AA 131: Space Flight
This class is all about how to build a spacecraft. It is designed to introduce undergraduate engineering students to the engineering fundamentals of conceiving, designing, implementing, and operating satellites and other space systems. Topics include orbital dynamics, attitude dynamics, mission design, and subsystem technologies. The space environment and the seven classic spacecraft subsystems  propulsion, attitude control and navigation, structure, thermal, power, telemetry and command, and payload  will be explored in detail. Prerequisites: Freshmanlevel physics, basic calculus and differential equations.
Terms: Spr

Units: 3

UG Reqs: WAYAQR

Grading: Letter (ABCD/NP)
ANTHRO 116: Data Analysis for Quantitative Research (ANTHRO 216)
An introduction to numeric methods in Anthropology and related fields employing the Data Desk statistics package to test hypotheses and to explore data. Examples chosen from the instructor¿s research and other relevant projects. No statistical background is necessary, but a working knowledge of algebra is important. Topics covered include: Frequency Distributions; Measures of Central Tendency, Dispersion, and Variability; Probability and Probability Distributions; Statistical Inference, Comparisons of Sample Means and Standard Deviations; Analysis of Variance; Contingency Tables, Comparisons of Frequencies; Correlation and Regression; Principal Components Analysis; Discriminant Analysis; and Cluster Analysis. Grading based on takehome problem sets.
Terms: Aut

Units: 5

UG Reqs: GER:DBSocSci, WAYAQR

Grading: Letter (ABCD/NP)
Instructors:
Klein, R. (PI)
ANTHRO 130D: Spatial Approaches to Social Science (ANTHRO 230D, POLISCI 241S, URBANST 124)
This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
Terms: Win

Units: 5

UG Reqs: WAYAQR, WAYSI

Grading: Letter or Credit/No Credit
Instructors:
Engel, C. (PI)
;
Rodden, J. (PI)
APPPHYS 61: Science as a Creative Process (BIO 61)
What is the process of science, and why does creativity matter? We'll delve deeply into the applicability of science in addressing a vast range of realworld problems. This course is designed to teach the scientific method as it's actually practiced by working scientists. It will cover how to ask a wellposed question, how to design a good experiment, how to collect and interpret quantitative data, how to recover from error, and how to communicate findings. Facts matter! Course topics will include experimental design, statistics and statistical significance, formulating appropriate controls, modeling, peer review, and more. The course will incorporate a significant handson component featuring device fabrication, testing, and measurement. Among other "Dorm Science" activities, we'll be distributing Arduino microcontroller kits and electronic sensors, then use these items, along with other materials, to complete a variety of group and individual projects outside the classroom. The final course assignment will be to develop and write a scientific grant proposal to test a studentselected myth or scientific controversy. Although helpful, no prior experience with electronics or computer programming is required. Recommended for freshmen.
Terms: Spr

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Block, S. (PI)
;
Stearns, T. (PI)
APPPHYS 189: Physical Analysis of Artworks
Students explore the use of Stanford Nano Shared Facilities (SNSF) for physical analysis of material samples of interest for art conservation, technical art history and archaeology. Weekly SNSF demonstrations will be supplemented by lectures on intellectual context by Stanford faculty/staff and conservators from the Fine Arts Museums of San Francisco (FAMSF). Students will complete the SNSF training sequence for electron microscopy and undertake analysis projects derived from ongoing conservation efforts at FAMSF."
Terms: Win

Units: 3

UG Reqs: WAYAQR, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Mabuchi, H. (PI)
BIO 61: Science as a Creative Process (APPPHYS 61)
What is the process of science, and why does creativity matter? We'll delve deeply into the applicability of science in addressing a vast range of realworld problems. This course is designed to teach the scientific method as it's actually practiced by working scientists. It will cover how to ask a wellposed question, how to design a good experiment, how to collect and interpret quantitative data, how to recover from error, and how to communicate findings. Facts matter! Course topics will include experimental design, statistics and statistical significance, formulating appropriate controls, modeling, peer review, and more. The course will incorporate a significant handson component featuring device fabrication, testing, and measurement. Among other "Dorm Science" activities, we'll be distributing Arduino microcontroller kits and electronic sensors, then use these items, along with other materials, to complete a variety of group and individual projects outside the classroom. The final course assignment will be to develop and write a scientific grant proposal to test a studentselected myth or scientific controversy. Although helpful, no prior experience with electronics or computer programming is required. Recommended for freshmen.
Terms: Spr

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Block, S. (PI)
;
Stearns, T. (PI)
BIO 141: Biostatistics (STATS 141)
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut

Units: 5

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Holmes, S. (PI)
BIO 183: Theoretical Population Genetics (BIO 283)
Models in population genetics and evolution. Selection, random drift, gene linkage, migration, and inbreeding, and their influence on the evolution of gene frequencies and chromosome structure. Models are related to DNA sequence evolution. Prerequisites: calculus and linear algebra, or consent of instructor.
Terms: Win, alternate years, not given next year

Units: 3

UG Reqs: WAYAQR, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Feldman, M. (PI)
BIOE 42: Physical Biology
BIOE 42 is designed to introduce students to general engineering principles that have emerged from theory and experiments in biology. Topics covered will cover the scales from molecules to cells to organisms, including fundamental principles of entropy, diffusion, and continuum mechanics. These topics will link to several biological questions, including DNA organization, ligand binding, cytoskeletal mechanics, and the electromagnetic origin of nerve impulses. In all cases, students will learn to develop toy models that can explain quantitative measurements of the function of biological systems. Prerequisites:
MATH 19, 20, 21
CHEM 31A, B (or 31X),
PHYSICS 41; strongly recommended:
CS 106A,
CME 100 or
MATH 51, and
CME 106; or instructor approval.
Terms: Spr

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Bryant, Z. (PI)
;
Huang, K. (PI)
BIOE 101: Systems Biology (BIOE 210)
Complex biological behaviors through the integration of computational modeling and molecular biology. Topics: reconstructing biological networks from highthroughput data and knowledge bases. Network properties. Computational modeling of network behaviors at the small and large scale. Using model predictions to guide an experimental program. Robustness, noise, and cellular variation. Prerequisites:
CME 102;
BIO 82,
BIO 84; or consent of instructor.
Terms: Aut

Units: 3

UG Reqs: WAYAQR

Grading: Letter (ABCD/NP)
Instructors:
Covert, M. (PI)
Filter Results: