printer friendly pageOIT 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
Instructors:
Hu, Y. (PI)
;
Saban, D. (PI)
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
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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 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
Instructors:
Karaduman, O. (PI)
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
Instructors:
Weintraub, G. (PI)
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
OIT 276: Data and Decisions - Accelerated (Flipped Classroom)
Accelerated 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 more 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 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. The accelerated course is designed for students with strong quantitative backgrounds. Students taking this course need to be comfortable with mathematical notation, algebra, and basic probability. Students without quantitative backgrounds should consider enrolling in the base version of the course.
Terms: Win
| Units: 3
Instructors:
Wager, S. (PI)
OIT 280: Operations, Innovation, and Technology I
This course is the first part of a new two quarter course series (
OIT 280 &
OIT 281) that offers students a holistic perspective on the rapidly evolving and integrated world of operations, technology and innovation.
OIT 280 covers fundamental concepts and tools for excellent operations and new content on how business models, operational processes, technology and innovation come together in the real world.
OIT 280 is more methodological, focusing on key operational processes and how they interact with business models and innovation processes.
OIT 281 is more hands on, focusing on innovation processes. In
OIT 281, students study and practice the creation of new business and operating models and engage in an innovation project. In
OIT280, students focus on learning the key analytical tools and prepare a proposal for their innovation project. The course is under construction. Don't take it if you cannot tolerate sharp turns.
Terms: Win
| Units: 3
OIT 281: Operations, Innovation, and Technology II
This course is the second part of the two-quarter course series (
OIT 280 &
OIT 281) and expands on the learnings developed in Part I:
OIT 280. Students will learn how to structure business models and innovation processes and will apply the frameworks in a team project. A team project on an innovation challenge selected by students will provide a real world experience applying these frameworks. We encourage diverse innovation challenges that could lead to one of the following: a concept for a new venture, a critical evaluation of an existing business model with a recommendation for a change, a critical evaluation of operational processes for an existing organization with recommendations for changes. Students will develop a project proposal as part of OIT280 and they will launch and implement the project in
OIT281. In addition, students will examine through a series of case studies how organizations develop operating models that implement innovative business models and integrate operations, innovation and technology. Key Topics: business model analysis and design, design thinking, lean startup, precedent-based innovation, technology readiness level assessment, AI and 3D printing, value chain innovation, innovation process applications.
Terms: Spr
| Units: 3
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