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1 - 10 of 28 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.
Units: 3 | Grading: GSB Letter Graded

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.
Units: 3 | Grading: GSB Letter Graded
Instructors: Xu, K. (PI)

OIT 248: The Art and Science of Optimization Modeling in Practice

This course is the Advanced Applications option in the menu of courses that satisfy the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course will focus on using optimization techniques in practice, with the following objectives: (1) Students should leave with a good understanding of different types of optimization models and when they are useful; (2) Students should be able to solve real-world optimization models, and use tips and tricks for solving these models efficiently; (3) When faced with a business problem, students should be able to identify whether or not optimization is appropriate, and how to set up the correct model to solve the problem. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, and scheduling. We will be using the software Gurobi through Python. Students should be comfortable using these software packages by the end of the class, but no prior experience specifically with these software packages is necessary. Some prior coding experience is helpful, but the first week of the course is designed to bring all students up to speed with Python.
Units: 3 | Grading: GSB Letter Graded
Instructors: O'Hair, A. (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.
Units: 3 | Grading: GSB Letter Graded
Instructors: Plambeck, E. (PI)

OIT 269: MSx: Operations and Strategies

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.
Units: 3 | Grading: GSB Letter Graded

OIT 271: Operations - Accelerated

This course, which is an accelerated version of OIT 262 (Operations), focuses on basic managerial issues arising in the operations of both manufacturing and service industries, and on strategic issues arising in global supply chains. 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.
Units: 3 | Grading: GSB Letter Graded
Instructors: Wein, L. (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 Microeconomics (all base). 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.
Units: 2 | Grading: GSB Letter Graded

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.
Units: 3 | Grading: GSB Letter Graded

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 Microeconomics. 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.
Units: 2 | Grading: GSB Letter Graded

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.
Units: 3 | Grading: GSB Letter Graded
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