printer friendly pageOIT 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 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
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:
Wager, S. (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 testing,
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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 351: AI and Data Science: Strategy, Management and Entrepreneurship
How can one best put AI and Data Science to work in a modern company and manage data science teams effectively? Leaning on the emerging theory and best practices, we will examine companies at various sizes and stages, from seed through IPO, and study real-life cases to understand how companies should leverage AI, data science and machine learning to build effective teams, core competencies, and competitive advantages. We will draw similarities and contrasts between regular technology and data-science-heavy companies in terms of management, technical risks, and economics, and more. The students will learn how to reason about the cost and benefits of building up a data science capability within a company, how to best manage teams to maximize performance and innovation, as well as how to evaluate the value creation through data and AI from the perspective of investors. We will have several AI entrepreneurs, executives, and investors participating in discussions. This is a 3-unit version of
OIT 551. An up-to-date syllabus for
OIT 351 can be found on this site:
https://www.aistanford.org/.
Terms: Win
| Units: 3
Instructors:
Xu, K. (PI)
;
Voloch, L. (SI)
OIT 367: Business Intelligence from Big Data
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
| Repeatable
2 times
(up to 6 units total)
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 healthcare needs, invent new health 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 and screen 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, 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 sess
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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), 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), 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
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 644: Research in Operations, Information and Technology
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
15 times
(up to 15 units total)
Instructors:
Bayati, M. (PI)
;
Karaduman, O. (PI)
;
Mendelson, H. (PI)
...
more instructors for OIT 644 »
Instructors:
Bayati, M. (PI)
;
Karaduman, O. (PI)
;
Mendelson, H. (PI)
;
Zenios, S. (PI)
;
Spiess, J. (SI)
;
Wager, S. (SI)
OIT 652: OIT Modeling
This course is designed for OIT students of all cohorts. It will focus on alternative approaches to modeling the types of problems that arise in OIT research, based on the analysis of papers in the area.
Terms: Win
| Units: 3
Instructors:
Mendelson, H. (PI)
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
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