Master of Science

The MS degree program represents substantial progress in the major field beyond the bachelor’s degree.

Students must complete at least 45 units at Stanford in courses numbered 100 and above. See Graduate Degrees for university requirements for the master’s degree. Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this Bulletin.

MS&E master’s students complete breadth as well as depth in MS&E; depth possibilities include:

1. Data, AI, and Decisions: Courses in Data, AI, and Decisions prepare students in the fundamentals of data and decision sciences, machine learning, and artificial intelligence that are transforming operational and strategic decision making in information technology, finance, healthcare, services, manufacturing, and government.  The program combines rigorous technical foundations with emerging modern applications. 

2. Financial Analytics: Courses in Financial Analytics prepare students for careers requiring analytical rigor and the ability to innovate around market challenges. Example career paths include financial services, risk management, investment management, financial technology and data processing, financial regulation and policy, exchanges and clearing houses, and auditing and compliance. Courses combine the in-depth study of quantitative techniques with practical, hands-on business problem-solving. Students learn to use mathematical models and quantitative tools to solve complex problems in finance practice, exploiting the intellectual ties between finance, operations research, computer science, and engineering. It offers a high level of flexibility and a range of elective courses that allow students to tailor the program to their specific career goals. Required courses immerse students in quantitative methods and deepen their understanding of finance fundamentals. Projects courses feature practical, data-driven team projects and case studies, fostering group learning and peer interaction. 

3. Technology and Engineering Management: Courses in Technology and Engineering Management prepare students for careers including product and project management, management consulting, and entrepreneurship. They acquire skills to manage technical organizations, foster innovation, and deal with rapidly evolving technologies and dynamic markets. Specialized coursework is flexible, allowing students to explore and gain depth and understanding of technical organizations to develop a culture of successful innovation and entrepreneurship, along with decision-making methods under uncertainty, financial analysis, and strategic planning.

4. Technology Policy: Courses in Technology Policy focus on the design and analysis of public policies and corporate strategies, especially those with technology-based issues. It features a grounding in microeconomics and modeling approaches. Courses with a policy focus include such topics such as national security, energy and environment, and health care.

Students may also self-design a depth area; possibilities for a self-designed depth area include:

1. Computational Social Science: Courses in Computational Social Science teach students how to apply rigorous statistical and computational methods to address problems in economics, sociology, political science, and beyond. Students are prepared for diverse career paths in data science, information technology, and policy analysis. Coursework covers fundamental statistical concepts, large-scale computation, and network analysis. Students can explore topics such as experimental design, algorithmic economics, and machine learning through electives.

2. Decision Analysis and Risk Analysis: Courses in Decision and Risk Analysis prepare students for careers including management consulting, policy analysis, and risk management, applying engineering systems analysis to tackle complex economic and technical management problems in the private and public sectors. They acquire the skills to identify and develop opportunities in uncertain situations while recognizing and hedging the downside risks. Specialized coursework includes the mathematical foundations for modeling in dynamic, uncertain environments to value and manage uncertain opportunities and risks, applications to public policy, and a chance to work on a client project under faculty guidance.  Recommended courses include MS&E 250A, 250B, 252, and 355.

3. Energy and Environment: Courses in Energy and Environment are designed for students interested in energy and environmental issues from the perspectives of public policy, non-governmental organizations, or corporations. Recommended courses include MS&E 241, 243, and 394, CEE 207A, 273S, and ENERGY 201A, 201B, 201C, 223, 240, 241, 281 and 291.

4. Health Systems Modeling: Courses in Health Systems Modeling are designed for students interested in health care operations and policy. Courses emphasize applying mathematical and economic analysis to problems in public health policy and the design and operation of health care services. Recommended courses include MS&E 263, 292, and 463, and HRP 263 and 392.