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.
Units: 3

Grading: GSB Letter Graded
OIT 274: Data and Decisions  Base (Flipped Classroom)
Base Data and Decisions is a firstyear MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a labbased 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, ttests, 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.
Units: 4

Grading: GSB Letter Graded
OIT 276: Data and Decisions  Accelerated (Flipped Classroom)
Accelerated Data and Decisions is a firstyear MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a more labbased 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, ttests, 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.
Units: 4

Grading: GSB Letter Graded
Instructors:
O'Hair, A. (PI)
;
Yurukoglu, A. (PI)
OIT 333: Design for Extreme Affordability
Design for Extreme Affordability is a twoquarter projectbased 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 world's poorest citizens. This multidisciplinary projectbased 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, partnerships, team dynamics and project management. Since the course was first offered, we have executed 140 projects with 57 partners in 31 emerging and developing economies around the world. Many of the projects have been implemented and are achieving significant social impact. Students have worked on Agricultural, Medical, Water, Sanitation, Energy, Lighting, Nutrition and Education based projects. For further information go to
extreme.stanford.edu
Units: 4

Grading: GSB Letter Graded
OIT 356: Electronic Business
The course focuses on the analysis and design of business models that are enabled by Information Technology (IT). It considers the impact of IT on multiple industries and ways to take advantage of new opportunities that are enabled by new technologies. Preparation includes case studies involving both qualitative and quantitative analyses; homework assignments involving quantitative and some qualitative analyses; preclass online quizzes and a group project involving the design of a new business model that takes advantage of modern IT. A typical class will cover an aspect of a business model which is enabled by IT in an industry which is transformed by technology. Sample topics include the transformation of retail, media, electronic commerce logistics, transportation and healthcare. Some classes will focus on the interaction of strategy and IT, building on what you learned earlier in the program. Students opting to go into technology would probably benefit the most from the course, but this will require a significant time commitment. For students rooted in technology, some of the classes will reinforce and structure things they have experienced already, and others would introduce them to new applications. MSx students are invited to petition to take the course.
Units: 3

Grading: GSB Letter Graded
Instructors:
Mendelson, H. (PI)
OIT 367: Business Intelligence from Big Data
The objective of this course is to analyze realworld situations where significant competitive advantage can be obtained through largescale 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 businessrelevant 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 R and SQL) to data sets to create knowledge that will inform decisions. The course covers fundamentals of statistical modeling, machine learning, and datadriven decision making. Students are expected to layer these topics over an existing facility with mathematical notation, algebra, calculus, probability, and basic statistics.
Units: 3

Grading: GSB Letter Graded
Instructors:
Bayati, M. (PI)
OIT 384: Biodesign Innovation: Needs Finding and Concept Creation
In this twoquarter course series (
OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify realworld 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 2019), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm and screen initial technologybased solutions. In the second quarter (spring 2019), teams screen their ideas, select a lead solution, and move it toward the market through prototyping, technical rerisking, strategies to address healthcarespecific 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 facultyled instruction and case studies, coa
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In this twoquarter course series (
OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify realworld 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 2019), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm and screen initial technologybased solutions. In the second quarter (spring 2019), teams screen their ideas, select a lead solution, and move it toward the market through prototyping, technical rerisking, strategies to address healthcarespecific 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 facultyled 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/stanfordcourses/biodesigninnovation.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 40 venturebacked healthcare companies and has helped hundreds of students launch health technology careers, can be found at
http://biodesign.stanford.edu/.
Units: 4

Grading: GSB Student Option LTR/PF
Instructors:
Yock, P. (PI)
;
Brinton, T. (SI)
;
Denend, L. (SI)
;
Venook, R. (SI)
;
Watkins, F. (SI)
OIT 674: Decisionmaking and Learning under Model Uncertainty: Theory and Applications
In most realworld problems, decisionmakers often face uncertainty with respect to the underlying models that drive the rewards/costs associated with potential strategies. The uncertainty in the problem can be modeled in a number of ways (e.g., a probability distribution over some parameters or an uncertainty set for some variables) and a selection of an appropriate framework depends on various considerations ranging from the availability of historical data (or lack thereof) to the robustness of resulting strategies or the tractability of the formulation. In addition, once a framework is selected, further challenges often arise when considering dynamic settings, in which the level of uncertainty may be updated from one period to another. The highlevel objectives of this course are: (1) to introduce various frameworks for decisionmaking under model uncertainty (2) to introduce tools to solve such problems, including ones to develop optimal or nearoptimal learning strategies (3) to d
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In most realworld problems, decisionmakers often face uncertainty with respect to the underlying models that drive the rewards/costs associated with potential strategies. The uncertainty in the problem can be modeled in a number of ways (e.g., a probability distribution over some parameters or an uncertainty set for some variables) and a selection of an appropriate framework depends on various considerations ranging from the availability of historical data (or lack thereof) to the robustness of resulting strategies or the tractability of the formulation. In addition, once a framework is selected, further challenges often arise when considering dynamic settings, in which the level of uncertainty may be updated from one period to another. The highlevel objectives of this course are: (1) to introduce various frameworks for decisionmaking under model uncertainty (2) to introduce tools to solve such problems, including ones to develop optimal or nearoptimal learning strategies (3) to discuss the various tradeoffs that arise such as tractability vs. performance, exploration vs. exploitation, and remembering vs. forgetting (4) to explore research papers that demonstrate applications of discussed methods and models to various problems areas such as dynamic pricing, revenue management, product recommendations, and assortment selection
Units: 3

Grading: GSB Letter Graded
Instructors:
Gur, Y. (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.
Units: 115

Repeatable for credit

Grading: GSB Pass/Fail
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)
;
Wager, S. (PI)
;
Wein, L. (PI)
;
Weintraub, G. (PI)
;
Whang, S. (PI)
;
Xu, K. (PI)
;
Zenios, S. (PI)