Results for OIT

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.

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.

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.

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.

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.

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.

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.

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.

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/.

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.

This course offers an overview of information technologies for enterprises and supply chain management. The course has two key components - a series of guest speakers and a set of readings. Students are expected to have read the assigned note on related technologies before class, and prepare to discuss technologies with the guest speaker in class. We will not discuss the technology per se in class, so students who enroll are expected to have some exposure to technologies in order to digest the materials on their own. The main topics of technologies are: DBMS (Database Management System), ERP (Enterprise Resource Planning), EAI (Enterprise Application Interface), data mining, Big Data, platform-based business model, cloud computing, RFID/NFC, mobile technologies, and mobile payment. In particular, students are encouraged to think hard about potential new businesses around the technology and discuss them in class.

This course focuses on studying a broad set of econometric methods to conduct empirical research in Operations Management and related fields in Management Science. The course complements formal econometrics and statistics classes by focusing on the application of different econometric methods and identification strategies to research problems that are relevant in different areas within Operations Management, including Supply Chain Management, Service Operations, Healthcare and Retail. Although statistics/econometrics classes provide a rigorous revision of the methods, they put less emphasis on how to apply these methods in different settings. This course aims to fill that gap by providing a problem-oriented approach, where the focus is on identifying empirical questions relevant to Operations Management and choosing an appropriate empirical strategy to address them. The course has a seminar format combining paper presentations by students, computer assignments and a short research proposal.

This course provides an overview of research in supply chain management (SCM). It has three parts. The first part reviews basic tools of SCM research through selected readings in economics, IT and operations research. The second part reviews the literature in SCM, covering topics such as inventory models, information sharing, information distortion, contract design, value of integration, performance measurement, risk management, and the use of markets for procurement. The last part is devoted to recent advances in SCM research.

In most real-world problems, decision-makers often face uncertainty with respect to the underlying modelsnnthat drive the rewards/costs associated with potential strategies. The uncertainty in the problem can bennmodeled in a number of ways (e.g., a probability distribution over some parameters or an uncertainty setnnfor some variables) and a selection of an appropriate framework depends on various considerations rangingnnfrom the availability of historical data (or lack thereof) to the robustness of resulting strategies or thenntractability of the formulation. In addition, once a framework is selected, further challenges often arise whennnconsidering dynamic settings, in which the level of uncertainty may be updated from one period to another.nnThe high-level objectives of this course are:nn1. to introduce various frameworks for decision-making under model uncertaintynn2. to introduce tools to solve such problems, including ones to develop optimal or near-optimal learningnnstrategiesnn3. to discuss the various tradeoffs that arise such as tractability vs. performance, exploration vs. exploitation, and remembering vs. forgettingnn4. to explore research papers that demonstrate applications of discussed methods and models to variousnnproblems areas such as dynamic pricing, revenue management, inventory management, and assortmentnnselection

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.

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.

Doctoral Practicum in Teaching

Doctoral Practicum in Research