printer friendly pageOIT 245: Optimization and Simulation Modeling
This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue and yield optimization.
Terms: Win
| Units: 2
Instructors:
Iancu, D. (PI)
OIT 247: Optimization and Simulation Modeling - Accelerated
The course is similar in content and emphasis to
OIT 245, but is aimed at students who already have background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth.
Terms: Win
| Units: 2
Instructors:
Bimpikis, K. (PI)
OIT 256: Electronic Business (Accelerated)
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.
Terms: Win
| Units: 3
Instructors:
Mendelson, H. (PI)
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.
Terms: Win, Spr
| Units: 4
OIT 268: Making Data Relevant
Data is everywhere. Firms collect it. Data on customers' preferences are collected through websites or loyalty programs or cash registers. Data on employees' traits are collected through in-house databanks or social networking sites. All of us are used to thinking about data. How can you make data relevant to doing your job? How can data analysis serve to increase your competitive advantage over that of others? This class goes beyond graphing data in bar charts or time trends. It makes you think about causal relationships. The examples we use are primarily taken from talent management, because it's easy to think about our own careers or those of our employees. But the tools covered extend to all contexts, and your project is on an idea of your choosing. The class focuses on the use of regressions to think experimentally. To take the class, you should have covered regression analysis in a former class (such as an econometrics course for economics majors) or be comfortable with learning basic math concepts quickly. You also should understand distributions of data (such as the Bell curve, or normal distribution), but this topic is not covered. There are no required proofs or derivations; you've done that as undergraduates. This is about using data: we use cases, examples, Notes written for the class, and a quiz, final exam, and several assignments in which you play with data sets to answer questions. Note that this 4-unit course, if successfully completed, counts for the Data Analysis foundations requirement.
Terms: Win
| Units: 4
Instructors:
Shaw, K. (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.
Terms: Win
| Units: 4
Instructors:
Gorodsky, J. (PI)
;
Patell, J. (PI)
;
Beach, D. (SI)
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Instructors:
Gorodsky, J. (PI)
;
Patell, J. (PI)
;
Beach, D. (SI)
;
Cone, T. (SI)
;
Coulson, S. (SI)
OIT 356: Electronic Business
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. Case studies include Salesforce.com, Apple, Netflix, Evernote, Linden Lab (Second Life), Amazon.com and Zappos. 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.
Terms: Win
| Units: 3
Instructors:
Mendelson, H. (PI)
OIT 367: Analytics 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.
Terms: Win
| Units: 4
Instructors:
Bayati, M. (PI)
OIT 384: Biodesign Innovation: Needs Finding and Concept Creation
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.
Terms: Win
| Units: 4
OIT 558: Designing Large-Scale Nudge Engines
In many of the challenges faced by the modern world, from overcrowded road networks to overstretched healthcare systems, large benefits for society come about from small changes by very many individuals. This course survey the problems and the cost they impose on society. It describes a series of pilot projects which aim to develop principles for inducing small changes in behavior in Societal Networks--transportation networks, wellness programs, recycling systems and, if time permits, energy grids. Students will learn how low-cost sensing and networking technology can be used for sensing individual behavior, and how incentives and social norming can be used to influence the behavior. The effectiveness of this approach in pilots conducted in Bangalore (commuting), Singapore (public transit system), Stanford (congestion and parking), and a wellness program at Accenture-USA will be discussed. Students may experience the incentive platform as participants.nnThis course significantly overlaps with
OIT 258 - Incentive Mechanisms for Societal Networks. If you took this class last year, you may not take
OIT 558.
Terms: Win
| Units: 1
Instructors:
Prabhakar, B. (PI)
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