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# 1 - 10 of 39 results for: OIT

This course focuses on the intersection of strategy and information technology. It considers how you can take advantage of new technology opportunities and how they change the structure of firms, industries and value chains, with an emphasis on business issues. Classes combine lecture and case study discussions and the workload is above the GSB average.

## 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.

## OIT 267:Data and Decisions - Accelerated

Data and Decisions - Accelerated is a first-year MBA course in probability, statistics, multiple regression analysis, and decision trees 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. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. Decision tree analysis consists of quantitative approaches to decision making under uncertainty. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. 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.

## 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.

## OIT 344:Design for Service Innovation

This course focuses on the intersection of strategy and information technology. It considers how you can take advantage of new technology opportunities and how they change the structure of firms, industries and value chains, with an emphasis on business issues. Classes combine lecture and case study discussions and the workload is above the GSB average. While the advanced course will generally cover the same topics as OIT 256, it will go into more advanced techniques in a number of areas.

This is the first quarter of a two-quarter course series ( OIT 384/ OIT 385). In this course, students learn how to develop comprehensive solutions (most commonly medical devices) to some of the most significant medical problems. The first quarter includes an introduction to needs finding methods, brainstorming and concept creation. Students learn strategies for understanding and interpreting clinical needs, researching literature and searching patents. Working in small entrepreneurial multidisciplinary teams, students gain exposure to clinical and scientific literature review, techniques of intellectual property analysis and feasibility, basic prototyping and market assessment. Students create, analyze and screen medical technology ideas, and select projects for future development. Final presentations at the end of the winter quarter to a panel of prominent inventors and investors in medical technology provide the impetus for further work in the spring quarter. Course format includes expert guest lecturers (Thu: 4:15 to 6:05 pm), faculty-led practical demonstrations and coaching sessions, and interactive team meetings (Tues: 4:15 to 6:05 pm). Projects from previous years included: prevention of hip fractures in the elderly; methods to accelerate healing after surgery; less invasive techniques for bariatric surgery; point of care diagnostics to improve emergency room efficiency; novel devices to bring specialty-type of care to primary care community doctors. More than 300,000 patients have been treated to date with technologies developed as part of this program and more than thirty venture-backed companies were started by alums of the program. Students must apply and be accepted into the course. The application is available online at http://biodesign.stanford.edu/bdn/courses/bioe374.jsp.
Units: 4 | Grading: GSB Student Option LTR/PF

Two-quarter sequence (see OIT384 for complete description of the sequence). The second quarter focuses on how to take a conceptual solution to a medical need forward into development and potential commercialization. Continuing work in teams with engineering and medical colleagues, students will learn the fundamentals of medical device prototyping; patent strategies; advanced planning for reimbursement and FDA approval; choosing a commercialization route (licensing vs. start-up); marketing, sales and distribution strategies; ethical issues including conflict of interest; fundraising approaches and cash requirements; financial modeling; essentials of developing a business or research plan/canvas; and strategies for assembling a development team. Final project presentations are made to a panel of prominent venture and corporate investors. New students (i.e. students who did not take OIT384 in the winter quarter) may be admitted, depending on team needs. Candidates need to submit an application at http://biodesign.stanford.edu/bdn/courses/bioe374app.jsp by March 1.
Units: 4 | Grading: GSB Student Option LTR/PF

## OIT 587:Global Biodesign

This course examines the development and commercialization of innovative medical technologies in different global settings. Faculty and guest speakers from the medtech field will discuss the status of the industry, as well as opportunities in and challenges to medical technology innovation unique to seven primary geographic regions: Africa, China, Europe, India, Japan, United States and Latin America. Students will be exposed to the biodesign innovation process, which provides a proven approach for identifying important unmet medical needs and inventing meaningful solutions to address them. They will also explore key differences between the covered geographies, which range from emerging markets with vast bottom-of-the-pyramid and growing middle class populations, to well-established markets with sophisticated demands and shifting demographics.
Units: 1 | Grading: GSB Pass/Fail

## OIT 664:Stochastic Networks

Processing network models may be used to represent service delivery systems, multi-stage manufacturing processes, or data processing networks. The first half of this two-unit course consists of lectures on performance analysis (e.g., estimating congestion and delay) for classical product-form networks and for Brownian networks. The second half consists of student presentations of recent papers on managing processing networks, typically with game-theoretic aspects. Prerequisites: Statistics 217 and 218, or consent of instructor; some prior exposure to stochastic models in general, and queueing theory in particular, is useful but not essential.
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