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11 - 20 of 162 results for: MS&E

MS&E 120: Introduction to Probability

Probability is the foundation behind many important disciplines including statistics, machine learning, risk analysis, stochastic modeling and optimization. This course provides an in-depth undergraduate-level introduction to fundamental ideas and tools of probability. Topics include: the foundations (sample spaces, random variables, probability distributions, conditioning, independence, expectation, variance), a systematic study of the most important univariate and multivariate distributions (Normal, Multivariate Normal, Binomial, Poisson, etc...), as well as a peek at some limit theorems (basic law of large numbers and central limit theorem) and, time permitting, some elementary markov chain theory. Prerequisite: CME 100 or MATH 51.
Terms: Aut | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

MS&E 120ACE: Introduction to Probability, ACE

Students attend MS&E 120 lectures with additional recitation sessions; two to four hours per week. Enrollment by permission only. Prerequisite: students should submit application for enrollment at: https://engineering.stanford.edu/students/programs/engineering-diversity-programs/additional-calculus-engineers before study list deadline. It is recommended students enroll in the regular section of MS&E 120 prior to submitting application. Corequisite: MS&E 120.
Terms: Aut | Units: 1
Instructors: Ahmed, R. (PI)

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: 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. Prerequisites: 111, 120, and CS 106A.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci

MS&E 134: Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, POLISCI 154, PUBLPOL 155, SOC 127)

Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use more »
Introduces students to the interdisciplinary intersection of data science and the social sciences through an in-depth examination of contemporary social problems. Provides a foundational skill set for solving social problems with data including quantitative analysis, modeling approaches from the social sciences and engineering, and coding skills for working directly with big data. Students will also consider the ethical dimensions of working with data and learn strategies for translating quantitative results into actionable policies and recommendations. Lectures will introduce students to the methods of data science and social science and apply these frameworks to critical 21st century challenges, including education & inequality, political polarization, and health equity & algorithmic design in the fall quarter, and social media, climate change, and school choice & segregation in the spring quarter. In-class exercises and problem sets will provide students with the opportunity to use real-world datasets to discover meaningful insights for policymakers and communities. This course is the required gateway course for the new major in Data Science & Social Systems. Preference given to Data Science & Social Systems B.A. majors and prospective majors. Course material and presentation will be at an introductory level. Enrollment and participation in one discussion section is required. Sign up for the discussion section will occur on Canvas at the start of the quarter. Prerequisites: CS106A (required), DATASCI 112 (recommended as pre or corequisite). Limited enrollment. Please complete the interest form here: https://forms.gle/8ui9RPgzxjGxJ9k29. A permission code will be given to admitted students to register for the class.
Terms: Aut, Spr | Units: 5 | UG Reqs: WAY-SI, WAY-AQR

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: Win | 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: Spr, Sum | Units: 3

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. Recommended: 111 or 211, and ECON 50.
Terms: Win | Units: 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. Prerequisite: MS&E 120 or equivalent.
Terms: Win | Units: 4
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