MS&E 121: Introduction to Stochastic Modeling
Stochastic processes and models in operations research. Discrete and continuous time parameter Markov chains. Queuing theory, inventory theory, simulation. Prerequisite: 120 or equivalent.
Terms: Win
| Units: 4
| UG Reqs: GER:DB-EngrAppSci
MS&E 125: Introduction to Applied Statistics
An increasing amount of data is now generated in a variety of disciplines, ranging from finance and economics, to the natural and social sciences. Making use of this information, however, requires both statistical tools and an understanding of how the substantive scientific questions should drive the analysis. In this hands-on course, we learn to explore and analyze real-world datasets. We cover techniques for summarizing and describing data, methods for statistical inference, and principles for effectively communicating results. Prerequisite:
CME 100 or
MATH 51. Recommended: 120,
CS 106A, or equivalents.
Terms: Spr
| Units: 4
MS&E 130: Information Networks and Services
Architecture of the Internet and performance engineering of computer systems and networks. Switching, routing and shortest path algorithms. Congestion management and queueing networks. Peer-to-peer networking. Wireless and mobile networking. Information service engineering and management. Search engines and recommendation systems. Reputation systems and social networking technologies. Security and trust. Information markets. Select special topics and case studies. Prerequisite:
CME 100 or
MATH 51. Recommended: 111, 120, and
CS 106A.
Terms: Win
| Units: 3
| UG Reqs: GER:DB-EngrAppSci
Instructors:
Bambos, N. (PI)
;
Lotidis, K. (TA)
MS&E 134: Data Science for Social Impact (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, POLISCI 154, PUBLPOL 155, SOC 127)
You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem s
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You have some experience coding in R or Python. You've taken a class or two in basic stats or data science. But what's next? How can you use data science skills to make the world a better place? If you're asking those questions, then "Data Science for Social Impact" is for you. In this class, you'll work in four areas where data are being used to make the world better: health care, education, detecting discrimination, and clean energy technologies. You'll work with data from hospitals, schools, police departments, and electric utilities. You'll apply causal inference, prediction, and optimization techniques to help businesses, governments, and other organizations make better decisions. You'll see the challenges that arise when analyzing real data (for example, when some data are missing, or when the randomized experiment gets implemented wrong). You'll get ideas for an impactful and meaningful senior thesis, summer internship, and future career. Concretely, you'll have weekly problem sets involving data analysis in R or python. You'll learn and apply techniques like fixed effects regression, difference-in-differences, instrumental variables, regularized regression, random forests, causal forests, and optimization. Class sessions will feature active learning, discussions, and small-group case studies. You should only enroll if you expect to attend regularly and complete the problem sets on time. Prerequisites (recommended): Experience programming in R or python, or willingness to learn very quickly on your own. A basic statistics or data science course, such as any of the following:
DATASCI 112,
ECON 102 or 108,
CS 129,
EARTHSYS 140,
HUMBIO 88,
POLISCI 150A,
STATS 60,
SOC 180B, or MS&E 125.
Terms: Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
Instructors:
Hwang, J. (PI)
;
Hwang, S. (PI)
;
Nobles, M. (PI)
;
Lyu, D. (TA)
;
Scott-Hearn, N. (TA)
MS&E 135: Networks
This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include: an introduction to graph theory and graph concepts; social networks; information networks; the aggregate behavior of markets and crowds; network dynamics; information diffusion; the implications of popular concepts such as "six degrees of separation", the "friendship paradox", and the "wisdom of crowds".
Terms: Spr
| Units: 3
MS&E 140: Accounting for Managers and Entrepreneurs (MS&E 240)
Non-majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making. Designed for the user of accounting information and not as an introduction to a professional accounting career.
Terms: Aut, Spr, Sum
| Units: 3
Instructors:
Lord, J. (PI)
;
DiCicco, T. (TA)
;
Holmstrom, J. (TA)
;
Lapointe, R. (TA)
;
Lyu, P. (TA)
;
Massein, R. (TA)
;
Parikh, H. (TA)
;
Xie, B. (TA)
;
Zhuang, Y. (TA)
MS&E 141: Economic Analysis (MS&E 241)
Principal methods of economic analysis of the production activities of firms, including production technologies, cost and profit, and perfect and imperfect competition; individual choice, including preferences and demand; and the market-based system, including price formation, efficiency, and welfare. Practical applications of the methods presented. Prerequisite: microeconomics and welfare economics. Recommended: 111 or 211, and
ECON 50.
Terms: Win
| Units: 3-4
MS&E 145: Introduction to Finance and Investment
Introduction to modern quantitative finance and investments. The course focuses on the basic principles underlying financial decision making which are applicable to all forms of investment: stocks, bonds, real estate, corporate finance, etc., and how they are applied in practice. Topics: interest rates; evaluating investments: present value and internal rate of return; fixed-income markets: bonds, yield, duration, portfolio immunization; term structure of interest rates; measuring risk: volatility, value at risk, expected shortfall; designing optimal security portfolios; the capital asset pricing model. Group projects involving financial market data. No prior knowledge of finance required. Hard prerequisites: MS&E 120 or 220, or
CS 109, or
STATS 116. MS&E 111 is strongly suggested. Working knowledge of Excel, or Python, or R, or Matlab, or Mathematica is also required.
Terms: Win
| Units: 4
Instructors:
Giesecke, K. (PI)
;
Rebei, A. (TA)
MS&E 146: Corporate Financial Management (MS&E 249)
Key functions of finance in both large and small companies, and the core concepts and tools that provide their foundation. Identifying promising business opportunities. The role of finance in business planning, and in quantifying and managing uncertainty and risk. Determining what to do yourself and what to contract for with partners and suppliers. Valuation, raising money, and optimizing capital structure. Designing performance metrics to align and effectively measure the activities of functional groups and individuals within the firm.
Terms: Win
| Units: 3-4
MS&E 148: Ethics of Finance
Explores the ethical reasoning needed to make banking, insurance and financial services safer, fairer and more positively impactful. Weighs tradeoffs in how money is created, privileging some, under-privileging others, using market mechanisms for transforming and trading financial risk, return, maturity and asset types. Technology is changing banks, financial markets, insurance and money. Like technology for medicine, finance is being rebuilt as machine learned code, algorithmic investment rules and regulatory monitoring. Risk models can be built to detect fraud and ethical lapses, or to open doors for them. Investment valuation models can optimize short term or long term returns, by optimizing or ignoring environmental and social impacts. Transparency or opacity can be the norm. Transforming finance through engineering requires finding, applying and evolving codes of professional conduct to make sure that engineers use their skills within legal and ethical norms. Daily, financial engi
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Explores the ethical reasoning needed to make banking, insurance and financial services safer, fairer and more positively impactful. Weighs tradeoffs in how money is created, privileging some, under-privileging others, using market mechanisms for transforming and trading financial risk, return, maturity and asset types. Technology is changing banks, financial markets, insurance and money. Like technology for medicine, finance is being rebuilt as machine learned code, algorithmic investment rules and regulatory monitoring. Risk models can be built to detect fraud and ethical lapses, or to open doors for them. Investment valuation models can optimize short term or long term returns, by optimizing or ignoring environmental and social impacts. Transparency or opacity can be the norm. Transforming finance through engineering requires finding, applying and evolving codes of professional conduct to make sure that engineers use their skills within legal and ethical norms. Daily, financial engineers focus on two horizons: on the floor, we stand on the bare minimum standards of conduct, and on the ceiling, we aim for higher ethical goals that generate discoveries celebrated though individual fulfillment and TED Talks. Stanford engineers, computer scientists, data scientists, mathematicians and other professionals are building systems for lending, investment and portfolio management decisions that determine future economic and social growth. This course uses the case method to preview intersecting codes of conduct, legal hurdles and ethical impact opportunities, and creates as a safe academic setting for seeing career-limiting ethical stop signs (red lights) and previewing "what's my life all about" events, as unexpected threats or surprising ah-ha moments. Guest speakers will highlight real life situations, lawsuits and other events where ethics of financial engineering was a predominant theme, stumbling block or humanitarian opportunity.
Last offered: Autumn 2024
| Units: 1