## 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

Instructors:
Saban, D. (PI)
;
Bimpikis, K. (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.

Terms: Aut
| Units: 3

Instructors:
Xu, K. (PI)
;
Whang, S. (SI)

## 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 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:
Iancu, D. (PI)
;
O'Hair, A. (SI)

## 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: Sum
| 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 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.

Last offered: Winter 2017

## OIT 267: Data and Decisions - Accelerated

Data and Decisions - Accelerated is a first-year 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 non-linear 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.

Last offered: Winter 2017

## 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.

Terms: Win
| Units: 3

## 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.

Terms: Spr
| Units: 3

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 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

Instructors:
Weintraub, G. (PI)

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