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
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:
Bimpikis, 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 slightly expanded set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The class is taught in an interactive style, focusing on applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. There are several differences with 245 and 247 that are worth emphasizing. The first difference is in the pace and depth:
OIT 248 covers each topic significantly faster and at a deeper level; as such, the class requires slightly more mathematical sophistication (for instance, the ability to quickly digest mathematical formulas and equations). A second difference is that
OIT 248 leverages Pyt
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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 slightly expanded set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The class is taught in an interactive style, focusing on applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. There are several differences with 245 and 247 that are worth emphasizing. The first difference is in the pace and depth:
OIT 248 covers each topic significantly faster and at a deeper level; as such, the class requires slightly more mathematical sophistication (for instance, the ability to quickly digest mathematical formulas and equations). A second difference is that
OIT 248 leverages Python instead of Excel for implementation. (We emphasize that
OIT 248 uses Python to teach analytics, but is not a course on Python or coding. Although no prior coding experience in Python is required, every student in Advanced is expected to have had some prior coding experience, for instance, through coursework, in their prior jobs, etc.) Lastly, a third difference is that
OIT 248 devotes more time to discussing practical issues that arise in real-world, data-driven implementations; so by the end of the course, students should develop a more 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.
Terms: Aut
| Units: 3
Instructors:
Iancu, D. (PI)
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