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1 - 10 of 19 results for: OIT

OIT 265:Data and Decisions

This is the base version of D&D. This course introduces the fundamental concepts and techniques for analyzing risk and formulating sound decisions in uncertain environments. Approximately half of the course focuses on probability and its application. The remainder of the course examines statistical methods for interpreting and analyzing data including sampling concepts, regression analysis, and hypothesis testing. Applications include inventory management, demand analysis, portfolio analysis, surveys and opinion polls, A/B testing, environmental contamination, online advertising and the role of analytics in business settings more generally. The course emphasizes analytical techniques and concepts that are broadly applicable to business problems.
Units: 3 | Grading: GSB Letter Graded
Instructors: Somaini, P. (PI)

OIT 267:Data and Decisions - Accelerated

Data and Decisions - Accelerated is a first-year MBA course in probability and statistics for students with strong quantitative backgrounds. Probability provides the foundation for modeling uncertainties. Statistics provides techniques for interpreting data, permitting managers to use small amounts of information to answer larger questions. In statistics, we focus on the linear regression model. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. We introduce topics from non-linear models and machine learning model selection. Students taking this course need to be comfortable with mathematical notation, algebra, some calculus, and be open to learning to write short programs in statistical software (eg R or Stata). If you are not confident with your quantitative abilities, then you should enroll in OIT 265. Accelerated D&D will cover material covered in OIT 265 plus some additional topics such as discrete dependent variable models. While OIT 267 focuses on real world applicability, we will explore the mathematical underpinnings of these topics in more depth than OIT 265 as an avenue for deeper understanding. The group regression project is a key component of the course.
Units: 3 | Grading: GSB Letter Graded

OIT 273:Value Chain Innovations in Developing Economies

This course is about how to use entrepreneurship and innovations in the value chains to create values in developing economies. The course will cover important principles and ways in which the value chains can be re-engineered or new business models can be designed to create values. In addition to materials covering a diversity of industries and geographical regions, the course will also enable students to be exposed to some of the interventions that the Stanford Institute of Innovation in Developing Economies (SEED) is working on in West Africa. Work and exam requirements: Students are expected to develop a project report on either portfolio companies related to SEED or other enterprises to show how value chain innovations can be advanced.
Units: 2 | Grading: GSB Letter Graded
Instructors: Lee, H. (PI)

OIT 274:Data and Decisions - Base (Lab-based Pilot)

Data and Decisions is a first-year MBA course in statistics and regression analysis. The base D&D lab-based pilot is a new version of the course 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 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 software R.
Units: 4 | Grading: GSB Letter Graded
Instructors: Benkard, L. (PI)

OIT 276:Data and Decisions - Accelerated (Lab-based Pilot)

Data and Decisions is a first-year MBA course in statistics and regression analysis. The accelerated D&D lab-based pilot is a new version of the course 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 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 software R. nThe accelerated course is designed for students with strong quantitative backgrounds. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. 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)

OIT 333:Design for Extreme Affordability

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/index.html for details.
Units: 4 | Grading: GSB Letter Graded

The course focuses on the analysis and design of business models that are enabled by information technology. It considers the impact of information technology on multiple industries and how you can take advantage of new opportunities that are enabled by new technologies. nThe course is a compressed 2-unit course, with three double-sessions during the first week of the course and three double-sessions during the third week. During the intermediate week, students work on a final project where they design or analyze a business model. nnEach double-session analyzes a different aspect of business models that are enabled by information technology. Topics include online platforms, business models for online retail, 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 information technology. MSx students may petition to take the course.
Units: 2 | Grading: GSB Letter Graded

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 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.
Units: 3 | Grading: GSB Letter Graded
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 identify real-world unmet healthcare needs, invent new medtech products to address them, and plan for their development into patient care. During the first quarter (winter 2017), 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 2017), teams 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 medtech experts and investors. Class sessions include faculty-led instruction and case demonstrations, 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 student launch health technology careers, can be found at http://biodesign.stanford.edu/.
Units: 4 | Grading: GSB Student Option LTR/PF

OIT 536:Data for Action: From Insights to Applications

Data for Action is an MBA compressed course dedicated to identifying value in and creating value from data. It deals with the technical, legal, regulatory and business strategic decisions that must be considered when delivering solutions to customers.
Units: 2 | Grading: GSB Letter Graded
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