## Results for OIT |
14 courses |

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.

Terms: Win
| Units: 3

Instructors: ; Gur, Y. (PI); Lee, H. (PI); Gur, Y. (SI); Lee, H. (SI); Khojasteh, J. (GP); Ponce, S. (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, Spr
| Units: 4

Instructors: ; Benkard, L. (PI); Diamond, R. (PI); Somaini, P. (PI); Alvarez, G. (GP); Kocharyan, N. (GP); Smith, J. (GP)

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: 4

This course is a Bass Seminar. Project course jointly offered by School of Engineering and Graduate School of Business. Students apply engineering and business skills to design product or service prototypes, distribution systems, and business plans for entrepreneurial ventures that meet that challenges faced by the world's poor. Topics include user empathy, appropriate technology design, rapid prototype engineering and testing, social technology entrepreneurship, business modeling, and project management. Weekly design reviews; final course presentation. Industry and adviser interaction. Limited enrollment via application; see http://extreme.stanford.edu/ for details.

Terms: Win
| Units: 4

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. Instructional methods include case studies involving both qualitative and quantitative analyses; homework assignments involving quantitative and some qualitative analyses; and a course project involving the design of a new or incremental business model that takes advantage of modern IT. nnA 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, disruptive technologies, value chain coordination in healthcare, and mobile value chains.nnThe course requires a strong analytic background and knowledge of fundamental aspects of IT. MSx students may petition to take the course.

Terms: Win
| Units: 3

Instructors: ; Mendelson, H. (PI); Gutierrez, S. (GP)

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 R 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 2018), 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 2018), 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 40 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 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); Gutierrez, S. (GP)

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); Aranzamendez, O. (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

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); Alvarez, K. (GP); Aranzamendez, O. (GP); Davis, S. (GP); Lion-Transler, C. (GP); Moore, N. (GP); Patel, A. (GP); Ponce, S. (GP); Smeton, K. (GP); Wells, K. (GP)

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); Wager, S. (PI); Wein, L. (PI); Weintraub, G. (PI); Whang, S. (PI); Xu, K. (PI); Zenios, S. (PI); Davis, S. (GP); Khojasteh, J. (GP); Ponce, S. (GP); Smeton, K. (GP); Wells, K. (GP)

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)

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); Alvarez, K. (GP); Aranzamendez, O. (GP); Davis, S. (GP); Gutierrez, S. (GP); Lion-Transler, C. (GP); Moore, N. (GP); Patel, A. (GP); Ponce, S. (GP); Wells, 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); Wager, S. (PI); Wein, L. (PI); Weintraub, G. (PI); Whang, S. (PI); Xu, K. (PI); Zenios, S. (PI); Alvarez, K. (GP); Gutierrez, S. (GP); Khojasteh, J. (GP); Lion-Transler, C. (GP); Patel, A. (GP); Zweig, S. (GP)