## 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 simple machine learning / prediction models. The group regression project is a key component of the course, and all students will learn the statistical software package R and use the AI tools Copilot and ChatGPT.

Terms: Win
| Units: 3

## 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.

Terms: Win
| Units: 3

Instructors:
Spiess, J. (PI)

## OIT 280: Operations, Innovation, and Technology I

This course is the first part of a new two quarter course series (
OIT 280 &
OIT 281) that offers students a holistic perspective on the rapidly evolving and integrated world of operations, technology and innovation.
OIT 280 covers fundamental concepts and tools for excellent operations and new content on how business models, operational processes, technology and innovation come together in the real world.
OIT 280 is more methodological, focusing on key operational processes and how they interact with business models and innovation processes.
OIT 281 is more hands on, focusing on innovation processes. In
OIT 281, students study and practice the creation of new business and operating models and engage in an innovation project. In
OIT280, students focus on learning the key analytical tools and prepare a proposal for their innovation project. The course is under construction. Don't take it if you cannot tolerate sharp turns.

Terms: Win
| Units: 3

## OIT 333: Design for Extreme Affordability

Design for Extreme Affordability (¿Extreme¿) is for students who have a passion for social impact, and want to experience designing products and services that address issues of global poverty, through tackling real world challenges in collaboration with low-resource communities. Extreme is a two-quarter graduate level sequence cross listed by the Graduate School of Business (
OIT333/334) and the School of Engineering (
ME206A/B). The program is hosted by the d.school and open to students from all Stanford schools. This multidisciplinary team, fast paced, project based experience creates an enabling environment in which students learn to design products and services that will change the lives of the world's poorest citizens. Students work directly with course partners, and the communities they serve, on real world problems, the culmination of which is actual implementation and real impact. Topics include design thinking, product and service design, rapid prototype engineering and testin
more »

Design for Extreme Affordability (¿Extreme¿) is for students who have a passion for social impact, and want to experience designing products and services that address issues of global poverty, through tackling real world challenges in collaboration with low-resource communities. Extreme is a two-quarter graduate level sequence cross listed by the Graduate School of Business (
OIT333/334) and the School of Engineering (
ME206A/B). The program is hosted by the d.school and open to students from all Stanford schools. This multidisciplinary team, fast paced, project based experience creates an enabling environment in which students learn to design products and services that will change the lives of the world's poorest citizens. Students work directly with course partners, and the communities they serve, on real world problems, the culmination of which is actual implementation and real impact. Topics include design thinking, product and service design, rapid prototype engineering and testing, business modeling, social entrepreneurship, team dynamics, impact measurement, operations planning and ethics. Products and services designed in the class have impacted well over 150 million people worldwide. Limited enrollment by application. Must sign up for both
OIT333/
ME206A (Winter) and
OIT334/
ME206B (Spring).See
extreme.stanford.edu for more details and application process which opens in October. Cardinal Course certified by the Haas Center for Public Service.

Terms: Win
| Units: 4

Instructors:
Coulson, S. (PI)
;
Yeturu, M. (SI)

## OIT 367: Business Intelligence from Big Data

The objective of this course is to analyze real-world situations in which a significant competitive advantage can be obtained through large-scale data analysis. Particular attention is given to the actionable insights that can be derived from data and the potential pitfalls associated with data-driven approaches. Students are challenged to formulate business-relevant questions and solve them through the manipulation of large data sets. The course showcases applications from diverse domains, including advertising, eCommerce, finance, healthcare, marketing, and revenue management. Students will learn to apply technologies such as Artificial Intelligence, Python, and SQL to analyze data sets and generate knowledge that informs decision-making. The course covers the fundamentals of data-driven decision-making, including statistical modeling, machine learning, and experimental design. Students are expected to integrate these topics with their existing proficiency in mathematical notation, algebra, calculus, probability, and basic statistics.

Terms: Win
| Units: 3

Instructors:
Bayati, M. (PI)

## OIT 384: Biodesign Innovation: Needs Finding and Concept Creation

In this two-quarter course series (
OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify real-world unmet health-related needs, invent new technologies to address them, and plan for their development and implementation into patient care. During the first quarter (winter), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm initial technology-based solutions. In the second quarter (spring), 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, payment), and business planning. Final presentations are made to a panel of prominent health technology industry experts and/or investors. Class sessions include faculty-led instruction and case studies, coaching sessions by industry specialists, expert gu
more »

In this two-quarter course series (
OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify real-world unmet health-related needs, invent new technologies to address them, and plan for their development and implementation into patient care. During the first quarter (winter), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm initial technology-based solutions. In the second quarter (spring), 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, payment), and business planning. Final presentations are made to a panel of prominent health technology industry experts and/or 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 dozens of 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

Instructors:
Denend, L. (PI)
;
Edmonds, Z. (SI)
;
Makower, J. (SI)
...
more instructors for OIT 384 »

Instructors:
Denend, L. (PI)
;
Edmonds, Z. (SI)
;
Makower, J. (SI)
;
Venook, R. (SI)
;
Sunier, S. (TA)

## OIT 666: Engineering Online Markets

This class will explore topics the intersection of operations, engineering, and economics relevant to modern internet marketplaces, including those for dating, labor, accommodation, services, and rides. The objective of the class is to introduce and revisit technical tools traditionally used in the operations literature that may help advance the research frontier, as well as to expose students to recent developments and state-of-the-art research in online markets. The class will not cover the important and heavily studied topics of stable marriage and auctions. List of topics (preliminary): Intro to two-sided platforms and search frictions in matching markets; Design of the "search environment" and information disclosure policies on platforms; Balancing supply and demand in a spatio-temporal environment; Pricing issues in platforms; Service platforms; Reputation systems.

Terms: Win
| Units: 3

Instructors:
Saban, D. (PI)
;
Ashlagi, I. (SI)

## OIT 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, MKTG 691, OB 691, POLECON 691, STRAMGT 691)

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

Instructors:
Bayati, M. (PI)
;
Bimpikis, K. (PI)
;
Gur, Y. (PI)
;
Hu, Y. (PI)
;
Iancu, D. (PI)
;
Karaduman, O. (PI)
;
Mendelson, H. (PI)
;
Plambeck, E. (PI)
;
Saban, D. (PI)
;
Spiess, J. (PI)
;
Wager, S. (PI)
;
Wein, L. (PI)
;
Weintraub, G. (PI)
;
Xu, K. (PI)
;
Zenios, S. (PI)

## OIT 692: PhD Dissertation Research (ACCT 692, FINANCE 692, GSBGEN 692, HRMGT 692, MGTECON 692, MKTG 692, OB 692, POLECON 692, STRAMGT 692)

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)
;
Hu, Y. (PI)
;
Iancu, D. (PI)
;
Karaduman, O. (PI)
;
Mendelson, H. (PI)
;
Plambeck, E. (PI)
;
Saban, D. (PI)
;
Spiess, J. (PI)
;
Wager, S. (PI)
;
Wein, L. (PI)
;
Weintraub, G. (PI)
;
Xu, K. (PI)
;
Zenios, S. (PI)

## OIT 698: Doctoral Practicum in Teaching

Doctoral Practicum in Teaching

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

Instructors:
Bayati, M. (PI)
;
Bimpikis, K. (PI)
;
Gur, Y. (PI)
;
Hu, Y. (PI)
;
Iancu, D. (PI)
;
Karaduman, O. (PI)
;
Mendelson, H. (PI)
;
Plambeck, E. (PI)
;
Saban, D. (PI)
;
Spiess, J. (PI)
;
Wager, S. (PI)
;
Wein, L. (PI)
;
Weintraub, G. (PI)
;
Xu, K. (PI)
;
Zenios, S. (PI)