## Results for OIT |
13 courses |

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); Bagalso, R. (GP)

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

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); Rivera, S. (GP)

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

The objective of this course is to analyze real-world situations where significant competitive advantage can be obtained through large-scale data analysis, with special attention to what can be done with the data and where the potential pitfalls lie. Students will be challenged to develop business-relevant questions and then solve for them by manipulating large data sets. Problems from advertising, eCommerce, finance, healthcare, marketing, and revenue management are presented. Students learn to apply software (such as Python and SQL) to data sets to create knowledge that will inform decisions. The course covers fundamentals of statistical modeling, machine learning, and data-driven decision making. Students are expected to layer these topics over an existing facility with mathematical notation, algebra, calculus, probability, and basic statistics.

Terms: Win
| Units: 3

Instructors: ; Bayati, M. (PI); Davis, S. (GP)

In this two-quarter course series (OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify real-world unmet healthcare needs, invent new health technologies to address them, and plan for their development and implementation into patient care. During the first quarter (winter 2021), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm and screen initial technology-based solutions. In the second quarter (spring 2021), teams screen their ideas, select a lead solution, and move it toward the market through prototyping, technical re-risking, strategies to address healthcare-specific requirements (regulation, reimbursement), and business planning. Final presentations in winter and spring are made to a panel of prominent health technology industry experts and investors. Class sessions include faculty-led instruction and case studies, coaching sessions by industry specialists, expert guest lecturers, and interactive team meetings. Enrollment is by application only, and students are expected to participate in both quarters of the course. Visit http://biodesign.stanford.edu/programs/stanford-courses/biodesign-innovation.html to access the application, examples of past projects, and student testimonials. More information about Stanford Biodesign, which has led to the creation of more than 50 venture-backed healthcare companies and has helped hundreds of students launch health technology careers, can be found at http://biodesign.stanford.edu/.

Terms: Win
| Units: 4

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)

The goal of this course is to provide a comprehensive introduction to the theory and practice of revenue management. It will comprise of a set of lectures that will cover the theoretical fundamentals of the area as well as an overview of current research developments through the presentation and discussion of recent papers. Topics include capacity control (single-resource and network), consumer behavior and market response models, dynamic pricing, procurement auctions, price experimentation, supply chain management and pricing.

Terms: Win
| Units: 3

Instructors: ; Bimpikis, K. (PI); Alvarez, K. (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

Instructors: ; Bayati, M. (PI); Bimpikis, K. (PI); Gur, Y. (PI); Iancu, D. (PI); Lee, H. (PI); Mendelson, H. (PI); Plambeck, E. (PI); Saban, D. (PI); Spiess, J. (PI); Wager, S. (PI); Wein, L. (PI); Weintraub, G. (PI); Whang, S. (PI); Xu, K. (PI); Zenios, S. (PI); Davis, S. (GP); Patel, A. (GP); Rivera, S. (GP); Smeton, K. (GP)

Doctoral Practicum in Teaching

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

Doctoral Practicum in Research

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

Instructors: ; Bayati, M. (PI); Bimpikis, K. (PI); Gur, Y. (PI); Iancu, D. (PI); Lee, H. (PI); Mendelson, H. (PI); Plambeck, E. (PI); Saban, D. (PI); Spiess, J. (PI); Wager, S. (PI); Wein, L. (PI); Weintraub, G. (PI); Whang, S. (PI); Xu, K. (PI); Zenios, S. (PI); Alvarez, K. (GP); Davis, S. (GP); Lion-Transler, C. (GP); Lumagui, S. (GP); Moore, N. (GP); Patel, A. (GP); Smeton, K. (GP); Zweig, S. (GP)

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

Instructors: ; Bayati, M. (PI); Bimpikis, K. (PI); Gur, Y. (PI); Iancu, D. (PI); Lee, H. (PI); Mendelson, H. (PI); Plambeck, E. (PI); Saban, D. (PI); Spiess, J. (PI); Wager, S. (PI); Wein, L. (PI); Weintraub, G. (PI); Whang, S. (PI); Xu, K. (PI); Zenios, S. (PI); Alvarez, K. (GP); Davis, S. (GP); Lumagui, S. (GP); Moore, N. (GP); Patel, A. (GP)