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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.

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.

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.

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.

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.

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.

Design for Extreme Affordability (¿Extreme¿) is for students who have a passion for social impact, and want to experience designing products and services that address issues of global poverty, through tackling real world challenges in collaboration with low-resource communities. Extreme is a two-quarter graduate level sequence cross listed by the Graduate School of Business ( OIT333/334) and the School of Engineering ( ME206A/B). The program is hosted by the d.school and open to students from all Stanford schools. This multidisciplinary team, fast paced, project based experience creates an enabling environment in which students learn to design products and services that will change the lives of the world's poorest citizens. Students work directly with course partners, and the communities they serve, on real world problems, the culmination of which is actual implementation and real impact. Topics include design thinking, product and service design, rapid prototype engineering and testing, business modeling, social entrepreneurship, team dynamics, impact measurement, operations planning and ethics. Products and services designed in the class have impacted well over 150 million people worldwide. Limited enrollment by application. Must sign up for both OIT333/ ME206A (Winter) and OIT334/ ME206B (Spring).See extreme.stanford.edu for more details and application process which opens in October. Cardinal Course certified by the Haas Center for Public Service.

Design for Extreme Affordability (¿Extreme¿) is for students who have a passion for social impact, and want to experience designing products and services that address issues of global poverty, through tackling real world challenges in collaboration with low-resource communities. Extreme is a two-quarter graduate level sequence cross listed by the Graduate School of Business ( OIT333/334) and the School of Engineering ( ME206A/B). The program is hosted by the d.school and open to students from all Stanford schools. This multidisciplinary team, fast paced, project based experience creates an enabling environment in which students learn to design products and services that will change the lives of the world's poorest citizens. Students work directly with course partners, and the communities they serve, on real world problems, the culmination of which is actual implementation and real impact. Topics include design thinking, product and service design, rapid prototype engineering and testing, business modeling, social entrepreneurship, team dynamics, impact measurement, operations planning and ethics. Products and services designed in the class have impacted well over 150 million people worldwide. Limited enrollment by application. Must sign up for both OIT333/ ME206A (Winter) and OIT334/ ME206B (Spring).See extreme.stanford.edu for more details and application process which opens in October. Cardinal Course certified by the Haas Center for Public Service.

The objective of this course is to analyze real-world situations in which a significant competitive advantage can be obtained through large-scale data analysis. Particular attention is given to the actionable insights that can be derived from data and the potential pitfalls associated with data-driven approaches. Students are challenged to formulate business-relevant questions and solve them through the manipulation of large data sets. The course showcases applications from diverse domains, including advertising, eCommerce, finance, healthcare, marketing, and revenue management. Students will learn to apply technologies such as Artificial Intelligence, Python, and SQL to analyze data sets and generate knowledge that informs decision-making. The course covers the fundamentals of data-driven decision-making, including statistical modeling, machine learning, and experimental design. Students are expected to integrate these topics with their existing proficiency in mathematical notation, algebra, calculus, probability, and basic statistics.