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 inclass 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
Instructors:
Bimpikis, K. (PI)
;
Saban, D. (SI)
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:
O'Hair, A. (PI)
;
Xu, K. (PI)
OIT 248: The Art and Science of Optimization Modeling in Practice
This is the Advanced Applications option in the menu of courses that satisfy the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is tailored to students who already have command of basic optimization and modeling techniques, or who have a quantitative background that will allow them to catch up quickly. Some basic programming will be required, so experience with at least one programming language is recommended. The course will focus on using optimization techniques in practice. We will start by discussing different types of optimization models, including linear, integer, and quadratic optimization models. We will then discuss modeling techniques to make these models more realistic, such as multiobjective approaches and using regression models within optimization formulations. Lastly, we will cover tips and tricks for solving large models in practice, such as setting solving limits and heuristics.
Units: 3

Grading: GSB Letter Graded
Instructors:
O'Hair, A. (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.
Units: 2

Grading: GSB Letter Graded
Instructors:
Reiss, P. (PI)
OIT 256: Electronic Business (Accelerated)
This course focuses on the way information technology affects the structure of business models. It considers the impact of information technology on industries ranging from retail to logistics and from healthcare to smartphones. It considers how you can take advantage of new technology opportunities and how they change the structure of firms, industries and value chains, with an emphasis on business issues. Classes combine lecture and case study discussions and the workload is above the GSB average. The course is designed to help you make a transition into technologyrelated fields.
Units: 2

Grading: GSB Letter Graded
OIT 258: Incentive Mechanisms for Societal Networks
In many of the challenges faced by the modern world, from overcrowded road networks to overstretched healthcare systems, large benefits for society come about from small changes by very many individuals. This course survey the problems and the cost they impose on society. It describes a series of pilot projects which aim to develop principles for inducing small changes in behavior in Societal Networkstransportation networks, wellness programs, recycling systems and, if time permits, energy grids. Students will learn how lowcost sensing and networking technology can be used for sensing individual behavior, and how incentives and social norming can be used to influence the behavior. The effectiveness of this approach in pilots conducted in Bangalore (commuting), Singapore (public transit system), Stanford (congestion and parking), and a wellness program at AccentureUSA will be discussed. Students may experience the incentive platform as participants.
Units: 2

Grading: GSB Letter Graded
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:
Gur, Y. (PI)
;
Plambeck, E. (PI)
OIT 265: Data and Decisions
This is the base version of D&D. This course introduces the fundamental concepts and techniques for analyzing risk and formulating sound decisions in uncertain environments. Approximately half of the course focuses on probability and its application. The remainder of the course examines statistical methods for interpreting and analyzing data including sampling concepts, regression analysis, and hypothesis testing. Applications include inventory management, demand analysis, portfolio analysis, surveys and opinion polls, A/B testing, environmental contamination, online advertising and the role of analytics in business settings more generally. The course emphasizes analytical techniques and concepts that are broadly applicable to business problems.
Units: 3

Grading: GSB Letter Graded
Instructors:
Somaini, P. (PI)
OIT 267: Data and Decisions  Accelerated
Data and Decisions  Accelerated is a firstyear MBA course in probability and statistics for students with strong quantitative backgrounds. Probability provides the foundation for modeling uncertainties. Statistics provides techniques for interpreting data, permitting managers to use small amounts of information to answer larger questions. In statistics, we focus on the linear regression model. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. We introduce topics from nonlinear models and machine learning model selection. Students taking this course need to be comfortable with mathematical notation, algebra, some calculus, and be open to learning to write short programs in statistical software (eg R or Stata). If you are not confident with your quantitative abilities, then you should enroll in
OIT 265. Accelerated D&D will cover material covered in
OIT 265 plus some additional topics such as discrete dependent variable models. While
OIT 267 focuses on real world applicability, we will explore the mathematical underpinnings of these topics in more depth than
OIT 265 as an avenue for deeper understanding. The group regression project is a key component of the course.
Units: 3

Grading: GSB Letter Graded
Instructors:
Yurukoglu, A. (PI)
OIT 268: Making Data Relevant
Data is everywhere. Firms collect it. Data on customers' preferences are collected through websites or loyalty programs or cash registers. Data on employees' traits are collected through inhouse databanks or social networking sites. All of us are used to thinking about data. How can you make data relevant to doing your job? How can data analysis serve to increase your competitive advantage over that of others? This class goes beyond graphing data in bar charts or time trends. It makes you think about causal relationships. The examples we use are primarily taken from talent management, because it's easy to think about our own careers or those of our employees. But the tools covered extend to all contexts, and your project is on an idea of your choosing. The class focuses on the use of regressions to think experimentally. To take the class, you should have covered regression analysis in a former class (such as an econometrics course for economics majors) or be comfortable with learning
more »
Data is everywhere. Firms collect it. Data on customers' preferences are collected through websites or loyalty programs or cash registers. Data on employees' traits are collected through inhouse databanks or social networking sites. All of us are used to thinking about data. How can you make data relevant to doing your job? How can data analysis serve to increase your competitive advantage over that of others? This class goes beyond graphing data in bar charts or time trends. It makes you think about causal relationships. The examples we use are primarily taken from talent management, because it's easy to think about our own careers or those of our employees. But the tools covered extend to all contexts, and your project is on an idea of your choosing. The class focuses on the use of regressions to think experimentally. To take the class, you should have covered regression analysis in a former class (such as an econometrics course for economics majors) or be comfortable with learning basic math concepts quickly. You also should understand distributions of data (such as the Bell curve, or normal distribution), but this topic is not covered. There are no required proofs or derivations; you've done that as undergraduates. This is about using data: we use cases, examples, Notes written for the class, and a quiz, final exam, and several assignments in which you play with data sets to answer questions. Note that this 4unit course, if successfully completed, counts for the Data Analysis foundations requirement.
Units: 4

Grading: GSB Letter Graded
Filter Results: