## Results for OIT |
10 courses |

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); Lumagui, S. (GP)

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); Alvarez, K. (GP)

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 an advanced version of OIT 245 and OIT 247, covering a similar set of basic concepts of OSM (such as modelling for optimization, math programming, Monte Carlo simulations and decision tree). But here we use Python, instead of Excel. By the end of the course, students should develop the basic mental framework of optimization and leave with a good understanding of different types of optimization and simulation. 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. In terms of software, we will use Python as the basic software, complemented with Gurobi for formulating and solving optimization models. Note well, however, that this is not a course on Python, but on modelling and optimization. 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); Smeton, K. (GP)

This year-long course takes a hands-on approach to learning about conducting research in Operations, Information and Technology. It will cover a broad spectrum of cutting-edge research in OIT from conceiving an idea to formulating a research problem, deriving results, and publication. The topical content will be customized to the specific interests of the enrolled students, but generally will be concerned with questions of operational interest.

Terms: Aut, Win, Spr
| Units: 1
| Repeatable
12 times
(up to 12 units total)

In this course we cover current research on data-driven approaches to the market design of online platforms. We cover diverse topics such as search, matching, demand estimation, learning under strategic behavior, and pricing. We will do so in the context of different application domains such as rentals, sharing, e-commerce, and advertising. The course is eclectic in terms of approaches, using reduced-form and structural econometrics, machine learning, and experimentation. The course mostly consists of recent papers presented by the instructor, guests, and students. Some background knowledge required to understand current work is provided as needed.

Terms: Aut
| Units: 2

Instructors: ; Weintraub, G. (PI); Lumagui, S. (GP)

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

Doctoral Practicum in Teaching

Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
25 times
(up to 50 units total)

Doctoral Practicum in Research

Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable
25 times
(up to 50 units total)

Terms: Aut, Win, Spr, Sum
| Units: 0
| Repeatable
for credit