Business Analytics
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Instructor:
Charles R Guetta
Info -
Instructor:
Thomas Vladeck
Info - 1.5 points
Managerial Statistics
- TR 2:20 p.m.-3:50 p.m.
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Instructor:
Paul Glasserman
Info -
Instructor:
Cyrus Mohebbi
Info -
Instructor:
Paul Glasserman
Info - 1.5 points
Business Analytics
- TR 2:20 p.m.-3:50 p.m.
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Instructor:
Tianyi Peng
Info -
Instructor:
Malek A Ben Sliman
Info -
Instructor:
Tianyi Peng
Info - 1.5 points
OPERATIONS MANAGEMENT
- MW 10:50 a.m.-12:20 p.m.
- MW 2:20 p.m.-3:50 p.m.
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Instructor:
Hannah Q Li
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
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
- T 6 p.m.-9:15 p.m.
- 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.
- 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.
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Instructor:
Hongseok Namkoong
h-index: 11 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."
- 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 9 a.m.-12:15 p.m.
- R 2:20 p.m.-5:35 p.m.
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Instructor:
Medini Singh
Info - 3 points
Game-Theoretic Business Strategy
GAME-THEORETIC BUSINESS S
This course is an introductory business-strategy course designed for analytically-oriented graduate students, particularly students in the joint Business School-IEOR programs. The course has three objectives.:
1 - Provide you with the economic theory to understand why a given company is (or is not) profitable. (For potential entrepreneurs, this theory becomes a tool to assess whether your proposed venture will be profitable in a competitive environment.)
2 - Provide you with perspectives for assessing the sustainability of a given company’s profitability. We will place special emphasis on understanding and evaluating the key assumptions and judgments underpinning your assessments. The course includes historical cases of managing a changing business environment.
3 - Enable you to identify the substantive issues behind the trends and frameworks in the strategy field.
- R 9 a.m.-12:15 p.m.
- R 2:20 p.m.-5:35 p.m.
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Instructor:
Harborne Stuart
h-index: 10 Info - 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.
- T 9 a.m.-12:15 p.m.
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Instructor:
David Juran
Info - 1.5 points
Leading Sustainable Supply Chains and Op
Leading Sust Supply Chain
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.
- M 9 a.m.-12:15 p.m.
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Instructor:
Nicole Dehoratius
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.
- T 9 a.m.-12:15 p.m.
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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
- TWRFS 9 a.m.-5 p.m.
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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. "
- M 2:20 p.m.-5:35 p.m.
- 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 6 p.m.-7:30 p.m.
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Instructor:
Mattan Griffel
Info - 1.5 points
Data Analytics in Python
The collection, interpretation, and analysis of data has always been a central pillar of business decision making. Historically, this has followed a two step process, statisticians gather data, organize it, run analytics and prepare reports. At some future point, a decision maker examines these reports, interprets the results and makes decisions. However, with the advent of powerful and inexpensive computing platforms, the collection and analysis of data has moved into the continuous decision making cycle itself, with decisions being constantly updated as new data is instantly analyzed and acted upon. Consequently, decision makers can no longer isolate themselves from the grungy side of data and they need to know where the data originated, how it was transformed, what is the nature, the strengths and the limitations of the analytical techniques used. Today, to be effective, decision makers need an intuitive understanding of the statistics, the math, and the programming that underlie this “live” analytical and decision making process.
The objective of this course is to give you an understanding of the analytical side of the decision making cycle, focusing on programming as the element that “glues” the collection, transformation, visualization, and analysis of data. We will see how to get data from common sources (APIs, web scraping), examine the rudiments of data visualization (charts, maps), and get an intuitive understanding of the types of analytical tools in use today (machine learning, deep learning, analysis of networks, analyzing natural language texts).
With its extensive collection of libraries, Python is fast becoming the platform of choice for data analytics so Python will be our language for this course. The course is very hands on, and you should expect a lot of programming work, all of it fairly intense. A basic understanding of how to write programs in Python is therefore a must for this class. But, the primary takeaway from the course is not the programming but rather an understanding of the mechanics, the vocabulary, and the techniques in data analytics. Even if you find programming a frustrating and head banging exercise, you can get a lot out of the class (if you’re willing to suffer a bit!).
STUDENTS WILL NEED TO EITHER HAVE PASSED B8154 (PYTHON FOR MBAS) OR THE ADVANCED PYTHON QUALIFICATION EXAM (https://www8.gsb.columbia.edu/courses/python#advanced_qual) BEFORE THE FIRST DAY OF CLASS
- W 2:20 p.m.-5:35 p.m.
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Instructor:
Hardeep Johar
Info - 3 points
Foundations of AI for Business
- M 9 a.m.-12:15 p.m.
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Instructor:
Hongseok Namkoong
h-index: 11 Info - 1.5 points
Applying Healthcare IT and Digital Healt
Apply Healthcare IT & Dig
- W 2:20 p.m.-5:35 p.m.
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Instructor:
Raymond G Falci
Info - 1.5 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
- W 9 a.m.-12:15 p.m.
- W 2:20 p.m.-5:35 p.m.
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Instructor:
Charles R Guetta
Info - 1.5 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.
- T 6 p.m.-9:15 p.m.
- R 6 p.m.-9:15 p.m.
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Instructor:
David L Bendes
Info -
Instructor:
Mattan Griffel
Info - 1.5 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.
- R 2:20 p.m.-5:35 p.m.
- T 9 a.m.-12:15 p.m.
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Instructor:
Yashodhan Kanoria
h-index: 18 Info - 1.5 points
(PhD) Foundations of Stochasti
This course covers basic concepts and methods in applied probability and stochastic modeling. The intended audience is master's and doctoral students in programs such as EE, CS, IEOR, Statistics, Mathematics, and those in the DRO division in the Business School. In terms of prerequisites, basic familiarity with probability theory and stochastic processes will be assumed (an ideal preliminary course is IEOR 6711: Stochastic Modeling I, but a more basic substitute will do as well). The topics and material covered in this course complement those covered in IEOR 6712: Stochastic Modeling II, hence the two courses can be taken simultaneously. The exposition will be (mostly) rigorous, yet intentionally skirting some measure-theoretic details; for those interested in such details they can be found in measure theoretic textbooks and other courses (e.g., Probability Theory I/II given in the statistics/math department).
- M 9 a.m.-12:15 p.m.
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Instructor:
Assaf Zeevi
h-index: 40 Info - 3 points
Analysis of Algorithms in Operations Res
Analytics and e-commerce have drastically increased the sophistication both in how goods are sold to customers, and how these goods are fulfilled. Examples of the former include dynamic pricing, recommending product assortments, and personalized coupons, and are studied in the area of Revenue Management; examples of the latter include flexible products and dynamic warehouse selection, and are studied in the area of Supply Chain Management. This research-oriented course will review recent developments in both of these areas and discuss open directions; there will also be a slant toward learning how to apply the techniques of linear and integer programming, analysis of online algorithms, and mechanism design. This course is meant for Ph.D. students in operations research, industrial engineering, computer science, or related departments who are familiar with optimization and probability at the introductory graduate level.
(DRO) Topics Seminar
The purpose of the course is to broadly cover topics in Operations Management and Operations Research, as well as areas of interest to the Decision, Risk, and Operations division at CBS. It will consist of 3-hour sessions, each on a different topic, with the intent to introduce you to the topic, pique your interest, expose you to the methodologies and research areas people are working on, and help you think about what types of courses to take in the future in order to prepare yourself for this course. We will ask students to submit a one-page summary of each session or a particular paper discussed in the session. PhD students are invited to take the course and/or select certain sessions to attend. Spring 2022 Faculty instructors (each teaching one session) will be Mark Broadie, Paul Glasserman, Fanyin Zheng, Jing Dong, Will Ma, Hongyao Ma.
- R 2:20 p.m.-5:35 p.m.
- 1.5 points
Reliable Statistical Learning
- R 9 a.m.-12:15 p.m.
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Instructor:
Hongseok Namkoong
h-index: 11 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
- 0.5 points