Managerial Statistics
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Instructor:
Cyrus Mohebbi
Info -
Instructor:
Faculty
h-index: 37 Info - 3 points
OPERATIONS MANAGEMENT
- TR 2:20 p.m.-3:50 p.m.
- TR 4:10 p.m.-5:40 p.m.
- TR 2:20 p.m.-3:50 p.m.
- TR 4:10 p.m.-5:40 p.m.
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Instructor:
Marcelo Olivares
Info - 1.5 points
Business Analytics II
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.
- MTWRF 9 a.m.-5 p.m.
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Instructor:
Charles R Guetta
Info - 3 points
Applied Regression Analysis
This course is designed for students who wish to increase their capability to build, use, and interpret statistical models for business.
A primary goal of the course is to enable students to build and evaluate statistical models for managerial use in finance, operations and marketing. The focus is on generating managerially useful information and practical decision-making tools, rather than on statistical theory per se. A number of actual business cases are studied.
Concepts covered are multiple linear regression models and the computer-assisted methods for building them, including stepwise regression and all subsets regression. Emphasis is placed on diagnostic and graphical methods for testing the validity and reliability of regression models.
Course topics include a review of basic statistical ideas, numerical and graphical methods for summarizing data, simple linear and nonlinear regression, multiple regression, qualitative independent and dependent variables, diagnostic methods for assessing the validity of statistical models. The course studies applications of regression to business forecasting and also examines alternative times series forecasting models, including exponential smoothing.
While the primary focus of the course is on regression models, some other statistical models will be studied as well, including cluster analysis, discriminant analysis, analysis of variance, and goodness-of-fit tests.
Term project: A major aspect of course is the opportunity to carry out a practical statistical analysis project of one’s own. Students work in teams on a problem of their own choosing. The goal of the project is to develop a useful statistical model for a specific business problem, with the professor providing ongoing guidance and advice during the course of project. The teams will give an oral presentation of their results at the term’s end.
Excel is used for basic statistical analysis as well as for developing straightforward regression models. In addition, more advanced commercial statistical software, such as Minitab or SAS, is used to carry out more complex and advanced analyses. In addition to the term project, there will be several computer-based assignments.
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Instructor:
David Juran
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, we’ll 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:
Kyle Owens
Info - 1.5 points
Business Analytics II
Business Analytics II (FT
This course extends and deepens the material you learned in business analytics. We will apply these methods in more unstructured and diverse situations, introduce new analytics tools and methods (including Tableau Visualization, text mining, and random forests), and study a modern framework for overfitting reduction called regularization that underlies much of modern machine learning. This course does not require coding or knowledge beyond Business Analytics, but the mathematical sophistication level will be somewhat more advanced.
- MTWRU 9 a.m.-5 p.m.
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Instructor:
Charles R Guetta
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."
- MTWRF 9 a.m.-5 p.m.
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Instructor:
Medini Singh
Info - 3 points
Supply Chain Management
Revenue and Supply Chain
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.
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.
- MTWRF 9 a.m.-5 p.m.
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Instructor:
Mark N Broadie
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.
- T 6 p.m.-9:15 p.m.
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Instructor:
Sunny Israni
Info - 1.5 points
The U.S. Healthcare Industry
The U.S. healthcare system is an enormously complex, trillion-dollar industry. It includes thousands of hospitals, nursing homes, specialized care facilities, independent practices and partnerships, web-based and IT supported service companies, managed care organizations, and major manufacturing corporations. Healthcare is the fastest growing component of many consulting practices and investment portfolios. In dollar terms, it accounts for over 18% of GDP and is larger than the total economy of Italy. It continues to grow in size and complexity, complicating the long-standing problems of increasing costs, limited consumer access, and inconsistent quality. And, the historic Affordable Care Act has resulted in significant changes throughout the entire industry and will have major implications for years to come. This tremendous dynamism is unmatched by any other industry and offers incredible opportunities for new business endeavors."
- MTWRF 2 p.m.-6 p.m.
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Instructor:
Carri Chan
h-index: 17 Info - 1.5 points