveness of many engineering and product teams. More importantly, vibe coding has opened the door for non-engineers to build working software with little to no coding backgrounds. Suddenly, what used to take a full team and weeks of effort can now be accomplished by a single person and an AI assistant in a matter of hours, drastically reducing the cost and time for product development and experimentation.
In this class, students will learn how to design, build, and deploy lightweight software products using AI coding tools (e.g. Cursor, Lovable, Replit, Bolt, v0). You will practice structured workflows and learn best practices for debugging and iteration, reason through simple systems architectures, evaluate tools critically, and deploy real working applications with speed, reliability, and risk in mind.
This course runs like a product studio: structured tool explorations, weekly build sprints, and a public demo day. It bridges the gap between strategy courses and technical electives (e.g. Python and SQL) by giving business leaders hands-on experience in AI-assisted product creation.
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Instructor:
Mattan Griffel
Info - 1.5 points
AI Solutions: From Diagnostics to Deploy
AI solutions are being deployed for an increasing number of problems across industries. At the same time, the ROI on AI deployment has been questioned in certain cases. What drives successful deployment? How does one identify when, and where and what type of AI solutions to deploy? These are exactly the type of questions that many MBA students will face in their careers, is it in the context of managerial roles within large enterprises, as entrepreneurs, or in Consulting, Private Equity, Venture Capital, etc…
This class aims at helping students sharpen their ``AI acumen’’, and will go through a series of core questions associated with AI deployment and a broad set of business scenarios associated with AI solutions. The first part of the class will focus on the diagnostic step and AI feasibility, while the second part of the course will focus on the deployment of AI solutions.
● AI Feasibility: the goal is to provide students with practical frameworks and intuition on how to assess efficiently if a business use case in their company is feasible and what steps they need to take to maximize the chance of success.
● AI Deployment : The goal of this part is to cover practical dimensions of deploying AI solutions, and the tradeoffs they induce. Among others, the course will touch on privacy, cost optimization, accuracy and reliability considerations.
- 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,
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Instructor:
Miguel Morin
Info - 3 points
Quantitative Pricing & Revenue
Quantitative pricing and revenue analytics collectively refers to the set of practices and tools that firms in various industries use to quantitatively model consumer preferences, segment their market, and tactically optimize (often in micro targeted or personalized manner) their product assortment, pricing, and promotion strategies. The origins of this field, often referred to as revenue management as it is also called, are in the airline industry during the late 80s. The prototypical question is how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximize its profitability. In the airline industry, as most of us know, tickets for the same flight may be sold at many different fares, the availability of which is changing as a function of purchase restrictions, the forecasted future demand, and the number of unsold seats. The adoption of such systems has transformed the transportation and hospitality industries, and is increasingly important in retail, telecommunications, entertainment, financial services, health care, manufacturing, as well as on-line advertising, online retailing, and online markets. In parallel, pricing and revenue optimization has become a rapidly expanding practice in consulting services, and a growing area of software and IT development. We will be doing a hands-on dive into the above tools in the context of 2-3 case studies and datasets, in conjunction with lectures to set the stage. The case studies will cover markdown pricing for a retailer, demand and inventory data for a self-storage company, customer research data of a mortgage lender, and peak load pricing data for a highway toll booth.Through this course, students will be able to model and identify opportunities for revenue optimization in different business contexts. As the ensuing outline reveals, most of the topics covered in the course are either directly or indirectly related to customer segmentation, demand modeling, and tactical price optimization.TextbookOne recommended book for the course is by Robert Phillips titled "Pricing and Revenue Optimization. This will primarily be done in teams, much of it in class, and with the help of the TA(s) and the professor. Sample code will be shared for various parts of these analyses. Course deliverables align. Apart from class participation (30% of the total grade), the other course deliverables consist of a set of in-class (homework) assignments (40%) and a take-home final exam (30%).Class participation: I wi
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."
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Instructor:
Carri Chan
h-index: 17 Info - 1.5 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.
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Instructor:
Santiago R Balseiro
Info - 3 points
DROM Preseminar Discussion
Students get together to discuss the paper which will be presented at the IEOR-DRO seminar. One group of students (~2 students) presents. A faculty member is present to guide and facilitate the discussion. Students are evaluated on their effort in leading one of the discussions and participating in the other discussions
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Instructor:
Daniel J Russo
Info - 0.5 points
Introduction to Econometrics and Statist
Intro to Econ and Stat In
Formerly known as FINCB9323.
This course serves as an introduction to econometrics and statistical inference at the graduate level. Topics will include asymptotic inference, linear regression, an introduction to causal inference, general model estimation techniques, and discrete choice models. The intent is to thoroughly understand basic econometric models and tools necessary for empirical research.
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Instructor:
Faculty
h-index: 37 Info - 3 points
Econometrics and Statistical Inference I
Econ and Stat Infer II
Formerly known as FINCB9324.
This course serves as an introduction to econometrics and statistical inference at the graduate level. Topics will include mathematical statistics, estimation methods for linear and non-linear models, statistical methods for making inferences, and an introduction to causal inference. The intent is to thoroughly understand basic econometric models and statistical tools necessary for empirical research.
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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.
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Instructor:
Miguel Morin
Info - 3 points