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1 - 10 of 42 results for: OIT

OIT 245: Optimization and Simulation Modeling

This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, supply chain management, revenue and yield optimization.
Terms: Aut | Units: 3

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.
Terms: Aut | Units: 3
Instructors: Xu, K. (PI)

OIT 248: Optimization And Simulation Modeling - Advanced

This course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). It is an advanced version of OIT 245 and OIT 247 and it covers a similar (but slightly expanded) set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The main differences are in the pace and depth, with OIT 248 covering each topic significantly faster and at a deeper level. Additionally, OIT 248 leverages Python instead of Excel for implementation and devotes more time to discussing practical issues that arise in real-world, data-driven implementations. By the end of the course, students should develop an in-depth mental framework of the topics and leave with a good understanding of how they fit within modern machine-learning / AI pipelines that aid decision-making in complex problems. The class is taught in an interactive st more »
This course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). It is an advanced version of OIT 245 and OIT 247 and it covers a similar (but slightly expanded) set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The main differences are in the pace and depth, with OIT 248 covering each topic significantly faster and at a deeper level. Additionally, OIT 248 leverages Python instead of Excel for implementation and devotes more time to discussing practical issues that arise in real-world, data-driven implementations. By the end of the course, students should develop an in-depth mental framework of the topics and leave with a good understanding of how they fit within modern machine-learning / AI pipelines that aid decision-making in complex problems. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. We emphasize that OIT 248 uses Python to teach analytics, but is not a course on Python or coding per se. Some prior coding experience is helpful but is not a strict requirement for the course.
Terms: Aut | Units: 3
Instructors: Iancu, D. (PI)

OIT 249: MSx: Data and Decisions

Data and Decisions teaches you how to use data and quantitative reasoning to make sound decisions in complex and uncertain environments. The course draws on probability, statistics, and decision theory. Probabilities provide a foundation for understanding uncertainties, such as the risks faced by investors, insurers, and capacity planners. We will discuss the mechanics of probability (manipulating some probabilities to get others) and how to use probabilities to make decisions about uncertain events. Statistics allows managers to use small amounts of information to answer big questions. For example, statistics can help predict whether a new product will succeed or what revenue will be next quarter. The third topic, decision analysis, uses probability and statistics to plan actions, such as whether to test a new drug, buy an option, or explore for oil. In addition to improving your quantitative reasoning skills, this class seeks to prepare you for later classes that draw on this material, including finance, economics, marketing, and operations. At the end we will discuss how this material relates to machine learning and artificial intelligence.
Terms: Aut | Units: 3
Instructors: Somaini, P. (PI)

OIT 256: Digital Business

This course focuses on the way information technology affects the structure of business models. It considers the impact of information technology on industries ranging from retail to transportation and finance. It considers how you can take advantage of new technology opportunities and how they change the structure of firms, industries and value chains, with an emphasis on business issues. Classes combine lecture and case study discussions and the workload is above the GSB average. The course is designed to help you make a transition into technology-related fields.
Last offered: Spring 2023

OIT 262: Operations

Operations is the design and management of processes for production and delivery of services or goods. This course covers fundamental concepts and tools for excellent operations: Process Analysis - analysis, improvement, and design of operational processes; aligning operational processes with your business model. Managing variability and uncertainty in demand and supply. Climate Change - challenge and solutions. Quality Management - quantitative tools to measure and manage quality; best practices in quality management and innovation processes. Value Chain - managing flows of material and information through global value chains. The course has heavy analytic and quantitative work. No prior knowledge of operations is expected.
Terms: Spr | Units: 3

OIT 269: MSx: Operations and Strategies

Operations refer to the processes through which businesses produce and deliver products or services. Managing operations well is necessary in order for these processes to be completed in a timely manner, consume minimal resources and costs, and achieve their goal effectively. This course focuses on managerial issues arising in the operations of manufacturing and service industries. The objectives of the course are to introduce operational problems and challenges faced by managers, as well as language, conceptual models, analytical techniques and strategies that are broadly applicable in confronting such problems.
Last offered: Summer 2023

OIT 271: Operations and Strategies

Operations refer to the processes through which businesses produce and deliver products or services. The objectives of the course are to introduce operational problems and challenges faced by managers, and effective methods for confronting such problems. The first half of the course focuses on acquiring language, analytical frameworks and techniques, and conceptual models that are broadly applicable in confronting operational problems in a variety of domains. The second half of the course focuses on how different operations decisions should be interconnected under the umbrella of an operations strategy that supports the business strategy of the firm, and demonstrates this through a segment on the design of global supply chains. With the first part of the course being compressed with quantitative analysis, it is designed for students with a quantitative background (for example, in engineering) that is not necessarily related to Operations.
Last offered: Winter 2023

OIT 272: Online Marketplaces

The course studies one of the most impactful business models in recent decades. We will study what makes an online marketplace successful, from network effects to reducing search and matching frictions, fostering trust, and effective ways to monetize. Students will explore both strategic decisions and the inner operations of these platforms, getting hands-on with the analytical and data science tools that power them. We will look at well-known models like those of Amazon, Google, Uber, and Airbnb, while also touching on the latest trends in the space. A particular emphasis will be on how AI is reshaping the way online marketplaces interact with users and the broader changes it might bring. Overall, the course will provide basic business knowledge for future investors, entrepreneurs, product managers, and anyone interested on online marketplaces.
Terms: Spr | Units: 2

OIT 274: Data and Decisions - Base (Flipped Classroom)

Base Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a lab-based classroom approach. Traditional lecture content will be learned through online videos, simulations, and exercises, while time spent in the classroom will be discussions, problem solving, or computer lab sessions. Content covered includes basic probability, sampling techniques, hypothesis testing, t-tests, linear regression, and prediction models. The group regression project is a key component of the course, and all students will learn the statistical software package R.
Terms: Win | Units: 3
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