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1 - 10 of 27 results for: OIT ; Currently searching offered courses. You can also include unoffered courses

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: Bimpikis, K. (PI)

OIT 248: Optimization And Simulation Modeling - Advanced

This course constitutes an advanced option in the menu of classes satisfying the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is a superset of OIT 245 and OIT 247, starting with a very fast paced overview of basic concepts, and quickly diving into more advanced topics and software tools. By the end of the course, students should (1) leave with a good understanding of different types of optimization and simulation models and when they are useful; (2) be able to solve real-world models using up-to-date software; (3) when faced with a business problem, be able to identify what type of optimization model is appropriate, and how to set it up most efficiently; (4) be able to understand and discuss model outputs in a critical fashion. 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 manag more »
This course constitutes an advanced option in the menu of classes satisfying the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is a superset of OIT 245 and OIT 247, starting with a very fast paced overview of basic concepts, and quickly diving into more advanced topics and software tools. By the end of the course, students should (1) leave with a good understanding of different types of optimization and simulation models and when they are useful; (2) be able to solve real-world models using up-to-date software; (3) when faced with a business problem, be able to identify what type of optimization model is appropriate, and how to set it up most efficiently; (4) be able to understand and discuss model outputs in a critical fashion. 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 will be using Python as the basic software, complemented with suitable packages for formulating and solving optimization models (e.g., the Gurobi software) and for conducting Monte Carlo simulation. Students should be comfortable using these software packages by the end of the class, but no specific prior experience with these packages is necessary. Some prior coding experience is helpful, but is not a strict requirement for the course.
Terms: Aut | Units: 3
Instructors: Whang, S. (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: Win | Units: 2
Instructors: Reiss, P. (PI)

OIT 262: Operations

This course focuses on basic managerial issues arising in the operations of both manufacturing and service industries. The objectives of the course are to familiarize students with the problems and issues confronting operations managers and to introduce language, conceptual models, and analytical techniques that are broadly applicable in confronting such problems. The spectrum of different process types used to provide goods and services is developed and then examined through methods of process analysis and design.
Terms: Spr | Units: 3
Instructors: Plambeck, E. (PI)

OIT 272: Online Marketplaces

How does Uber match drivers to passengers? How does Airbnb select the set of listings to show to a guest in a search? How does eBay manage trust and reputation between buyers and sellers? How does Google optimize auctions for billions of dollars' worth of online advertising? This course focuses on the basic analytic and data science tools used to address these and other challenges encountered in the most exciting online marketplaces in the world. With hands-on exercises we will open and understand the "black-box" of online marketplaces' operations. We will cover application areas such as transportation, rentals, sharing, e-commerce, labor markets, and advertising, leveraging tools from D&D, OSM, and Micro (all base). In particular, the course will use tools from R covered in D&D. Overall, the course will provide basic business knowledge for future investors, product managers, sales and marketing managers, operation 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

OIT 275: Online Marketplaces, Accelerated

How does Uber match drivers to passengers? How does Airbnb select the set of listings to show to a guest in a search? How does eBay manage trust and reputation between buyers and sellers? How does Google optimize auctions for billions of dollars' worth of online advertising? This course focuses on analytics and data science tools used to address these and other challenges encountered in the most exciting online marketplaces in the world. With hands-on exercises we will open and understand the "black-box" of online marketplaces' operations. We will cover application areas such as transportation, rentals, sharing, e-commerce, labor markets, and advertising, leveraging tools from D&D, OSM, and Micro. In particular, the course will use tools from R covered in D&D. Overall, the course will provide business knowledge for future investors, product managers, sales and marketing managers, operation managers, and anyone interested on online marketplaces. [This is the accelerated version of OIT 272 and knowledge from D&D and OSM is expected at the accelerated (or advanced) level.]
Terms: Spr | Units: 2

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 333: Design for Extreme Affordability

Design for Extreme Affordability is a two-quarter project-based course hosted by Stanford's d.school and jointly offered by the Graduate School of Business and the School of Mechanical Engineering. We focus on the development of products and services to improve the lives of the our poorest citizens. This multidisciplinary project-based experience creates an enabling environment in which students learn to design products and services that will change lives. Topics include user empathy, product and service design, rapid prototype engineering and testing, social entrepreneurship, business modeling, ethics, equity, partnerships, team dynamics and project management. Since the course was first offered, we have executed 168 projects with 72 partners. Many of the projects have been implemented and are achieving significant social impact. This year we will launch for the first time, Extreme Local, where we will team up with local Bay Area partners to address some of their challenges, We will continue to publish latest information for prospective students here: https://extreme.stanford.edu/prospective-students
Terms: Win | Units: 4
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