GEOPHYS 160: D^3: Disasters, Decisions, Development
This class connects the science behind natural disasters with the realworld constraints of disaster management and development. In each iteration of this class we will focus on a specific, disasterprone location as case study. By collaborating with local stakeholders we will explore how science and engineering can make a make a difference in reducing disaster risk in the future. Offered every other year.
Terms: not given this year

Units: 35

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
GS 1: Introduction to Geology (EARTHSYS 11)
Why are earthquakes, volcanoes, and natural resources located at specific spots on the Earth surface? Why are there rolling hills to the west behind Stanford, and soaring granite walls to the east in Yosemite? What was the Earth like in the past, and what will it be like in the future? Lectures, handson laboratories, inclass activities, and one field trip will help you see the Earth through the eyes of a geologist. Topics include plate tectonics, the cycling and formation of different types of rocks, and how geologists use rocks to understand Earth's history.
Terms: Win

Units: 5

UG Reqs: GER: DBNatSci, WAYAQR, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Sperling, E. (PI)
GS 42: Landscapes and Tectonics of the San Francisco Bay Area (EARTH 42)
Active faulting and erosion in the Bay Area, and its effects upon landscapes. Earth science concepts and skills through investigation of the valley, mountain, and coastal areas around Stanford. Faulting associated with the San Andreas Fault, coastal processes along the San Mateo coast, uplift of the mountains by plate tectonic processes, and landsliding in urban and mountainous areas. Field excursions; student projects.
Terms: Aut

Units: 4

UG Reqs: WAYAQR, WAYSMA

Grading: Letter (ABCD/NP)
Instructors:
Hilley, G. (PI)
HUMBIO 51: Big Data for Biologists  Decoding Genomic Function
Biology and medicine are becoming increasingly dataintensive fields. This course is designed to introduce students interested in human biology and related fields to methods for working with large biological datasets. There will be inclass activities analyzing real data that have revealed insights about the role of the genome and epigenome in health and disease. For example, we will explore data from largescale gene expression and chromatin state studies. The course will provide an introduction to the relevant topics in biology and to fundamental computational skills such as editing text files, formatting and storing data, visualizing data and writing data analysis scripts. Students will become familiar with both UNIX and Python. This course is designed at the introductory level. Previous universitylevel courses in biology and programming experience are not required.
Terms: Aut

Units: 3

UG Reqs: WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Kundaje, A. (PI)
;
Salmeen, A. (PI)
HUMBIO 85A: Essential Statistics for Human Biology (BIO 108)
Introduction to statistical concepts and methods that are essential to the study of questions in biology, environment, health and related areas. The course will teach and use the computer language R and Python (you learn both, choose one). Topics include distributions, probabilities, likelihood, linear models; illustrations will be based on recent research.
Terms: not given this year

Units: 4

UG Reqs: WAYAQR

Grading: Letter (ABCD/NP)
HUMBIO 88: Introduction to Statistics for the Health Sciences
Students will learn the statistical tools used to describe and analyze data in the fields of medicine and epidemiology. This very applied course will rely on current research questions and publicly available data. Students will gain proficiency with Stata to do basic analyses of healthrelated data, including linear and logistic regression, and will become sophisticated consumers of healthrelated statistical results.
Terms: Win

Units: 4

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter (ABCD/NP)
Instructors:
Kurina, L. (PI)
;
Klein, L. (TA)
HUMBIO 89: Statistics in the Health Sciences
This course aims to provide a firm grounding in the foundations of probability and statistics, with a focus on analyzing data from the health sciences. Students will learn how to read, interpret, and critically evaluate the statistics in medical and biological studies. The course also prepares students to be able to analyze their own data, guiding them on how to choose the correct statistical test, avoid common statistical pitfalls, and perform basic functions in R deducer.
Terms: Aut, Win

Units: 3

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Sainani, K. (PI)
;
Serghiou, S. (PI)
HUMBIO 154A: Engineering Better Health Systems: modeling for public health (HRP 234, MED 254)
This course teaches engineering, operations research and modeling techniques to improve public health programs and systems. Students will engage in indepth study of disease detection and control strategies from a "systems science" perspective, which involves the use of common engineering, operations research, and mathematical modeling techniques such as optimization, queuing theory, Markov and KermackMcKendrick models, and microsimulation. Lectures and problem sets will focus on applying these techniques to classical public health dilemmas such as how to optimize screening programs, reduce waiting times for healthcare services, solve resource allocation problems, and compare macroscale disease control strategies that cannot be easily evaluated through randomized trials. Readings will complement the lectures and problem sets by offering critical perspectives from the public health history, sociology, and epidemiology. Indepth case studies from nongovernmental organizations, departm
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This course teaches engineering, operations research and modeling techniques to improve public health programs and systems. Students will engage in indepth study of disease detection and control strategies from a "systems science" perspective, which involves the use of common engineering, operations research, and mathematical modeling techniques such as optimization, queuing theory, Markov and KermackMcKendrick models, and microsimulation. Lectures and problem sets will focus on applying these techniques to classical public health dilemmas such as how to optimize screening programs, reduce waiting times for healthcare services, solve resource allocation problems, and compare macroscale disease control strategies that cannot be easily evaluated through randomized trials. Readings will complement the lectures and problem sets by offering critical perspectives from the public health history, sociology, and epidemiology. Indepth case studies from nongovernmental organizations, departments of public health, and international agencies will drive the course. Prerequisites: A course in introductory statistics, and a course in multivariable calculus including ordinarily differential equations. Open to upperdivision undergraduate students and graduate students. Human Biology majors enroll in
HUMBIO 154A.
Terms: Aut

Units: 4

UG Reqs: WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Basu, S. (PI)
HUMBIO 154C: Cancer Epidemiology
Clinical epidemiological methods relevant to human research in cancer will be the focus. The concepts of risk; case control, cohort, and crosssectional studies; clinical trials; bias; confounding; interaction; screening; and causal inference will be introduced and applied. Social, political, economic, and ethical controversies surrounding cancer screening, prevention, and research will be considered. Human Biology 154 courses can be taken separately or as a series. Prerequisite: Human Biology core or equivalent, or instructor consent.
Terms: Win

Units: 4

UG Reqs: WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Fisher, P. (PI)
MATH 114: Introduction to Scientific Computing (CME 108)
Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floatingpoint arithmetic, nonlinear equations, numerical solution of systems of algebraic equations, banded matrices, least squares, unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, numerical stability for time dependent problems and stiffness. Implementation of numerical methods in MATLAB programming assignments. Prerequisites:
MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of
CS 106A or higher).
Terms: Win, Sum

Units: 3

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Dunham, E. (PI)
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