This course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). Topics covered include Monte-Carlo simulation, static optimization, data-driven optimization, and dynamic decision-making. Much like OIT 245/OIT 247, the class is taught in an interactive style, with in-class exercises focused on applications drawn from a variety of industry segments (including advertising, healthcare, finance, supply chain management, pricing, energy, scheduling, or risk management). Because this is an advanced version of OIT 245/OIT 247, several differences are also worth emphasizing. The first is in the breadth of topics: OIT 248 covers a slightly expanded set of topics compared to OIT 245/OIT 247, including a few classes on data-driven optimization and connections to modern machine learning and AI pipelines. The second is the depth and pace of the coverage: each topic in OIT 245/7 is covered at a faster pace and deeper level in OIT 248, and OIT 248 requires some versatility and willingness to absorb mathematical formulas and equations. A third difference is the coding environment: OIT 248 leverages Python instead of Excel for implementation, so some basic experience with coding is expected. No prior coding experience in Python is required, but every student in Advanced is expected to have had some prior formal coding experience in a programming language, e.g., C, Java, Matlab, etc. OIT 248 starts with an introduction to Python, but the express purpose of the class is not to teach Python, but rather to use Python; so the Python coverage will feel very fast-paced to anyone with zero coding background. The last difference is that OIT 248 devotes more time to discussing practical issues that arise in real-world business situations, so the course is a best fit for students interested in career paths involving AI, analytics, and data science.
3 units · GSB Letter Graded
This course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). Topics covered include Monte-Carlo simulation, static optimization, data-driven optimization, and dynamic decision-making. Much like OIT 245/247, the class is taught in an interactive style, with in-class exercises focused on applications drawn from a variety of industry segments (including advertising, healthcare, finance, supply chain management, pricing, energy, scheduling, or risk management). Because this is an advanced version of OIT 245/247, several differences are also worth emphasizing. The first is in the breadth of topics: OIT 248 covers a slightly expanded set of topics compared to OIT 245/247, including a few classes on data-driven optimization and connections to modern machine learning and AI pipelines. The second is the depth and pace of the coverage: each topic in OIT 245/7 is covered at a faster pace and deeper level in OIT 248, and OIT 248 requires some versatility and willingness to absorb mathematical formulas and equations. A third difference is the coding environment: OIT 248 leverages Python instead of Excel for implementation, so some basic experience with coding is expected. No prior coding experience in Python is required, but every student in Advanced is expected to have had some prior formal coding experience in a programming language, e.g., C, Java, Matlab, etc. OIT 248 starts with an introduction to Python, but the express purpose of the class is not to teach Python, but rather to use Python; so the Python coverage will feel very fast-paced to anyone with zero coding background. The last difference is that OIT 248 devotes more time to discussing practical issues that arise in real-world business situations, so the course is a best fit for students interested in career paths involving AI, analytics, and data science.
Offered in Autumn 2025 at Stanford University.