ENGR 10: Introduction to Engineering Analysis
Integrated approach to the fundamental scientific principles that are the cornerstones of engineering analysis: conservation of mass, atomic species, charge, momentum, angular momentum, energy, production of entropy expressed in the form of balance equations on carefully defined systems, and incorporating simple physical models. Emphasis is on setting up analysis problems arising in engineering. Topics: simple analytical solutions, numerical solutions of linear algebraic equations, and laboratory experiences. Provides the foundation and tools for subsequent engineering courses. Prerequisite: AP Physics and AP Calculus or equivalent.
Terms: Spr, Sum

Units: 4

UG Reqs: GER:DBEngrAppSci

Grading: Letter (ABCD/NP)
Instructors:
Cappelli, M. (PI)
;
Lee, R. (TA)
ENGR 40M: An Intro to Making: What is EE
Is a handson class where students learn to make stuff. Through the process of building, you are introduced to the basic areas of EE. Students build a "useless box" and learn about circuits, feedback, and programming hardware, a light display for your desk and bike and learn about coding, transforms, and LEDs, a solar charger and an EKG machine and learn about power, noise, feedback, more circuits, and safety. And you get to keep the toys you build. Prerequisite:
CS 106A.
Terms: Aut, Spr, Sum

Units: 35

UG Reqs: GER:DBEngrAppSci, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Horowitz, M. (PI)
;
Howe, R. (PI)
;
Kananian, S. (PI)
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Instructors:
Horowitz, M. (PI)
;
Howe, R. (PI)
;
Kananian, S. (PI)
;
Plummer, J. (PI)
;
Botbol Ponte, E. (TA)
;
Casino, A. (TA)
;
Chavez, K. (TA)
;
Datta, A. (TA)
;
Diamandis, T. (TA)
;
Gallo Dagir, R. (TA)
;
Greene, B. (TA)
;
Guerrero, O. (TA)
;
Herrera, L. (TA)
;
Kohli, A. (TA)
;
Lavengco, M. (TA)
;
Lee, C. (TA)
;
Liu, C. (TA)
;
Maldonado, S. (TA)
;
Mendoza, D. (TA)
;
Meza, M. (TA)
;
Padilla, M. (TA)
;
Pena, J. (TA)
;
Ramirez, W. (TA)
;
Staffa, N. (TA)
;
Stein, L. (TA)
ENGR 60: Engineering Economics and Sustainability (CEE 146S)
Engineering Economics is a subset of the field of economics that draws upon the logic of economics, but adds that analytical power of mathematics and statistics. The concepts developed in this course are broadly applicable to many professional and personal decisions, including making purchasing decisions, deciding between project alternatives, evaluating different processes, and balancing environmental and social costs against economic costs. The concepts taught in this course will be increasingly valuable as students climb the carrier ladder in private industry, a nongovernmental organization, a public agency, or in founding their own startup. Eventually, the ability to make informed decisions that are based in fundamental analysis of alternatives is a part of every career. As such, this course is recommended for engineering and nonengineering students alike. This course is taught exclusively online in every quarter it is offered. (Prerequisites:
MATH 19 or 20 or approved equivalent.)
Terms: Aut, Win, Spr, Sum

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Lepech, M. (PI)
ENGR 70A: Programming Methodology (CS 106A)
Introduction to the engineering of computer applications emphasizing modern software engineering principles: objectoriented design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the builtin facilities of respective languages. No prior programming experience required. Summer quarter enrollment is limited. Alternative versions of CS106A are available which cover most of the same material but in different programming languages: Java [Fall, Win, Spr, or Sum qtr enroll in CS106A Section 1] Javascript [Fall qtr enroll in
CS 106A Section 2] Python [Winter or Spring qtr enroll in
CS 106A Section 3]
Terms: Aut, Win, Spr, Sum

Units: 35

UG Reqs: GER:DBEngrAppSci, WAYFR

Grading: Letter or Credit/No Credit
ENGR 70B: Programming Abstractions (CS 106B)
Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Objectoriented programming, fundamental data structures (such as stacks, queues, sets) and datadirected design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities. Prerequisite: 106A or equivalent. Summer quarter enrollment is limited.
Terms: Aut, Win, Spr, Sum

Units: 35

UG Reqs: GER:DBEngrAppSci, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Gregg, C. (PI)
;
Lee, C. (PI)
;
Stepp, M. (PI)
;
Taylor, A. (PI)
;
Taylor, A. (TA)
;
Troccoli, N. (TA)
ENGR 90: Environmental Science and Technology (CEE 70)
Introduction to environmental quality and the technical background necessary for understanding environmental issues, controlling environmental degradation, and preserving air and water quality. Material balance concepts for tracking substances in the environmental and engineering systems.
Terms: Win, Sum

Units: 3

UG Reqs: GER:DBEngrAppSci, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Kopperud, R. (PI)
ENGR 118: CrossCultural Design for Service
Students spend the summer in China working collaboratively to use design thinking for a project in the countryside. Students learn and apply the principles of design innovation including user research, ideation, prototyping, storytelling and more in a cross cultural setting to design a product or service that will benefit Chinese villagers. Students should be prepared to work independently in a developing region of China, to deal with persistent ambiguity, and to work with a crosscultural, diverse team of students on their projects. Applications for Summer 2012 were due in March.
Terms: Sum

Units: 3

Grading: Letter (ABCD/NP)
ENGR 145: Technology Entrepreneurship
How do you create a successful startup? What is entrepreneurial leadership in a large firm? What are the differences between an idea and true opportunity? How does an entrepreneur form a team and gather the resources necessary to create a great enterprise? Mentorguided project focused on developing students' startup ideas, immersion in nuances of innovation and early stage entrepreneurship, case studies, research on the entrepreneurial process, and the opportunity to network with Silicon Valley's top entrepreneurs and venture capitalists. For undergraduates of all majors who seek to understand the formation and growth of highimpact startups in areas such as information, energy, medical and consumer technologies. No prerequisites. Limited enrollment.
Terms: Aut, Win, Sum

Units: 4

UG Reqs: GER:DBSocSci

Grading: Letter (ABCD/NP)
Instructors:
Byers, T. (PI)
;
Eesley, C. (PI)
;
Hwang, R. (PI)
;
Ayerdi, O. (TA)
;
Hejrati, Z. (TA)
;
Marrec, M. (TA)
;
Tidhar, R. (TA)
ENGR 155A: Ordinary Differential Equations for Engineers (CME 102)
Analytical and numerical methods for solving ordinary differential equations arising in engineering applications: Solution of initial and boundary value problems, series solutions, Laplace transforms, and nonlinear equations; numerical methods for solving ordinary differential equations, accuracy of numerical methods, linear stability theory, finite differences. Introduction to MATLAB programming as a basic tool kit for computations. Problems from various engineering fields. Prerequisite: 10 units of AP credit (Calc BC with 5, or Calc AB with 5 or placing out of the single variable math placement test:
https://exploredegreesnextyear.stanford.edu/undergraduatedegreesandprograms/#aptextt),), or
Math 1921. Recommended:
CME100.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Le, H. (PI)
;
Moin, P. (PI)
;
Ahn, S. (TA)
;
Amidi, S. (TA)
;
DePaul, G. (TA)
;
FournierBidoz, E. (TA)
;
Gallegos Ortega, D. (TA)
;
Infanger, A. (TA)
;
Lachevre, P. (TA)
;
Patel, H. (TA)
;
Wang, R. (TA)
;
Westhoff, P. (TA)
;
Wu, H. (TA)
ENGR 155C: Introduction to Probability and Statistics for Engineers (CME 106)
Probability: random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, nonparametric tests, regression and correlation analyses; applications in engineering, industrial manufacturing, medicine, biology, and other fields. Prerequisite:
CME 100/ENGR154 or
MATH 51 or 52.
Terms: Win, Sum

Units: 4

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Khayms, V. (PI)
;
Amidi, S. (TA)
;
Chhor, G. (TA)
;
Chu, C. (TA)
;
Lakshman, V. (TA)
;
Sagastuy Brena, J. (TA)
;
Wu, Y. (TA)
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