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AA 100: Introduction to Aeronautics and Astronautics

This class introduces the basics of aeronautics and astronautics through applied physics, hands-on activities, and real world examples. The principles of fluid flow, flight, and propulsion for aircraft will be illustrated, including the creation of lift and drag, aerodynamic performance including takeoff, climb, range, and landing. The principles of orbits, maneuvers, space environment, and propulsion for spacecraft will be illustrated. Students will be exposed to the history and challenges of aeronautics and astronautics.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

AA 108N: Surviving Space

Space is dangerous. Anything we put into orbit has to survive the intense forces experienced during launch, extreme temperature changes, impacts by cosmic rays and energetic protons and electrons, as well as hits by human-made orbital debris and meteoroids. If we venture beyond Earth's sphere of influence, we must also then endure the extreme plasma environment without the protection of our magnetic field. With all of these potential hazards, it is remarkable that our space program has experienced so few catastrophic failures. In this seminar, students will learn how engineers design and test spacecraft to ensure survivability in this harsh space environment. We will explore three different space environment scenarios, including a small satellite that must survive in Low Earth Orbit (LEO), a large spacecraft headed to rendezvous with an asteroid, and a human spaceflight mission to Mars.
Last offered: Spring 2021 | Units: 3 | UG Reqs: WAY-AQR

AA 109Q: Aerodynamics of Race Cars

Almost as soon as cars had been invented, races of various kinds were organized. In all its forms (open-wheel, touring car, sports car, production-car, one-make, stock car, etc.), car racing is today a very popular sport with a huge media coverage and significant commercial sponsorships. More importantly, it is a proving ground for new technologies and a battlefield for the giants of the automotive industry. While race car performance depends on elements such as engine power, chassis design, tire adhesion and of course, the driver, aerodynamics probably plays the most vital role in determining the performance and efficiency of a race car. Front and/or rear wings are visible on many of them. During this seminar, you will learn about many other critical components of a race car including diffusers and add-ons such as vortex generators and spoilers. You will also discover that due to the competitive nature of this sport and its associated short design cycles, engineering decisions about a race car must rely on combined information from track, wind tunnel, and numerical computations. It is clear that airplanes fly on wings. However, when you have completed this seminar, you will be able to understand that cars fly on their tires. You will also be able to appreciate that aerodynamics is important not only for drag reduction, but also for increasing cornering speeds and lateral stability. You will be able to correlate between a race car shape and the aerodynamics effects intended for influencing performance. And if you have been a fan of the Ferrari 458 Italia, you will be able to figure out what that black moustache in the front of the car was for.
Last offered: Spring 2022 | Units: 3 | UG Reqs: WAY-AQR

AA 114Q: Large Spacecraft Structures

In space, large structures are often advantageous - large solar arrays are required for collecting solar power and allowing spacecraft to operate in deep space, large diameter telescopes allow us to explore the origins of our universe, and large antennas allow us to track climate change and get large amounts of data back down to Earth. However, our ability to get large structures into space is limited by the size of modern rocket fairings, causing large space structures to be designed very differently from those on Earth. This seminar focuses on the design principles used by aerospace engineers to realize large space structures. Over the quarter, we will discuss techniques for deployable space structures folded on the ground and unfolded in orbit including origami, foldable thin structures, and inflatables. The seminar will also introduce students to current developments in space structures such as on-orbit assembly, in-space manufacturing, and reconfigurable space structures. We will examine the materials used in these structures, overview mathematical principles used for their design, and learn from past failures of deployable structures. The seminar will allow students to delve deeper into the concepts with hands-on experimentation, analysis of existing space structures (ex. James Webb, the ISS solar arrays, and CubeSat missions), and will allow students to practice written and oral communication skills.By the end of the course students will be able to:Explain the need for large space structures.Identify and compare the engineering approaches for the realization of large space structures.Analyze the challenges associated with large space structures.Design space structures using simple numerical models.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR
Instructors: ; Sakovsky, M. (PI)

AA 115Q: The Global Positioning System: Where on Earth are We, and What Time is It?

Preference to freshmen. Why people want to know where they are: answers include cross-Pacific trips of Polynesians, missile guidance, and distraught callers. How people determine where they are: navigation technology from dead-reckoning, sextants, and satellite navigation (GPS). Hands-on experience. How GPS works; when it does not work; possibilities for improving performance.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR
Instructors: ; Lo, S. (PI)

AA 116Q: Electric Automobiles and Aircraft

Transportation accounts for nearly one-third of American energy use and greenhouse gas emissions and three-quarters of American oil consumption. It has crucial impacts on climate change, air pollution, resource depletion, and national security. Students wishing to address these issues reconsider how we move, finding sustainable transportation solutions. An introduction to the issue, covering the past and present of transportation and its impacts; examining alternative fuel proposals; and digging deeper into the most promising option: battery electric vehicles. Energy requirements of air, ground, and maritime transportation; design of electric motors, power control systems, drive trains, and batteries; and technologies for generating renewable energy. Two opportunities for hands-on experiences with electric cars. Prerequisites: Introduction to calculus and Physics AP or elementary mechanics.
Last offered: Autumn 2016 | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

AA 118N: How to Design a Space Mission: from Concept to Execution

Space exploration is truly fascinating. From the space race led by governments as an outgrowth of the Cold War to the new era of space commercialization led by private companies and startups, more than 50 years have passed, characterized by great leaps forward and discoveries. We will learn how space missions are designed, from concept to execution, based on the professional experience of the lecturer and numerous examples of spacecraft, including unique hardware demonstrations by startups of the Silicon Valley. We will study the essentials of systems engineering as applicable to a variety of mission types, for communication, navigation, science, commercial, and military applications. We will explore the various elements of a space mission, including the spacecraft, ground, and launch segments with their functionalities. Special emphasis will be given to the design cycle, to understand how spacecraft are born, from the stakeholders' needs, through analysis, synthesis, all the way to their integration and validation. We will compare the current designs with those employed in the early days of the space age, and show the importance of economics in the development of spacecraft. Finally, we will brainstorm startup ideas and apply the concepts learned to a notional space mission design as a team.
Last offered: Autumn 2019 | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

AA 119N: 3D Printed Aerospace Structures

The demand for rapid prototyping of lightweight, complex, and low-cost structures has led the aerospace industry to leverage three-dimensional (3D) printing as a manufacturing technology. For example, the manufacture of aircraft engine components, unmanned aerial vehicle (UAV) wings, CubeSat parts, and satellite sub-systems have recently been realized with 3D printing and other additive manufacturing techniques. In this freshman seminar, a survey of state-of-the-art 3D printing processes will be reviewed and the process-dependent properties of 3D-printed materials and structures will be analyzed in detail. In addition, the advantages and disadvantages of this manufacturing approach will be debated during class! To give students exposure to 3D printing systems in action, tours of actual 3D printing facilities on campus (Stanford's Product Realization Laboratory), as well as in Silicon Valley (e.g., Made in Space) will be conducted.
Last offered: Spring 2021 | Units: 3 | UG Reqs: WAY-AQR

AA 120Q: Building Trust in Autonomy

Major advances in both hardware and software have accelerated the development of autonomous systems that have the potential to bring significant benefits to society. Google, Tesla, and a host of other companies are building autonomous vehicles that can improve safety and provide flexible mobility options for those who cannot drive themselves. On the aviation side, the past few years have seen the proliferation of unmanned aircraft that have the potential to deliver medicine and monitor agricultural crops autonomously. In the financial domain, a significant portion of stock trades are performed using automated trading algorithms at a frequency not possible by human traders. How do we build these systems that drive our cars, fly our planes, and invest our money? How do we develop trust in these systems? What is the societal impact on increased levels of autonomy?
Last offered: Winter 2021 | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

AA 121Q: It IS Rocket Science!

It's an exciting time for space exploration. Companies like SpaceX and Blue Origin are launching rockets into space and bringing them back for reuse. NASA is developing the world's most powerful rocket. Startups are deploying constellations of hundreds of cubesats for communications, navigation, and earth monitoring. The human race has recently gotten a close look at Pluto, soft landed on a comet, and orbited two asteroids. The upcoming launch of the James Webb Space Telescope will allow astronomers to look closer to the beginning of time than ever before. The workings of space systems remain mysterious to most people, but in this seminar we'll pull back the curtain for a look at the basics of "rocket science." How does a SpaceX rocket get into space? How do Skybox satellites capture images for Google Earth? How did the New Horizons probe find its way to Pluto? How do we communicate with spacecraft that are so distant? We'll explore these topics and a range of others during the quarter. We'll cover just enough physics and math to determine where to look in the sky for a spacecraft, planet, or star. Then we'll check our math by going outside for an evening pizza party observing these objects in the night sky. We'll also visit a spacecraft production facility or Mission Operations Center to see theory put into practice.
Last offered: Autumn 2022 | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

AA 122N: Dawn of the Drones: How Will Unmanned Aerial Systems Change Our World?

Unmanned aerial systems (UASs) have exploded on the scene in recent years, igniting a national debate about how to use them, how to regulate them, and how to make them safe. This seminar will dive into the many engineering challenges behind the headlines: in the future, how will we engineer UASs ranging in size from simple RC toys to highly-sophisticated autonomous scientific and military data gathering systems? This seminar will examine the key elements required to conceive, implement, deploy, and operate state-of-the-art of drone systems: What variety of problems can they help us solve? How autonomous are they and how autonomous do they need to be? What are the key technical bottlenecks preventing widespread deployment? How are they different from commercial aircraft? What kinds of companies will serve the market for UAV-related products and services? What business models will be successful and why? We will emphasize aspects of design, autonomy, reliability, navigation, sensing, and perception, as well as coordination/collaboration through a series of case studies drawn from our recent experience. Examples include imaging efforts to map the changing coral reefs in the South Pacific, using and controlling swarms of unmanned systems to perform search and rescue missions over large areas, and package delivery systems over large metropolitan areas. Hands-on experience with Stanford-developed UASs will be part of the seminar.
Last offered: Spring 2018 | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

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: Freshman-level physics, basic calculus and differential equations, AA 100 (Introduction to Aeronautics and Astronautics).
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

AA 151: Lightweight Structures

The development of lightweight structures aids in enhancing the robustness, efficiency, and cost of aerospace systems. In this course, the theoretical principles used to analyze stress-strain behavior, beam bending, torsion, and thin-walled structures will be reviewed and exercised. In addition, students will study structures under various loading conditions found in real-world applications such as the design of airframes, high-altitude balloons, and solar sails. Students from various disciplines of engineering can benefit from this course. ENGR 14 (Introduction to Solid Mechanics) is a highly recommended prerequisite.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

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 take-home problem sets.
Terms: Aut | Units: 5 | UG Reqs: GER:DB-SocSci, WAY-AQR
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.
Last offered: Winter 2020 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

ANTHRO 198A: Archaeological Geographic Information Systems (ANTHRO 298A, ARCHLGY 198A, ARCHLGY 298A)

This advanced undergraduate and graduate seminar will provide students with practical and theoretical training in Geographical Information Systems (GIS) as applied to archaeological research, introducing students to spatial theories and GIS methodological applications to research design and analysis. Topics covered in the course will include: cartographic skills of displaying and visualizing archaeological data, GIS applications to research design and sampling, data acquisition and generation, spatial analyses of artifacts, features, sites, and landscapes, as well as a critical evaluation of the strengths and limitations of GIS spatial analyses and epistemologies. Prerequisites: By instructor consent. Significant work outside of class time is expected of the student in this course.
Terms: Win | Units: 5 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; Bauer, A. (PI); Engel, C. (PI)

APPPHYS 189: Physical Analysis of Artworks (APPPHYS 389, ARCHLGY 189)

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 undertake analysis projects derived from ongoing conservation efforts at FAMSF, including training on the use of relevant SNSF instruments and data analysis.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA
Instructors: ; Mabuchi, H. (PI)

ARCHLGY 189: Physical Analysis of Artworks (APPPHYS 189, APPPHYS 389)

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 undertake analysis projects derived from ongoing conservation efforts at FAMSF, including training on the use of relevant SNSF instruments and data analysis.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA
Instructors: ; Mabuchi, H. (PI)

ARCHLGY 198A: Archaeological Geographic Information Systems (ANTHRO 198A, ANTHRO 298A, ARCHLGY 298A)

This advanced undergraduate and graduate seminar will provide students with practical and theoretical training in Geographical Information Systems (GIS) as applied to archaeological research, introducing students to spatial theories and GIS methodological applications to research design and analysis. Topics covered in the course will include: cartographic skills of displaying and visualizing archaeological data, GIS applications to research design and sampling, data acquisition and generation, spatial analyses of artifacts, features, sites, and landscapes, as well as a critical evaluation of the strengths and limitations of GIS spatial analyses and epistemologies. Prerequisites: By instructor consent. Significant work outside of class time is expected of the student in this course.
Terms: Win | Units: 5 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; Bauer, A. (PI); Engel, C. (PI)

BIO 101: Science for Conservation Policy: Meeting California's Pledge to Protect 30% by 2030 (EARTHSYS 101C)

California has set the ambitious goal of conserving 30% of its lands and waters by the year 2030. In this course, students will develop science-based recommendations to help policymakers reach this '30 by 30' goal. Through lectures, labs, and field trips, students will gain practical skills in ecology, protected area design in the face of climate change, and science communication. Students will apply these skills to analyze real-world data, formulate conservation recommendations, and communicate their findings in verbal and written testimony to policymakers. Prerequisites: BIO 81 or BIO/EARTHSYS 105 or BIO/EARTHSYS 111 or instructor approval.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR

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: Win | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR

BIO 143: Quantitative Methods for Marine Ecology and Conservation (BIO 243, CEE 164, CEE 264H, EARTHSYS 143H, EARTHSYS 243H, OCEANS 143)

NOTE: This course will be taught in-person on main campus, in hybrid format with Zoom options. The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Formally BIOHOPK 143H and 243H.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-FR

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.
Last offered: Winter 2022 | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

BIODS 48N: Riding the Data Wave (STATS 48N)

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Last offered: Autumn 2020 | Units: 3 | UG Reqs: WAY-AQR, WAY-FR

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: WAY-AQR, WAY-SMA

BIOE 101: Systems Biology (BIOE 210)

Complex biological behaviors through the integration of computational modeling and molecular biology. Topics: reconstructing biological networks from high-throughput 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: WAY-AQR

BIOE 102: Physical Biology of Macromolecules

Principles of statistical physics, thermodynamics, and kinetics with applications to molecular biology. Topics include entropy, temperature, chemical forces, enzyme kinetics, free energy and its uses, self assembly, cooperative transitions in macromolecules, molecular machines, feedback, and accurate replication. Prerequisites: MATH 19, 20, 21; CHEM 31A, B (or 31X); strongly recommended: PHYSICS 41, CME 100 or MATH 51, and CME 106; or instructor approval.
Last offered: Winter 2019 | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

BIOE 103: Systems Physiology and Design

Physiology of intact human tissues, organs, and organ systems in health and disease, and bioengineering tools used (or needed) to probe and model these physiological systems. Topics: Clinical physiology, network physiology and system design/plasticity, diseases and interventions (major syndromes, simulation, and treatment, instrumentation for intervention, stimulation, diagnosis, and prevention), and new technologies including tissue engineering and optogenetics. Discussions of pathology of these systems in a clinical-case based format, with a view towards identifying unmet clinical needs. Learning computational skills that not only enable simulation of these systems but also apply more broadly to biomedical data analysis. Prerequisites: CME 102; PHYSICS 41; BIO 82 OR 83; BIO 84. CS 106A or programming experience highly recommended.
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

BIOE 103B: Systems Physiology and Design

ONLINE Offering of BIOE 103. This pilot class, BIOE103B, is an entirely online offering with the same content, learning goals, and prerequisites as BIOE 103. The class is open to BioE-declared students who are not on campus in the spring. Students attend class by watching videos and completing assignments remotely. Physiology of intact human tissues, organs, and organ systems in health and disease, and bioengineering tools used (or needed) to probe and model these physiological systems. Topics: Clinical physiology, network physiology and system design/plasticity, diseases and interventions (major syndromes, simulation, and treatment, instrumentation for intervention, stimulation, diagnosis, and prevention), and new technologies including tissue engineering and optogenetics. Discussions of pathology of these systems in a clinical case-based format, with a view towards identifying unmet clinical needs. Learning computational skills that not only enable simulation of these systems but also apply more broadly to biomedical data analysis. Prerequisites: CME 102; PHYSICS 41; BIO 82 OR 83; BIO 84. CS 106A or programming experience highly recommended.
Last offered: Spring 2023 | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

BIOE 158: Soft Matter in Biomedical Devices, Microelectronics, and Everyday Life (MATSCI 158)

The relationships between molecular structure, morphology, and the unique physical, chemical, and mechanical behavior of polymers and other types of soft matter are discussed. Topics include methods for preparing synthetic polymers and examination of how enthalpy and entropy determine conformation, solubility, mechanical behavior, microphase separation, crystallinity, glass transitions, elasticity, and linear viscoelasticity. Case studies covering polymers in biomedical devices and microelectronics will be covered. Recommended: ENGR 50 and Chem 31A or equivalent.
Last offered: Winter 2020 | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

CEE 41Q: Clean Water Now! Urban Water Conflicts

Why do some people have access to as much safe, clean water as they need, while others do not? You will explore answers to this question by learning about, discussing and debating urban water conflicts including the Flint water crisis, the drought in South Africa, intermittent water supply in Mumbai, and arsenic contamination in Bangladesh. In this course, you will explore the technical, economic, institutional, social, policy, and legal aspects of urban water using these and more water conflicts as case studies. You will attend lectures, and participate in discussions, laboratory modules, and field work. In lectures, you will learn about the link between water and human and ecosystem health, drinking water and wastewater treatment methods, as well as policies and guidelines (local, national, and global from the World Health Organization) on water and wastewater, and the role of various stakeholders including institutions and the public, in the outcome of water conflicts. You will dive into details of conflicts over water through case studies using discussion and debate. You will have the opportunity to measure water contaminants in a laboratory module. You will sample a local stream and measure concentrations of Escherichia coli and enterococci bacteria in the water. A field trip to a local wastewater treatment plant will allow you to see how a plant operates. By the end of this course, you will have a greater appreciation of the importance of institutions, stakeholders and human behavior in the outcome of water conflicts, and the complexity of the coupled human-ecosystem-urban water system.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR, WAY-SI

CEE 70: Environmental Science and Technology (ENGR 90)

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 | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

CEE 80N: Engineering the Built Environment: An Introduction to Structural Engineering

In this seminar, students will be introduced to the history of modern bridges, buildings and other large-scale structures. Classes will include presentations on transformations in structural design inspired by the development of new materials, increased understanding of hazardous overloads and awareness of environmental impacts. Basic principles of structural engineering and how to calculate material efficiency and structural safety of structural forms will be taught using case studies. The course will include a field trip to a Bay Area large-scale structure, hands-on experience building a structure, computational modeling of bridges, and a paper and presentation on a structure or structural form of interest to the student. The goal of this course is for students to develop an understanding and appreciation of modern structures, influences that have led to new forms, and the impact of structural design on society and the environment. Students from all backgrounds are welcome.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR

CEE 164: Quantitative Methods for Marine Ecology and Conservation (BIO 143, BIO 243, CEE 264H, EARTHSYS 143H, EARTHSYS 243H, OCEANS 143)

NOTE: This course will be taught in-person on main campus, in hybrid format with Zoom options. The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Formally BIOHOPK 143H and 243H.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-FR

CEE 176B: 100% Clean, Renewable Energy and Storage for Everything (CEE 276B)

This course discusses elements of a transition to 100% clean, renewable energy in the electricity, transportation, heating/cooling, and industrial sectors for towns, cities, states, countries, and companies. It examines wind, solar, geothermal, hydroelectric, tidal, and wave characteristics and resources; electricity, heat, cold and hydrogen storage; transmission and distribution; matching power demand with supply on the grid: efficiency; replacing fossil with electric appliances and machines in the buildings and industry; energy, health, and climate costs and savings; land requirements; feedbacks of renewables to the atmosphere; and 100% clean, renewable energy roadmaps to guide transitions.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR
Instructors: ; Jacobson, M. (PI)

CEE 178: Introduction to Human Exposure Analysis (CEE 276)

(Graduate students register for 276.) Scientific and engineering issues involved in quantifying human exposure to toxic chemicals in the environment. Pollutant behavior, inhalation exposure, dermal exposure, and assessment tools. Overview of the complexities, uncertainties, and physical, chemical, and biological issues relevant to risk assessment. Lab projects. Recommended: MATH 51. Apply at first class for admission.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

CHEM 131: Instrumental Analysis Principles and Practice

The core objectives of the course will focus upon introducing and providing hands-on practice with analytical separation, spectroscopic identification, and calibrated quantification with strong technical communication (for the Writing-in-the-Major requirement) emphasized throughout the course. Lectures will focus on theory, and laboratory activities will provide hands-on practice with the GC, LC, XPS, ICP, MS, and UV/Vis instruments. Data analysis will be emphasized throughout the course with Python being the primary tool for plotting and computations. Statistical measurements will be introduced to gauge the quality and validity of data. Lectures will be three times a week with a required four-hour laboratory section. The course should be completed prior to CHEM courses 174,176, or 184. Prerequisite: CHEM 33 or CHEM 100; and CS 106A.
Terms: Spr | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA
Instructors: ; Kromer, M. (PI); Liu, F. (PI)

CHEMENG 20: Introduction to Chemical Engineering (ENGR 20)

Overview of chemical engineering through discussion and engineering analysis of physical and chemical processes. Topics: overall staged separations, material and energy balances, concepts of rate processes, energy and mass transport, and kinetics of chemical reactions. Applications of these concepts to areas of current technological importance: biotechnology, energy, production of chemicals, materials processing, and purification. Prerequisite: CHEM 31.
Terms: Win | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

CHEMENG 55: Foundational Biology for Engineers (ENGR 55)

Biology, physics, and chemistry are the substrates for the modern engineer. Whether you are interested in developing the next generation of medicines or would like the next material or catalyst you design to be inspired by solutions found in Nature, this course will deepen your knowledge of the foundational concepts in biology and enrich your engineering skills. We will introduce the physical principles that underlie the construction and function of living cells, the fundamental building block of life. Emphasis will be on systems, logic, quantitation, and mechanisms of the molecular processes utilized by all life on Earth. This course has no prerequisites, but prior completion of CHEM 31 or equivalent is highly recommended.
Terms: Aut | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

CHEMENG 60Q: Environmental Regulation and Policy

Preference to sophomores. How does government, politics and science affect environmental policy? We examine environmental policy including the precautionary principal, acceptable risks, mathematical models, and cost-effectiveness of regulation. You will learn how data is changing environmental regulation and how different administrations mold environmental policy in real-time. We examine the use of science and engineering, its media presentation and misrepresentation, and the effect of public scientific and technical literacy. You will learn how to participate in the process and effect change.
Last offered: Autumn 2020 | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

CHEMENG 65Q: Chemical Engineering for Sustainability

Do you want to make the world more sustainable? How will we address the tremendous challenges that climate change brings? How can we reduce carbon emissions and not have huge disruptions in society? This class is for anyone who wants to create sustainable alternatives to what we use every day: engineers, scientists, those in humanities and the arts. Everyone has a role to play in designing our future. We will learn how to make the world more sustainable by exploring the exciting new world of (chemical) engineering sustainability. We will discuss renewable diesel and jet fuels; synthetic meat; compostable plastics; building materials that save energy; direct capture of carbon from the air; biological pharmaceuticals; and advanced recycling operations. The class starts with a brief overview of the deep cuts in carbon emissions and other pollutants that will be needed. Then, we focus on how sustainable (chemical) engineering can provide a solution, visiting four companies who are changing the world. Students will leave the class with an appreciation of how sustainable (chemical) engineering can help address climate change's substantial challenges, and perhaps an internship with one of the companies we visit. High school chemistry (balancing a chemical equation) and high school physics (unit conversions) are recommended for this course.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR
Instructors: ; Libicki, S. (PI)

CME 106: Introduction to Probability and Statistics for Engineers (ENGR 155C)

Probability: random variables, independence, and conditional probability; discrete and continuous distributions, moments, distributions of several random variables. Numerical simulation using Monte Carlo techniques. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses. Numerous applications in engineering, manufacturing, reliability and quality assurance, medicine, biology, and other fields. Prerequisite: CME100/ENGR154 or Math 51 or 52.
Terms: Win, Sum | Units: 4 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

CME 108: Introduction to Scientific Computing

Introduction to Scientific Computing Numerical computation for mathematical, computational, physical sciences and engineering: error analysis, floating-point 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: CME 100, 102 or MATH 51, 52, 53; prior programming experience (MATLAB or other language at level of CS 106A or higher).
Terms: Aut | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

COLLEGE 105: The Politics of Development

This course examines foundational reasons for why some countries remain poor and why inequality persists today. In addition to answering the why question, we will also examine how practitioners, policy-makers, and academics have tackled global development challenges, where they have met success, and where failure has provided key lessons for the future. The course will examine issues of colonialism and contemporary foreign aid. Students will learn about and explore patterns of development across the world, critically evaluate foundational theories of development, and understand the practical challenges and possible solutions to reducing poverty, creating equality, and ensuring good governance. Course assignments will aim to have students practice linking data and evidence with policy innovation, using global datasets to perform statistical analyses. Students will leave this class with an understanding of how development works (and doesn't work) in practice.
Last offered: Spring 2023 | Units: 4 | UG Reqs: College, THINK, WAY-AQR, WAY-SI

COMM 106: Communication Research Methods (COMM 206)

(Graduate students register for COMM 206. COMM 106 is offered for 5 units, COMM 206 is offered for 4 units.) Conceptual and practical concerns underlying commonly used quantitative approaches, including experimental, survey, content analysis, and field research in communication. Pre- or corequisite: STATS 60 or consent of instructor. (Cardinal Course certified by the Haas Center)
Terms: Aut | Units: 4-5 | UG Reqs: GER:DB-SocSci, WAY-AQR

COMM 138: Deliberative Democracy Practicum: Applying Deliberative Polling (COMM 238)

In this course, students will work directly on a real-world deliberative democracy project using the method of Deliberative Polling. Students in this course will work in partnership with the Center for Deliberative Democracy at Stanford, a research center devoted to the research in democracy and public opinion around the world. This unique practicum will allow students to work on an actual Deliberative Polling project on campus. In just one quarter, the students will prepare for, implement, and analyze the results for an Deliberative Polling project. This is a unique opportunity that allows students to take part in the entire process of a deliberative democracy project. Through this practicum, students will learn and apply quantitative and qualitative research methods. Students will explore the underlying challenges and complexities of what it means to actually do community-engaged research in the real world. As such, this course will provide students with skills and experience in research design in deliberative democracy, community and stakeholder engagement, and the practical aspects of working in local communities. This practicum is a collaboration between the Center for Deliberative Democracy and the Haas Center for Public Service. CDD website: http://cdd.stanford.edu; Hass Center website: https://haas.stanford.edu. Cardinal Course certified by the Haas Center for Public Service.
Terms: Aut | Units: 3-5 | UG Reqs: WAY-AQR, WAY-SI | Repeatable 3 times (up to 15 units total)
Instructors: ; Siu, A. (PI)

COMM 140X: Solving Social Problems with Data (DATASCI 154, EARTHSYS 153, ECON 163, MS&E 134, 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 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-AQR, WAY-SI

CS 46: Working with Data: Delights and Doubts

The use of data to drive decisions and discoveries has increased dramatically over the past two decades, thanks to prevalent data collection, cheaper storage, faster computers, and sophisticated algorithms. This introductory seminar has three components: (1) Hands-on instruction in tools and techniques for working with data, from spreadsheets to data visualization systems to machine learning packages. This material is designed for students with little or no computer programming or data science experience. (2) A quarter-long "quantified self" project where students identify a set of questions about themselves or their surroundings, collect data to answer the questions, and analyze and visualize the collected data. (3) A set of guest speakers, including some who focus on the "doubts" of collecting and exploiting data, such as questions of ethics, bias, and privacy. In addition to the course project, students will complete short assignments to practice the learned tools and techniques, and will be expected to do some readings in advance of each guest speaker and engage in thoughtful discussion.
| Units: 3 | UG Reqs: WAY-AQR

CS 102: Working with Data - Tools and Techniques

Aimed at non-CS undergraduate and graduate students who want to learn a variety of tools and techniques for working with data. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing data sets. This course provides a broad and practical introduction to working with data: data analysis techniques including databases, data mining, machine learning, and data visualization; data analysis tools including spreadsheets, Tableau, relational databases and SQL, Python, and R; introduction to network analysis and unstructured data. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science, CS106A, or other equivalent programming experience.
Last offered: Spring 2020 | Units: 3-4 | UG Reqs: WAY-AQR

CS 109: Introduction to Probability for Computer Scientists

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms. Prerequisites: 103, 106B or X, multivariate calculus at the level of MATH 51 or CME 100 or equivalent.
Terms: Aut, Win, Spr, Sum | Units: 3-5 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).
Terms: Win | Units: 3-4 | UG Reqs: WAY-AQR

CS 230: Deep Learning

Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Familiarity with programming in Python and Linear Algebra (matrix / vector multiplications). CS 229 may be taken concurrently.
Last offered: Spring 2023 | Units: 3-4 | UG Reqs: WAY-AQR, WAY-FR

CSRE 141S: Immigration and Multiculturalism (POLISCI 141A)

What are the economic effects of immigration? Do immigrants assimilate into local culture? What drives native attitudes towards immigrants? Is diversity bad for local economies and societies and which policies work for managing diversity and multiculturalism? We will address these and similar questions by synthesizing the conclusions of a number of empirical studies on immigration and multiculturalism. The emphasis of the course is on the use of research design and statistical techniques that allow us to move beyond correlations and towards causal assessments of the effects of immigration and immigration policy.
Last offered: Winter 2022 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

DATASCI 112: Principles of Data Science

A hands-on introduction to the methods of data science. Strategies for analyzing and visualizing tabular data, including common patterns and pitfalls. Data acquisition through web scraping and REST APIs. Core principles of machine learning: supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. Introduction to data of different shapes and sizes, including text, image, and geospatial data. The focus is on intuition and implementation, rather than theory and math. Implementation is in Python and Jupyter notebooks, using libraries such as pandas and scikit-learn. Course culminates in a final project where students apply the methods to a data science problem of their choice. Prerequisite: CS 106A or equivalent programming experience in Python. (Students with experience in another programming language should take CS 193Q to catch up on Python.)
Terms: Win, Spr, Sum | Units: 4 | UG Reqs: WAY-AQR

DATASCI 154: Solving Social Problems with Data (COMM 140X, EARTHSYS 153, ECON 163, MS&E 134, 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 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-AQR, WAY-SI

EARTHSYS 11: Introduction to Geology (EPS 1)

(Former GEOLSCI 1) Why are earthquakes, volcanoes, and natural resources located at specific spots on the Earth's 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, hands-on laboratories, in-class activities, and one virtual 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. Change of Department Name: Earth & Planetary Sciences (Formerly Geological Science)
Terms: Spr | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

EARTHSYS 100A: Introduction to Data Science for Geoscience (EPS 6)

(Formerly GEOLSCI 6) This course provides an overview of the most relevant areas of data science to address geoscientific challenges and questions as they pertain to the environment, earth resources & hazards. The focus lies on the methods that treat common characters of geoscientific data: multivariate, multi-scale, compositional, geospatial and space-time. In addition, the course will treat those statistical method that allow a quantification of the human dimension by looking at quantifying impact on humans (e.g. hazards, contamination) and how humans impact the environment (e.g. contamination, land use). The course focuses on developing skills that are not covered in traditional statistics and machine learning courses. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).
Terms: Win | Units: 3 | UG Reqs: WAY-AQR | Repeatable 3 times (up to 9 units total)

EARTHSYS 101: Energy and the Environment (ENERGY 101)

Energy use in modern society and the consequences of current and future energy use patterns. Case studies illustrate resource estimation, engineering analysis of energy systems, and options for managing carbon emissions. Focus is on energy definitions, use patterns, resource estimation, pollution.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

EARTHSYS 101C: Science for Conservation Policy: Meeting California's Pledge to Protect 30% by 2030 (BIO 101)

California has set the ambitious goal of conserving 30% of its lands and waters by the year 2030. In this course, students will develop science-based recommendations to help policymakers reach this '30 by 30' goal. Through lectures, labs, and field trips, students will gain practical skills in ecology, protected area design in the face of climate change, and science communication. Students will apply these skills to analyze real-world data, formulate conservation recommendations, and communicate their findings in verbal and written testimony to policymakers. Prerequisites: BIO 81 or BIO/EARTHSYS 105 or BIO/EARTHSYS 111 or instructor approval.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR

EARTHSYS 104: The Water Course (EARTHSYS 204, GEOPHYS 104, GEOPHYS 204)

The Central Valley of California provides a third of the produce grown in the U.S., but recent droughts and increasing demand have raised concerns about both food and water security. The pathway that water takes from rainfall to the irrigation of fields or household taps ('the water course') determines the quantity and quality of the available water. Working with various data sources (measurements made on the ground, in wells, and from satellites) allows us to model the water budget in the valley and explore the recent impacts on freshwater supplies.
Last offered: Winter 2022 | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

EARTHSYS 110: Introduction to the Foundations of Contemporary Geophysics (GEOPHYS 110, GEOPHYS 215)

Introduction to the foundations of contemporary geophysics. Lectures link important topics in contemporary Geophysics ("What we study") to methods used to make progress on these topics ("How we study"). Topics range from plate tectonics to natural hazards; ice sheets to sustainability. For each topic, we focus is on how the interpretation of geophysical measurements (e.g., gravity, seismology, heat flow, electromagnetism and remote sensing) provides fundamental insight into the behavior of the Earth. The course will includes a required all-day Saturday field exercise Feb 02/10 (rain-date: 02/17). Prerequisite: CME 100 or MATH 51, or co-registration in either.
Terms: Win | Units: 3 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

EARTHSYS 113: Earthquakes and Volcanoes (GEOPHYS 90)

Is the "Big One" overdue in California? What kind of damage would that cause? What can we do to reduce the impact of such hazards in urban environments? Does "fracking" cause earthquakes and are we at risk? Is the United States vulnerable to a giant tsunami? The geologic record contains evidence of volcanic super eruptions throughout Earth's history. What causes these gigantic explosive eruptions, and can they be predicted in the future? This course will address these and related issues. For non-majors and potential Earth scientists. No prerequisites. More information at: https://stanford.box.com/s/zr8ar28efmuo5wtlj6gj2jbxle76r4lu
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
Instructors: ; Beroza, G. (PI)

EARTHSYS 114: Global Change and Emerging Infectious Disease (EARTHSYS 214, ESS 213, HUMBIO 114)

The changing epidemiological environment. How human-induced environmental changes, such as global warming, deforestation and land-use conversion, urbanization, international commerce, and human migration, are altering the ecology of infectious disease transmission, and promoting their re-emergence as a global public health threat. Case studies of malaria, cholera, hantavirus, plague, and HIV.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SMA
Instructors: ; Jones, J. (PI)

EARTHSYS 141: Remote Sensing of the Oceans (EARTHSYS 241, ESS 141, ESS 241, GEOPHYS 141)

How to observe and interpret physical and biological changes in the oceans using satellite technologies. Topics: principles of satellite remote sensing, classes of satellite remote sensors, converting radiometric data into biological and physical quantities, sensor calibration and validation, interpreting large-scale oceanographic features.
Terms: Win | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR
Instructors: ; Arrigo, K. (PI)

EARTHSYS 142: Remote Sensing of Land (EARTHSYS 242, ESS 162, ESS 262)

The use of satellite remote sensing to monitor land use and land cover, with emphasis on terrestrial changes. Topics include pre-processing data, biophysical properties of vegetation observable by satellite, accuracy assessment of maps derived from remote sensing, and methodologies to detect changes such as urbanization, deforestation, vegetation health, and wildfires.
Terms: Spr | Units: 3 | UG Reqs: WAY-AQR

EARTHSYS 143H: Quantitative Methods for Marine Ecology and Conservation (BIO 143, BIO 243, CEE 164, CEE 264H, EARTHSYS 243H, OCEANS 143)

NOTE: This course will be taught in-person on main campus, in hybrid format with Zoom options. The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Formally BIOHOPK 143H and 243H.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-FR

EARTHSYS 144: Fundamentals of Geographic Information Science (GIS) (ESS 164)

Everything is somewhere, and that somewhere matters." The rapid growth and maturity of spatial data technologies over the past decade represent a paradigm shift in the applied use of location data from high-level overviews of administrative interests, to highly personalized location-based services that place the individual at the center of the map, at all times. The use of spatial data and related technology continues to grow in fields ranging from environmental sciences to epidemiology to market prediction. This course will present an overview of current approaches to the use of spatial data and its creation, capture, management, analysis and presentation, in a research context. Topics will include modeling of geographic objects and associated data, modeling of geographic space and the conceptual foundations of "spatial thinking," field data collection, basic spatial statistical analysis, remote sensing & the use of satellite-based imagery, "Big Data" and machine learning approaches to spatial data, and cartographic design and presentation including the use of web-based "Storymap" platforms. The course will consist of weekly lectures, guest speakers, computer lab assignments, midterm and final exams, as well as an individual final project requirement. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit.
Terms: Aut, Spr | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR

EARTHSYS 152: Marine Chemistry (EARTHSYS 252, ESS 152, ESS 252, OCEANS 152, OCEANS 252)

Introduction to the interdisciplinary knowledge and skills required to critically evaluate problems in marine chemistry and related disciplines. Physical, chemical, and biological processes that determine the chemical composition of seawater. Air-sea gas exchange, carbonate chemistry, and chemical equilibria, nutrient and trace element cycling, particle reactivity, sediment chemistry, and diagenesis. Examination of chemical tracers of mixing and circulation and feedbacks of ocean processes on atmospheric chemistry and climate. Designed to be taken concurrently with Biological Oceanography (ESS/EARTHSYS 151/251)
Last offered: Spring 2023 | Units: 3-4 | UG Reqs: WAY-AQR, WAY-SMA

EARTHSYS 153: Solving Social Problems with Data (COMM 140X, DATASCI 154, ECON 163, MS&E 134, 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 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-AQR, WAY-SI

EARTHSYS 185: Feeding Nine Billion

Feeding a growing and wealthier population is a huge task, and one with implications for many aspects of society and the environment. There are many tough choices to be made- on fertilizers, groundwater pumping, pesticide use, organics, genetic modification, etc. Unfortunately, many people form strong opinions about these issues before understanding some of the basics of how food is grown, such as how most farmers currently manage their fields, and their reasons for doing so. The goal of this class is to present an overview of global agriculture, and the tradeoffs involved with different practices. Students will develop two key knowledge bases: basic principles of crop ecology and agronomy, and familiarity with the scale of the global food system. The last few weeks of the course will be devoted to building on this knowledge base to evaluate different future directions for agriculture.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR

ECON 46: Networks and Human Behavior

Two threads are interwoven: why social and economic networks have special features, and how those features shape power, opinions, opportunities, and behaviors. Some of the topics included are: the different ways in which a person's position in a network determines their influence; which systematic errors people make when forming opinions based on what they learn from others; how financial contagions work and why are they different from the spread of a flu; the role of splits in our social networks in inequality, immobility, and polarization; and how network patterns of trade and globalization have changed international conflict and wars. The course requires analyzing network data, which will be provided. No prerequisite but Econ 102A or equivalent is recommended.
Last offered: Spring 2021 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

ECON 47: Media Markets and Social Good

This class will apply tools from economics and related social sciences to study the functioning of media markets and their impact on society. The guiding question will be: when and how do media best serve the social good? Topics will include the economics of two-sided markets, media bias, polarization, social media, fake news, advertising, propaganda, effects of media on children, media and crime, and the role of media in corruption, protests and censorship. The course will give students a non-technical introduction to social science empirical methods, including regression analysis, causal inference, experimental and quasi-experimental methods, and machine learning.
Last offered: Autumn 2020 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

ECON 102A: Introduction to Statistical Methods (Postcalculus) for Social Scientists

Probabilistic modeling and statistical techniques relevant for economics. Concepts include: probability trees, conditional probability, random variables, discrete and continuous distributions, correlation, central limit theorems, point estimation, hypothesis testing and confidence intervals for both one and two populations. Prerequisite: MATH 20 or equivalent.
Terms: Aut, Win | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-SI
Instructors: ; McKeon, S. (PI)

ECON 102B: Applied Econometrics

Hypothesis tests and confidence intervals for population variances, chi-squared goodness-of-fit tests, hypothesis tests for independence, simple linear regression model, testing regression parameters, prediction, multiple regression, omitted variable bias, multicollinearity, F-tests, regression with indicator random variables, simultaneous equation models and instrumental variables. Topics vary slightly depending on the quarter. Prerequisites: Econ 102A or equivalent. Recommended: computer experience (course often uses STATA software to run regressions).
Terms: Win, Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; McKeon, S. (PI)

ECON 102C: Advanced Topics in Econometrics

This is an advanced econometrics class targeted to students who want to go deeper into and/or expand their knowledge of topics firstly learned in Econ 102B (or equivalent class). Topics include: Instrumental variables estimation; Panel data models (fixed and random effect models, dynamic panel data models); Limited dependent variable models (probit, logit, Tobit) and selection models; models for Duration data; Bootstrap and Estimation by Simulation. Applications from Labor Economics and Public Finance will be used to motivate the discussion. Prerequisite: Econ 102B
Terms: Win | Units: 5 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; Pistaferri, L. (PI)

ECON 102D: Econometric Methods for Public Policy Analysis and Business Decision-Making

This course focuses on the use of econometric methods in public policy analysis and business decision-making. Building on methods taught in Economics 102A and 102B, additional descriptive, predictive and causal econometric modeling methods will be introduced along with the assumptions required for the validity of each methodology. Methods for designing randomized controlled trials (RCT) and analyzing the resulting data will be discussed. The methods for recovering economically meaningful magnitudes such as price elasticities of demand and other behavioral responses from observational data will be discussed. Both classical econometric methods and modern techniques in machine learning will be employed. The class will be taught using the R programming language. Students will perform both in-class and out-of-class assignments working with actual datasets to address policy-relevant decisions and simulation exercises designed to deepen their knowledge of these methods. Prerequisites: Econ102A, Econ102B
Last offered: Autumn 2020 | Units: 5 | UG Reqs: WAY-AQR

ECON 118: Development Economics

The microeconomic problems and policy concerns of less developed countries. Topics include: health and education; risk and insurance; microfinance; agriculture; technology; governance. Emphasis is on economic models and empirical evidence. Prerequisites: ECON 50, ECON 102B.
Terms: Spr | Units: 5 | UG Reqs: GER:EC-GlobalCom, WAY-AQR, WAY-SI
Instructors: ; Saavedra Pineda, S. (PI)

ECON 125: Economic Development, Microfinance, and Social Networks

An introduction to the study of the financial lives of households in less developed countries, focusing on savings, credit, informal insurance, the expansion of microfinance, social learning, public finance/redistribution, and social networks. Prerequisites- Econ 51 or Publpol 51 and Econ 102B.
Terms: Spr | Units: 5 | UG Reqs: GER:EC-GlobalCom, WAY-AQR, WAY-SI
Instructors: ; Chandrasekhar, A. (PI)

ECON 137: Decision Modeling and Information

Effective decision models consider a decision maker's alternatives, information and preferences. The construction of such models in single-party situations with emphasis on the role of information. The course then evolves to two-party decision situations where one party has more information than the other. Models examined include: bidding exercises and the winner's curse, the Akerlof Model and adverse selection, the Principal-Agent model and risk sharing, moral hazard and contract design. Prerequisite: ECON 102A or equivalent. Recommended: Econ 50, Optimization and simulation in Excel.
Terms: Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-FR
Instructors: ; McKeon, S. (PI)

ECON 144: Family and Society

The family into which a child is born plays a powerful role in determining lifetime opportunities. This course will apply tools from economics and related social sciences to study how the functioning of families is shaped by laws, social insurance, social norms, and technology. Topics will include intergenerational transmission of wealth and health, the importance of the early family environment, partnership formation, cohabitation and marriage, teen pregnancy and contraception, assisted reproduction, Tiger Moms and Helicopter Parenting, and the employment effects of parenthood. In the context of these topics, the course will cover social science empirical methods, including regression analysis, causal inference, and quasi-experimental methods. Throughout the course, we will think critically about the role of the government and how the design of public policy targeting families affect our ability to solve some of the most important social and economic problems of our time. Prerequisites: Econ 50
Terms: Win | Units: 5 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; Persson, P. (PI)

ECON 145: Labor Economics

Analysis and description of labor markets. Determination of employment, hours of work, and wages. Wage differentials. Earnings inequality. Trade unions and worker co-operatives. Historical and international comparisons.. Prerequisites: ECON 51 (Public Policy majors may take PUBLPOL 51 as a substitute for ECON 51), ECON 102B.
Last offered: Autumn 2018 | Units: 5 | UG Reqs: GER:EC-Gender, WAY-AQR, WAY-SI

ECON 150: Economic Policy Analysis (PUBLPOL 104, PUBLPOL 204)

The relationship between microeconomic analysis and public policy making. How economic policy analysis is done and why political leaders regard it as useful but not definitive in making policy decisions. Economic rationales for policy interventions, methods of policy evaluation and the role of benefit-cost analysis, economic models of politics and their application to policy making, and the relationship of income distribution to policy choice. Theoretical foundations of policy making and analysis, and applications to program adoption and implementation. Prerequisites: PUBLPOL 50 or ECON 50. Students are also strongly encouraged to either complete ECON 102B prior to taking this course or take ECON 102B concurrently with this course. Undergraduate Public Policy students are required to take this class for a letter grade and enroll in this class for five units.
Terms: Win | Units: 4-5 | UG Reqs: WAY-AQR
Instructors: ; Rosston, G. (PI)

ECON 151: Tackling Big Questions Using Social Data Science (POLISCI 151)

Big data can help us provide answers to fundamental social questions, from poverty and social mobility, to climate change, migration, and the spread of disease. But making sense of data requires more than just statistical techniques: it calls for models of how humans behave and interact with each other. Social data science combines the analysis of big data with social science theory. We will take a project-oriented, many models-many methods approach. This course will introduce students to a variety of models and methods used across the social sciences, including tools such as game theoretical models, network models, models of diffusion and contagion, agent based models, model simulations, machine learning and causal inference. Students will apply these tools to tackle important topics in guided projects. Prerequisite is Econ 102A, Polisci 150A or equivalent.
Last offered: Autumn 2022 | Units: 5 | UG Reqs: WAY-AQR

ECON 162: Games Developing Nations Play (POLISCI 247A, POLISCI 347A)

If, as economists argue, development can make everyone in a society better off, why do leaders fail to pursue policies that promote development? The course uses game theoretic approaches from both economics and political science to address this question. Incentive problems are at the heart of explanations for development failure. Specifically, the course focuses on a series of questions central to the development problem: Why do developing countries have weak and often counterproductive political institutions? Why is violence (civil wars, ethnic conflict, military coups) so prevalent in the developing world, and how does it interact with development? Why do developing economies fail to generate high levels of income and wealth? We study how various kinds of development traps arise, preventing development for most countries. We also explain how some countries have overcome such traps. This approach emphasizes the importance of simultaneous economic and political development as two different facets of the same developmental process. No background in game theory is required.
Last offered: Winter 2021 | Units: 3-5 | UG Reqs: WAY-AQR, WAY-SI

ECON 163: Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, MS&E 134, 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 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-AQR, WAY-SI

ECON 179: Experimental Economics

Methods and major subject areas that have been addressed by laboratory experiments. Focus is on a series of experiments that build on one another. Topics include decision making, two player games, auctions, and market institutions. How experiments are used to learn about preferences and behavior, trust, fairness, and learning. Final presentation of group projects. Prerequisites: ECON 51 (Public Policy majors may take PUBLPOL 51 as a substitute for ECON 51), ECON 102A.
Last offered: Autumn 2019 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

EE 42: Introduction to Electromagnetics and Its Applications (ENGR 42)

Electricity and magnetism and its essential role in modern electrical engineering devices and systems, such as sensors, displays, DVD players, and optical communication systems. The topics that will be covered include electrostatics, magnetostatics, Maxwell's equations, one-dimensional wave equation, electromagnetic waves, transmission lines, and one-dimensional resonators. Pre-requisites: none.
Terms: Spr, Sum | Units: 5 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

EE 102A: Signals and Systems I

Concepts and tools for continuous- and discrete-time signal and system analysis with applications in signal processing, communications, and control. Mathematical representation of signals and systems. Linearity and time invariance. System impulse and step responses. System frequency response. Frequency-domain representations: Fourier series and Fourier transforms. Filtering and signal distortion. Time/frequency sampling and interpolation. Continuous-discrete-time signal conversion and quantization. Discrete-time signal processing. Prerequisites: MATH 53 or CME 102. EE 102A may be taken concurrently with either course, provided students have proficiency in complex numbers.
Terms: Win | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

EE 102B: Signals and Systems II

Continuation of EE 102A. Concepts and tools for continuous- and discrete-time signal and system analysis with applications in communications, signal processing and control. Analog and digital modulation and demodulation. Sampling, reconstruction, decimation and interpolation. Finite impulse response filter design. Discrete Fourier transforms, applications in convolution and spectral analysis. Laplace transforms, applications in circuits and feedback control. Z transforms, applications in infinite impulse response filter design. Prerequisite: EE 102A.
Terms: Spr | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

EE 108: Digital System Design

Digital circuit, logic, and system design. Digital representation of information. CMOS logic circuits. Combinational logic design. Logic building blocks, idioms, and structured design. Sequential logic design and timing analysis. Clocks and synchronization. Finite state machines. Microcode control. Digital system design. Control and datapath partitioning. Lab. *In Autumn, enrollment preference is given to EE majors. Any EE majors who must enroll in Autumn are invited to contact the instructor. Formerly EE 108A.
Terms: Aut, Win | Units: 5 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

EE 114: Fundamentals of Analog Integrated Circuit Design (EE 214A)

Analysis and simulation of elementary transistor stages, current mirrors, supply- and temperature-independent bias, and reference circuits. Overview of integrated circuit technologies, circuit components, component variations and practical design paradigms. Differential circuits, frequency response, and feedback will also be covered. Performance evaluation using computer-aided design tools. Undergraduates must take EE 114 for 4 units. Prerequisite: 101B. GER:DB-EngrAppSci
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

EE 115: Taking the Pulse of the Planet (GEOPHYS 115)

Grappling with the big questions of sustainability and climate change, requires that we have ways to measure ? as we cannot manage what we cannot measure. This course, Taking the Pulse of the Planet introduces a new research and teaching initiative at Stanford ? also called Taking the Pulse of the Planet, which has the following goal: to have in place a global network of satellite, airborne, land/water-based sensors to support the real-time adaptive management of planetary health and human activities. Measurements will be made at the spatial and temporal scales required to inform the development and implementation of new policies addressing critical issues related to climate change, sustainability, and equity. Tapping into rapid advancements in sensor technology and data science over the past decade, we can now image and monitor many components of the Earth system and human activities. With the launch of the Stanford Doerr School of Sustainability, we wish to celebrate, through this course, the powerful role that advancements in technology ? specifically sensors ? and advancements in data science are playing in addressing the global challenges in sustainability and climate change. This will be a lecture class for undergraduates and graduate students designed to introduce them to the incredible array of sensors and data sets now available. We will finish the quarter with group projects that will involve the making and deployment of sensors around campus. The course will be designed to accommodate students at any level, with any background, with no required pre-requisites. In most of the assignments, we will be using Google co-lab to work with various types of sensor data. We anticipate drawing to this course both data-science-savvy and data-science-interested students. Therefore, we have developed online modules that are designed to help any student get up to speed on the "jargon" and the computational approaches used in the class.
| Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

EE 134: Introduction to Photonics

Optics and photonics underpin the technologies that define our daily life, from communications and sensing to displays and imaging. This course provides an introduction to the principles that govern the generation, manipulation, and detection of light and will give students hands-on lab experience applying these principles to analyze and design working optical systems. The concepts we will cover form the basis for many systems in biology, optoelectronics, and telecommunications and build a foundation for further learning in photonics and optoelectronics. Connecting theory to observation and application is a major theme for the course. Prerequisite: EE 102A and one of the following: EE 42, Physics 43, or Physics 63.
Terms: Win | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
Instructors: ; Choi, J. (PI); Mishra, S. (TA)

EE 178: Probabilistic Systems Analysis

Introduction to probability and its role in modeling and analyzing real world phenomena and systems, including topics in statistics, machine learning, and statistical signal processing. Elements of probability, conditional probability, Bayes rule, independence. Discrete and continuous random variables. Signal detection. Functions of random variables. Expectation; mean, variance and covariance, linear MSE estimation. Conditional expectation; iterated expectation, MSE estimation, quantization and clustering. Parameter estimation. Classification. Sample averages. Inequalities and limit theorems. Confidence intervals. Prerequisites: Calculus at the level of MATH 51, CME 100 or equivalent and basic knowledge of computing at the level of CS106A.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

ENERGY 101: Energy and the Environment (EARTHSYS 101)

Energy use in modern society and the consequences of current and future energy use patterns. Case studies illustrate resource estimation, engineering analysis of energy systems, and options for managing carbon emissions. Focus is on energy definitions, use patterns, resource estimation, pollution.
Terms: Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

ENERGY 104: Sustainable Energy for 9 Billion (ENERGY 204)

This course explores the global transition to a sustainable global energy system. We will formulate and program simple models for future energy system pathways. We will explore the drivers of global energy demand and carbon emissions, as well as the technologies that can help us meet this demand sustainably. We will consider constraints on the large-scale deployment of technology and difficulties of a transition at large scales and over long time periods. Assignments will focus on building models of key aspects of the energy transition, including global, regional and sectoral energy demand and emissions as well as economics of change. Prerequisites: students should be comfortable with calculus and linear algebra (e.g. Math 20, Math 51) and be familiar with computer programming (e.g. CS106A, CS106B). We will use the Python programming language to build our models.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR

ENGLISH 184E: Literary Text Mining

This course will train students in applied methods for computationally analyzing texts for humanities research. The skills students will gain will include basic programming for textual analysis, applied statistical evaluation of results and the ability to present these results within a formal research paper or presentation. Students in the course will also learn the prerequisite steps of such an analysis including corpus selection and cleaning, metadata collection, and selecting and creating an appropriate visualization for the results. This class is enrollment by permission only. To request a spot in the class, please fill out the survey: https://stanforduniversity.qualtrics.com/jfe/form/SV_6PrXGyFeo7g5eNU
Terms: Aut | Units: 5 | UG Reqs: GER:DB-Hum, WAY-AQR
Instructors: ; Sherman, A. (PI)

ENGLISH 184F: Literary Text Mining 2: Studies in Cultural Analytics

In this course, students will learn how to apply quantitative and computational methods for analyzing text to questions that are of significance to Literary Studies, and the humanities more broadly. Beginning with a series of readings and discussions on the theoretical implications of using quantitative methods for literary analysis, we will move to in-depth instruction in more advanced methods for computational text analysis, including topic models, word embeddings, and large language models. Students will not only become familiar with training and querying these models, but, more importantly, will gain hands-on experience in how to build these analytical techniques into humanities-based research.
Terms: Win | Units: 3-5 | UG Reqs: WAY-AQR, WAY-FR
Instructors: ; Algee-Hewitt, M. (PI)

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: Sum | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR
Instructors: ; Cappelli, M. (PI)

ENGR 14: Intro to Solid Mechanics

Introduction to engineering analysis using the principles of engineering solid mechanics. Builds on the math and physical reasoning concepts in Physics 41 to develop skills in evaluation of engineered systems across a variety of fields. Foundational ideas for more advanced solid mechanics courses such as ME80 or CEE101A. Interactive lecture sessions focused on mathematical application of key concepts, with weekly complementary lab session on testing and designing systems that embody these concepts. Limited enrollment, subject to instructor approval. Pre-requisite: Physics 41. When signing up for this course make sure to sign up both for the lecture and for a Discussion Section.
Terms: Aut, Win, Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

ENGR 20: Introduction to Chemical Engineering (CHEMENG 20)

Overview of chemical engineering through discussion and engineering analysis of physical and chemical processes. Topics: overall staged separations, material and energy balances, concepts of rate processes, energy and mass transport, and kinetics of chemical reactions. Applications of these concepts to areas of current technological importance: biotechnology, energy, production of chemicals, materials processing, and purification. Prerequisite: CHEM 31.
Terms: Win | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

ENGR 40A: Introductory Electronics

Instruction will be completed in the first seven weeks of the quarter. Students not majoring in Electrical Engineering may choose to take only ENGR 40A; Electrical Engineering majors should take both ENGR 40A and ENGR 40B. Overview of electronic circuits and applications. Electrical quantities and their measurement, including operation of the oscilloscope. Basic models of electronic components including resistors, capacitors, inductors, and operational amplifiers. Lab. Lab assignments. Enrollment limited to 300.
Terms: Sum | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

ENGR 42: Introduction to Electromagnetics and Its Applications (EE 42)

Electricity and magnetism and its essential role in modern electrical engineering devices and systems, such as sensors, displays, DVD players, and optical communication systems. The topics that will be covered include electrostatics, magnetostatics, Maxwell's equations, one-dimensional wave equation, electromagnetic waves, transmission lines, and one-dimensional resonators. Pre-requisites: none.
Terms: Spr, Sum | Units: 5 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

ENGR 50: Introduction to Materials Science, Nanotechnology Emphasis

The structure, bonding, and atomic arrangements in materials leading to their properties and applications. Topics include electronic and mechanical behavior, emphasizing nanotechnology, solid state devices, and advanced structural and composite materials.
Terms: Spr | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

ENGR 50M: Introduction to Materials Science, Biomaterials Emphasis

Topics include: the relationship between atomic structure and macroscopic properties of man-made and natural materials; mechanical and thermodynamic behavior of surgical implants including alloys, ceramics, and polymers; and materials selection for biotechnology applications such as contact lenses, artificial joints, and cardiovascular stents. No prerequisite.
Terms: Aut | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

ENGR 55: Foundational Biology for Engineers (CHEMENG 55)

Biology, physics, and chemistry are the substrates for the modern engineer. Whether you are interested in developing the next generation of medicines or would like the next material or catalyst you design to be inspired by solutions found in Nature, this course will deepen your knowledge of the foundational concepts in biology and enrich your engineering skills. We will introduce the physical principles that underlie the construction and function of living cells, the fundamental building block of life. Emphasis will be on systems, logic, quantitation, and mechanisms of the molecular processes utilized by all life on Earth. This course has no prerequisites, but prior completion of CHEM 31 or equivalent is highly recommended.
Terms: Aut | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

ENGR 62: Introduction to Optimization (MS&E 111, MS&E 211)

Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

ENGR 76: Information Science and Engineering

What is information? How can we measure and efficiently represent it? How can we reliably communicate and store it over media prone to noise and errors? How can we make sound decisions based on partial and noisy information? This course introduces the basic notions required to address these questions, as well as the principles and techniques underlying the design of modern information, communication, and decision-making systems with relations to and applications in machine-learning, through genomics, to neuroscience. Students will get a hands-on appreciation of the concepts via projects in small groups, where they will develop their own systems for streaming of multi-media data under human-centric performance criteria. Prerequisite: CS 106A.
Terms: Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-FR

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 | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

ENGR 108: Introduction to Matrix Methods

Formerly EE 103/CME 103. Introduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis. Clustering and the k-means algorithm. Matrices, left and right inverses, QR factorization. Least-squares and model fitting, regularization and cross-validation. Constrained and nonlinear least-squares. Applications include time-series prediction, tomography, optimal control, and portfolio optimization. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites:MATH 51 or CME 100, and basic knowledge of computing (CS 106A is more than enough, and can be taken concurrently). ENGR 108 and Math 104 cover complementary topics in applied linear algebra. The focus of ENGR 108 is on a few linear algebra concepts, and many applications; the focus of Math 104 is on algorithms and concepts.
Terms: Aut, Sum | Units: 3-5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

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. Numerical simulation using Monte Carlo techniques. Topics in mathematical statistics: random sampling, point estimation, confidence intervals, hypothesis testing, non-parametric tests, regression and correlation analyses. Numerous applications in engineering, manufacturing, reliability and quality assurance, medicine, biology, and other fields. Prerequisite: CME100/ENGR154 or Math 51 or 52.
Terms: Win, Sum | Units: 4 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

EPS 1: Introduction to Geology (EARTHSYS 11)

(Former GEOLSCI 1) Why are earthquakes, volcanoes, and natural resources located at specific spots on the Earth's 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, hands-on laboratories, in-class activities, and one virtual 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. Change of Department Name: Earth & Planetary Sciences (Formerly Geological Science)
Terms: Spr | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

EPS 6: Introduction to Data Science for Geoscience (EARTHSYS 100A)

(Formerly GEOLSCI 6) This course provides an overview of the most relevant areas of data science to address geoscientific challenges and questions as they pertain to the environment, earth resources & hazards. The focus lies on the methods that treat common characters of geoscientific data: multivariate, multi-scale, compositional, geospatial and space-time. In addition, the course will treat those statistical method that allow a quantification of the human dimension by looking at quantifying impact on humans (e.g. hazards, contamination) and how humans impact the environment (e.g. contamination, land use). The course focuses on developing skills that are not covered in traditional statistics and machine learning courses. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).
Terms: Win | Units: 3 | UG Reqs: WAY-AQR | Repeatable 3 times (up to 9 units total)

EPS 42: Moving and Shaking in the Bay Area

(Formerly GEOLSCI 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. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).
| Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

EPS 59N: Earthquake 9.0: The Heritage of Fukushima Daiichi 6 Years Later

(Formerly GEOLSCI 59N) We will consider the case for nuclear power as an energy source through the lens of the Fukushima disaster. Specific topics will include the cause of the earthquake and tsunami, the causes for the nuclear power plant failure, the mechanisms for the release of radioactivity at the time of the accident and today, and the ongoing human impact of this tragedy. In addition to the details of the accident and the release of radioactivity, class discussions and readings will explore the health and economic impacts of nuclear power and examine how the accident has affected the future prospects of nuclear power in Japan, the U.S., and around the world. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).
| Units: 3 | UG Reqs: WAY-AQR

ESS 141: Remote Sensing of the Oceans (EARTHSYS 141, EARTHSYS 241, ESS 241, GEOPHYS 141)

How to observe and interpret physical and biological changes in the oceans using satellite technologies. Topics: principles of satellite remote sensing, classes of satellite remote sensors, converting radiometric data into biological and physical quantities, sensor calibration and validation, interpreting large-scale oceanographic features.
Terms: Win | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR
Instructors: ; Arrigo, K. (PI)

ESS 152: Marine Chemistry (EARTHSYS 152, EARTHSYS 252, ESS 252, OCEANS 152, OCEANS 252)

Introduction to the interdisciplinary knowledge and skills required to critically evaluate problems in marine chemistry and related disciplines. Physical, chemical, and biological processes that determine the chemical composition of seawater. Air-sea gas exchange, carbonate chemistry, and chemical equilibria, nutrient and trace element cycling, particle reactivity, sediment chemistry, and diagenesis. Examination of chemical tracers of mixing and circulation and feedbacks of ocean processes on atmospheric chemistry and climate. Designed to be taken concurrently with Biological Oceanography (ESS/EARTHSYS 151/251)
Last offered: Spring 2023 | Units: 3-4 | UG Reqs: WAY-AQR, WAY-SMA

ESS 162: Remote Sensing of Land (EARTHSYS 142, EARTHSYS 242, ESS 262)

The use of satellite remote sensing to monitor land use and land cover, with emphasis on terrestrial changes. Topics include pre-processing data, biophysical properties of vegetation observable by satellite, accuracy assessment of maps derived from remote sensing, and methodologies to detect changes such as urbanization, deforestation, vegetation health, and wildfires.
Terms: Spr | Units: 3 | UG Reqs: WAY-AQR

ESS 164: Fundamentals of Geographic Information Science (GIS) (EARTHSYS 144)

Everything is somewhere, and that somewhere matters." The rapid growth and maturity of spatial data technologies over the past decade represent a paradigm shift in the applied use of location data from high-level overviews of administrative interests, to highly personalized location-based services that place the individual at the center of the map, at all times. The use of spatial data and related technology continues to grow in fields ranging from environmental sciences to epidemiology to market prediction. This course will present an overview of current approaches to the use of spatial data and its creation, capture, management, analysis and presentation, in a research context. Topics will include modeling of geographic objects and associated data, modeling of geographic space and the conceptual foundations of "spatial thinking," field data collection, basic spatial statistical analysis, remote sensing & the use of satellite-based imagery, "Big Data" and machine learning approaches to spatial data, and cartographic design and presentation including the use of web-based "Storymap" platforms. The course will consist of weekly lectures, guest speakers, computer lab assignments, midterm and final exams, as well as an individual final project requirement. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit.
Terms: Aut | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR

ESS 241: Remote Sensing of the Oceans (EARTHSYS 141, EARTHSYS 241, ESS 141, GEOPHYS 141)

How to observe and interpret physical and biological changes in the oceans using satellite technologies. Topics: principles of satellite remote sensing, classes of satellite remote sensors, converting radiometric data into biological and physical quantities, sensor calibration and validation, interpreting large-scale oceanographic features.
Terms: Win | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR
Instructors: ; Arrigo, K. (PI)

FEMGEN 141P: Political Economy of Development (POLISCI 141)

The last few decades have seen remarkable progress in lifting millions of people out of poverty worldwide. Yet, millions still are unable to meet even their most basic sustenance needs. This course examines foundational reasons for why some countries remain poor and why inequality persists today. In addition to answering the why question, we will also examine how practitioners, policy-makers, and academics have tackled global development challenges, where they have met success, and where failure has provided key lessons for the future. The course will examine how social, political, and economic institutions affect prospects for development, including by covering issues of colonialism and contemporary foreign aid. Students will learn about and explore patterns of development across the world, critically evaluate foundational theories of development, and understand the practical challenges and possible solutions to reducing poverty, creating equality, and ensuring good governance. Course assignments will aim to have students practice linking data and evidence with policy innovation, using global datasets to perform statistical analyses. Students will leave this class with an understanding of how development works (and does not work) in practice.
| Units: 5 | UG Reqs: WAY-AQR, WAY-SI

GEOPHYS 20N: How to Predict a Super Eruption

The physics and chemistry of volcanic processes and modern methods of volcano monitoring. Volcanoes as manifestations of the Earth's internal energy and hazards to society. How earth scientists better forecast eruptive activity by monitoring seismic activity, bulging of the ground surface, and the discharge of volcanic gases, and by studying deposits from past eruptions. Focus is on the interface between scientists and policy makers and the challenges of decision making with incomplete information. Field trip to Mt. St. Helens, site of the 1980 eruption.
Terms: Win | Units: 3 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA
Instructors: ; Segall, P. (PI)

GEOPHYS 90: Earthquakes and Volcanoes (EARTHSYS 113)

Is the "Big One" overdue in California? What kind of damage would that cause? What can we do to reduce the impact of such hazards in urban environments? Does "fracking" cause earthquakes and are we at risk? Is the United States vulnerable to a giant tsunami? The geologic record contains evidence of volcanic super eruptions throughout Earth's history. What causes these gigantic explosive eruptions, and can they be predicted in the future? This course will address these and related issues. For non-majors and potential Earth scientists. No prerequisites. More information at: https://stanford.box.com/s/zr8ar28efmuo5wtlj6gj2jbxle76r4lu
Terms: Spr | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
Instructors: ; Beroza, G. (PI)

GEOPHYS 104: The Water Course (EARTHSYS 104, EARTHSYS 204, GEOPHYS 204)

The Central Valley of California provides a third of the produce grown in the U.S., but recent droughts and increasing demand have raised concerns about both food and water security. The pathway that water takes from rainfall to the irrigation of fields or household taps ('the water course') determines the quantity and quality of the available water. Working with various data sources (measurements made on the ground, in wells, and from satellites) allows us to model the water budget in the valley and explore the recent impacts on freshwater supplies.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

GEOPHYS 110: Introduction to the Foundations of Contemporary Geophysics (EARTHSYS 110, GEOPHYS 215)

Introduction to the foundations of contemporary geophysics. Lectures link important topics in contemporary Geophysics ("What we study") to methods used to make progress on these topics ("How we study"). Topics range from plate tectonics to natural hazards; ice sheets to sustainability. For each topic, we focus is on how the interpretation of geophysical measurements (e.g., gravity, seismology, heat flow, electromagnetism and remote sensing) provides fundamental insight into the behavior of the Earth. The course will includes a required all-day Saturday field exercise Feb 02/10 (rain-date: 02/17). Prerequisite: CME 100 or MATH 51, or co-registration in either.
Terms: Win | Units: 3 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

GEOPHYS 115: Taking the Pulse of the Planet (EE 115)

Grappling with the big questions of sustainability and climate change, requires that we have ways to measure ? as we cannot manage what we cannot measure. This course, Taking the Pulse of the Planet introduces a new research and teaching initiative at Stanford ? also called Taking the Pulse of the Planet, which has the following goal: to have in place a global network of satellite, airborne, land/water-based sensors to support the real-time adaptive management of planetary health and human activities. Measurements will be made at the spatial and temporal scales required to inform the development and implementation of new policies addressing critical issues related to climate change, sustainability, and equity. Tapping into rapid advancements in sensor technology and data science over the past decade, we can now image and monitor many components of the Earth system and human activities. With the launch of the Stanford Doerr School of Sustainability, we wish to celebrate, through this course, the powerful role that advancements in technology ? specifically sensors ? and advancements in data science are playing in addressing the global challenges in sustainability and climate change. This will be a lecture class for undergraduates and graduate students designed to introduce them to the incredible array of sensors and data sets now available. We will finish the quarter with group projects that will involve the making and deployment of sensors around campus. The course will be designed to accommodate students at any level, with any background, with no required pre-requisites. In most of the assignments, we will be using Google co-lab to work with various types of sensor data. We anticipate drawing to this course both data-science-savvy and data-science-interested students. Therefore, we have developed online modules that are designed to help any student get up to speed on the "jargon" and the computational approaches used in the class.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

GEOPHYS 130: Introductory Seismology

Introduction to seismology including: elasticity and the wave equation, P, S, and surface waves, dispersion, ray theory, reflection and transmission of seismic waves, seismic imaging, large-scale Earth structure, earthquake location, earthquake statistics and forecasting, magnitude scales, seismic source theory.
Last offered: Autumn 2022 | Units: 3 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

GEOPHYS 141: Remote Sensing of the Oceans (EARTHSYS 141, EARTHSYS 241, ESS 141, ESS 241)

How to observe and interpret physical and biological changes in the oceans using satellite technologies. Topics: principles of satellite remote sensing, classes of satellite remote sensors, converting radiometric data into biological and physical quantities, sensor calibration and validation, interpreting large-scale oceanographic features.
Last offered: Winter 2023 | Units: 3-4 | UG Reqs: GER: DB-NatSci, WAY-AQR

GEOPHYS 148: Machine Learning and the Physical Sciences (CME 215, GEOPHYS 248)

This course provides a survey of the rapidly growing field of machine learning in the physical sciences. It covers various areas such as inverse problems, emulating physical processes, model discovery given data, and solution discovery given equations. It both introduces the background knowledge required to implement physics-informed deep learning and provides practical in-class coding exercises. Students have the opportunity to apply this emerging methodology to their own research interests across all fields of the physical sciences, including geophysics, climate, fluids, or other systems where the same technique applies. Students develop individual projects throughout the semester. Recommended Prerequisite: Calculus (e.g. Math 21), Differential Equations (e.g. MATH 53 or PHYSICS 111) or equivalents.
Terms: Spr | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA
Instructors: ; Lai, C. (PI)

HUMBIO 51: Big Data for Biologists - Decoding Genomic Function

Biology and medicine are becoming increasingly data-intensive 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 in-class 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 large-scale 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 university-level courses in biology and programming experience are not required.
Last offered: Winter 2023 | Units: 3 | UG Reqs: WAY-AQR

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 health-related data, including linear and logistic regression, and will become sophisticated consumers of health-related statistical results.
Terms: Win | Units: 4 | UG Reqs: GER:DB-Math, WAY-AQR

HUMBIO 89: Introduction to Health Sciences Statistics

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. Cardinal Course certified by the Haas Center.
Terms: Aut, Win | Units: 3 | UG Reqs: GER:DB-Math, WAY-AQR

HUMBIO 89X: Introduction to Probability and Statistics for Epidemiology (EPI 259)

(HUMBIO students must enroll in HUMBIO 89X. Med/Graduate students must enroll in EPI 259.) Topics: random variables, expectation, variance, probability distributions, the central limit theorem, sampling theory, hypothesis testing, confidence intervals. Correlation, regression, analysis of variance, and nonparametric tests. Introduction to least squares and maximum likelihood estimation. Emphasis is on medical applications.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

HUMBIO 114: Global Change and Emerging Infectious Disease (EARTHSYS 114, EARTHSYS 214, ESS 213)

The changing epidemiological environment. How human-induced environmental changes, such as global warming, deforestation and land-use conversion, urbanization, international commerce, and human migration, are altering the ecology of infectious disease transmission, and promoting their re-emergence as a global public health threat. Case studies of malaria, cholera, hantavirus, plague, and HIV.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SMA
Instructors: ; Jones, J. (PI)

HUMBIO 154B: Principles of Epidemiology

Epidemiology is the study of the distribution and determinants of health and disease in human populations. In this course, students will learn about design, measures of disease occurrence and measures of association between exposures - be they environmental, behavioral or genetic - and health outcomes of interest. Students will also learn about how error, confounding and bias can impact epidemiological results. The course draws on both classic and contemporary research articles, which students will learn to critically appraise. Through lectures, problem sets, written responses to original articles and in-class discussions, students will gain a solid foundation in epidemiology. HUMBIO 154 courses can be taken separately or as a series. Upper division course with preference given to upperclassmen.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR
Instructors: ; Kurina, L. (PI); Yang, N. (TA)

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 cross-sectional 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. HUMBIO 154 courses can be taken separately or as a series. Prerequisites: Human Biology core or Biology Foundations or instructor consent.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR
Instructors: ; Fisher, P. (PI); Chen, C. (TA)

LINGUIST 180: From Languages to Information (CS 124, LINGUIST 280)

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites: CS106B, Python (at the level of CS106A), CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to CS107 (or taking CS107 or CS1U concurrently).
Terms: Win | Units: 3-4 | UG Reqs: WAY-AQR

MATSCI 156: Solar Cells, Fuel Cells, and Batteries: Materials for the Energy Solution

This course introduces students to emerging technological solutions to address the pressing energy demands of the world. It is motivated by discussions of the scale of global energy usage and requirements for possible solutions; however, the primary focus will be on the fundamental physics and chemistry of solar cells, fuel cells, and batteries from a materials science perspective. Students will learn about operating principles and performance, economic, and ethical considerations from the ideal device to the installed system. The promise of materials research for providing next generation solutions will be highlighted by guest speakers developing innovative energy technologies. Undergraduates register in 156 for 4 units; graduates register in 256 for 3 units. Prerequisites: Undergraduate coursework in thermodynamics (e.g., MATSCI 144, ME 30) and electromagnetism (e.g., PHYSICS 23/43).
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR
Instructors: ; Chen, E. (PI)

MATSCI 158: Soft Matter in Biomedical Devices, Microelectronics, and Everyday Life (BIOE 158)

The relationships between molecular structure, morphology, and the unique physical, chemical, and mechanical behavior of polymers and other types of soft matter are discussed. Topics include methods for preparing synthetic polymers and examination of how enthalpy and entropy determine conformation, solubility, mechanical behavior, microphase separation, crystallinity, glass transitions, elasticity, and linear viscoelasticity. Case studies covering polymers in biomedical devices and microelectronics will be covered. Recommended: ENGR 50 and Chem 31A or equivalent.
Last offered: Winter 2020 | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

MATSCI 162: X-Ray Diffraction Laboratory (MATSCI 172, PHOTON 172)

Experimental x-ray diffraction techniques for microstructural analysis of materials, emphasizing powder and single-crystal techniques. Diffraction from epitaxial and polycrystalline thin films, multilayers, and amorphorous materials using medium and high resolution configurations. Determination of phase purity, crystallinity, relaxation, stress, and texture in the materials. Advanced experimental x-ray diffraction techniques: reciprocal lattice mapping, reflectivity, and grazing incidence diffraction. Enrollment limited to 20. Undergraduates register for 162 for 4 units; graduates register for 172 for 3 units. Prerequisites: MATSCI 143 or equivalent course in materials characterization.
Terms: Win | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

MATSCI 190: Organic and Biological Materials (MATSCI 210)

Unique physical and chemical properties of organic materials and their uses. The relationship between structure and physical properties, and techniques to determine chemical structure and molecular ordering. Examples include liquid crystals, dendrimers, carbon nanotubes, hydrogels, and biopolymers such as lipids, protein, and DNA. Prerequisite: Thermodynamics and ENGR 50 or equivalent. Undergraduates register for 190 for 4 units; graduates register for 210 for 3 units.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA
Instructors: ; Appel, E. (PI)

ME 1: Introduction to Mechanical Engineering

This course is intended to be the starting point for Mechanical Engineering majors. It will cover the concepts, engineering methods, and common tools used by mechanical engineers while introducing the students to a few interesting devices. We will discuss how each device was conceived, design challenges that arose, application of analytical tools to the design, and production methods. Main class sections will include lectures, demonstrations, and in-class group exercises. Lab sections will develop specific skills in freehand sketching and computational modeling of engineering systems. Prerequisites: Physics: Mechanics, and first quarter Calculus.
Terms: Aut, Spr | Units: 3 | UG Reqs: WAY-AQR

ME 30: Engineering Thermodynamics

The basic principles of thermodynamics are introduced in this course. Concepts of energy and entropy from elementary considerations of the microscopic nature of matter are discussed. The principles are applied in thermodynamic analyses directed towards understanding the performances of engineering systems. Methods and problems cover socially responsible economic generation and utilization of energy in central power generation plants, solar systems, refrigeration devices, and automobile, jet and gas-turbine engines.
Terms: Aut, Win, Spr | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

ME 80: Mechanics of Materials

Mechanics of materials and deformation of structural members. Topics include stress and deformation analysis under axial loading, torsion and bending, column buckling and pressure vessels. Introduction to stress transformation and multiaxial loading. Prerequisite: ENGR 14.
Terms: Aut, Win | Units: 3 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

MS&E 111: Introduction to Optimization (ENGR 62, MS&E 211)

Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite: CME 100 or MATH 51.
Terms: Aut | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR

MS&E 111X: Introduction to Optimization (Accelerated) (MS&E 211X)

Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods: simplex and interior-point, gradient, Newton, and barrier. Prerequisite: CME 100 or MATH 51 or equivalent.
Terms: Spr | Units: 3-4 | UG Reqs: WAY-AQR

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 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 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-AQR, WAY-SI

MS&E 152: Introduction to Decision Analysis

How to make good decisions in a complex, dynamic, and uncertain world. People often make decisions that on close examination they regard as wrong. Decision analysis uses a structured conversation based on actional thought to obtain clarity of action in a wide variety of domains. Topics: distinctions, possibilities and probabilities, relevance, value of information and experimentation, relevance and decision diagrams, risk attitude. Prerequisites: high school algebra and basic spreadsheet skills.
Terms: Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

OCEANS 14H: Bio-logging and Bio-telemetry

Bio-logging is a rapidly growing discipline that includes diverse fields such as consumer electronics, medicine, and marine biology. The use of animal-attached digital tags is a powerful approach to study the movement and ecology of individuals over a wide range of temporal and spatial scales. This course is an introduction to bio-logging methods and analysis. Using whales as a model system, students will learn how use multi-sensor tags to study behavioral biomechanics. Course taught in-person only at Hopkins Marine Station; for information on how to spend spring quarter in residence: https://hopkinsmarinestation.stanford.edu/undergraduate-studies/spring-courses-23-24 (Individual course registration also permitted.) Depending on enrollment numbers, a weekly shuttle to Hopkins or mileage reimbursements for qualifying carpools will be provided; terms and conditions apply.
Terms: Spr | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

OCEANS 143: Quantitative Methods for Marine Ecology and Conservation (BIO 143, BIO 243, CEE 164, CEE 264H, EARTHSYS 143H, EARTHSYS 243H)

NOTE: This course will be taught in-person on main campus, in hybrid format with Zoom options. The goal of this course is to learn the foundations of ecological modeling with a specific (but not exclusive) focus on marine conservation and sustainable exploitation of renewable resources. Students will be introduced to a range of methods - from basic to advanced - to characterize population structure, conduct demographic analyses, estimate extinction risk, identify temporal trends and spatial patterns, quantify the effect of environmental determinants and anthropogenic pressures on the dynamics of marine populations, describe the potential for adaptation to climate change. This course will emphasize learning by doing, and will rely heavily on practical computer laboratories, in R and/or Phyton, based on data from our own research activities or peer reviewed publications. Students with a background knowledge of statistics, programming and calculus will be most welcome. Formally BIOHOPK 143H and 243H.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR, WAY-FR

OCEANS 152: Marine Chemistry (EARTHSYS 152, EARTHSYS 252, ESS 152, ESS 252, OCEANS 252)

Introduction to the interdisciplinary knowledge and skills required to critically evaluate problems in marine chemistry and related disciplines. Physical, chemical, and biological processes that determine the chemical composition of seawater. Air-sea gas exchange, carbonate chemistry, and chemical equilibria, nutrient and trace element cycling, particle reactivity, sediment chemistry, and diagenesis. Examination of chemical tracers of mixing and circulation and feedbacks of ocean processes on atmospheric chemistry and climate. Designed to be taken concurrently with Biological Oceanography (ESS/EARTHSYS 151/251)
| Units: 3-4 | UG Reqs: WAY-AQR, WAY-SMA

OCEANS 174H: Experimental Design and Probability (OCEANS 274H)

Nature is inherently variable. Statistics gives us the tools to quantify the uncertainty of our measurements and draw conclusions from data. This course is an introduction to experimental design, probability, and data analysis. Topics include summary statistics, data visualization, probability distributions, statistical inference, and general linear models (e.g., t-tests, analysis of variance, regression). Students will use R to explore and analyze datasets relevant to the life and ocean sciences. No programming or statistical background is assumed. This course takes place in-person only at Hopkins Marine Station; for information on how to spend spring quarter in residence: https://hopkinsmarinestation.stanford.edu/undergraduate-studies/spring-courses-23-24 (Individual course registration also permitted.) Depending on enrollment numbers, a weekly shuttle to Hopkins or mileage reimbursements for qualifying carpools will be provided; terms and conditions apply. Graduate students register for OCEANS 274H.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, GER:DB-Math, WAY-AQR, WAY-FR

OSPBER 50M: Introductory Science of Materials

Topics include: the relationship between atomic structure and macroscopic properties of man-made and natural materials; mechanical and thermodynamic behavior of surgical implants including alloys, ceramics, and polymers; and materials selection for biotechnology applications such as contact lenses, artificial joints, and cardiovascular stents. No prerequisite.
Last offered: Winter 2023 | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

OSPFLOR 27: Pasta, Soccer, and Opera: A Sampler of Economics and Data Analysis in Italy

The goal of this course is to introduce students to fun, real-world and sometimes surprising economic phenomena with modern and historical Italy as the centerpiece. Examples include the game theory of penalty kicks in Italian soccer, the salt monopoly that led to Tuscany's famous salt-less bread, the causal effect of BBC radio on Italian resistance efforts during World War II, the economic effects of the Mafia, and the effects of Napoleon's victories on Italian opera quality (through adoption of copyright laws). Students will learn basic economic concepts and econometric tools, as well as a basic introduction to coding in R.
Last offered: Autumn 2022 | Units: 3 | UG Reqs: WAY-AQR, WAY-SI

OSPFLOR 50M: Introductory Science of Materials

Topics include: the relationship between atomic structure and macroscopic properties of man-made and natural materials; mechanical and thermodynamic behavior of surgical implants including alloys, ceramics, and polymers; and materials selection for biotechnology applications such as contact lenses, artificial joints, and cardiovascular stents. No prerequisite.
Last offered: Winter 2023 | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

OSPISTAN 20: Dealing with Data

Dealing with data is a fundamental skill for the modern world. In this course, we will learn two broad dominant paradigms for how to deal with data. The first way deals with extracting causal relationships from data. The second way focuses on summarizing and describing patterns in data. Building from first principles, we will explain the basic ideas and concepts behind each of these approaches, and explore how they relate to each other. Throughout the course, we will see plenty of examples from across computer science, engineering and economics.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

OSPMADRD 70: Computational Biology: Structure of Biomolecules

All life depends on molecular machines, such as proteins, which carry out a huge variety of tasks within the cell. Much like the larger machines we encounter in our day-to-day lives, these miniature machines work because each has a three-dimensional structure and moving parts specialized to its particular function. Computation plays an increasingly crucial role in revealing the structures and motions of biomolecules, and in using that information to discover drugs, improve medical treatment, and engineer new biomolecules for use in food and energy production. Important computational approaches in this field range from physical simulation to machine learning. This course offers a gentle introduction to these computational methods and their practical applications.
Last offered: Winter 2023 | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA

OSPOXFRD 48: Causality, Counterfactuals and AI

The ability to reason about what might have been is one of the most central aspects of intelligence, and is a key part of what enables people to generalize from prior experience to inform their future decisions. This issue has captivated multiple communities and also is central to areas from healthcare to economics. In this course we will introduce the dominant approaches in machine learning and AI, with also reference to statistics and econometrics. Classes will combine lectures and discussions. Assignments will involve reasoning about the alternate frameworks and the questions they can address, using presented approaches to infer treatment effects in existing datasets, and essays arguing in favor of one of the particular frameworks for causal and counterfactual reasoning.
Last offered: Spring 2022 | Units: 4-5 | UG Reqs: WAY-AQR

OSPOXFRD 86: From the hills to the sea

This course would focus on the Thames River, at least since Roman times arguably the most important waterway in Britain. The basis of the class would be an exploration of the Thames from different angles both scientific and historical. The science side of the course would consider the following topics: the geology/geographic setting that gave rise to the Thames; its hydrology including a history of its floods and droughts as well as climate change trends; aspects of the hydrodynamics of tides and the estuarine environment of the Thames; the effects on the Thames of human modification such as loss of wetlands associated with building of the Docklands in the 18th and 19th centuries; sea level rise and the Thames including the design basis of the Thames Tidal Barrier. The history side of the course would consider how the Thames has played a role in the history of Britain, e.g., as an inland transportation corridor, as a barrier between states, as the site of the signing of the Magna Carta, as the heart of the global trade enterprise that built the British Empire, as a challenge to important engineering feats in Victorian London, as a subject for landscape painters like Turner, and as a spur of public policies of environmental protection and restoration.
Terms: Aut | Units: 4-5 | UG Reqs: WAY-AQR

OSPPARIS 50M: Introductory Science of Materials

Topics include: the relationship between atomic structure and macroscopic properties of man-made and natural materials; mechanical and thermodynamic behavior of surgical implants including alloys, ceramics, and polymers; and materials selection for biotechnology applications such as contact lenses, artificial joints, and cardiovascular stents. No prerequisite.
Last offered: Winter 2022 | Units: 4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-SMA

OSPSANTG 25: Health and Disease in an Aging Society: Chile in Transition

Chile is in the advanced stages of demographic and epidemiologic transition, and has the longest life expectancy of any South American country. This course will discuss the social impact of an aging society as well as factors that support or hinder health in an aging population. We will use the socio-ecologic model of public health to better understand the complex factors that support health and longevity. Additionally, students will learn epidemiologic methods for measurement and investigation and gain applied experience analyzing data. Language of instruction: English.
Terms: Spr | Units: 3 | UG Reqs: WAY-AQR
Instructors: ; Odden, M. (PI)

OSPSANTG 63: Entrepreneurship and Innovation in Latin America

Using the Chilean experience, study challenges and opportunities arising from the Latin American entrepreneurship and innovation eco-system. Sectors covered include technology, services, marketplaces, retail, natural resources, energy, biotech, and social innovation. Also the public policy environment as well as the funding one. Guest speakers include entrepreneurs, investors, academics, and policy makers.
Last offered: Autumn 2022 | Units: 4-5 | UG Reqs: WAY-AQR

PHYSICS 50: Astronomy Laboratory and Observational Astronomy

Introduction to observational astronomy emphasizing the use of optical telescopes. Observations of stars, nebulae, and galaxies in laboratory sessions with telescopes at the Stanford Student Observatory. Meets at the observatory one evening per week from dusk until well after dark, in addition to day-time lectures each week. No previous physics required. Limited enrollment.
Last offered: Summer 2019 | Units: 3 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

PHYSICS 100: Introduction to Observational Astrophysics

Designed for undergraduate physics majors but is open to all students with a calculus-based physics background and some laboratory and coding experience. Students make and analyze observations using the telescopes at the Stanford Student Observatory. Topics covered include navigating the night sky, the physics of stars and galaxies, telescope instrumentation and operation, imaging techniques, quantitative error analysis, and effective scientific communication. The course concludes with an independent project where student teams propose and execute an observational astronomy project of their choosing, using techniques learned in class to gather and analyze their data, and presenting their findings in the forms of professional-style oral presentations and research papers. Suggested preparation: Physics 89L. Enrollment by permission. Due to physical limitations at the observatory, this class has a firm enrollment cap. We may not be able to accommodate all requests to enroll. Before permission numbers are given students must complete this form: https://forms.gle/KDarBRcZWJZG3qr66.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

PHYSICS 104: Electronics and Introduction to Experimental Methods

Introductory laboratory electronics, intended for Physics and Engineering Physics majors but open to all students with science or engineering interests in analog circuits, instrumentation, and signal processing. The first part of the course is focused on hands-on exercises that build skills needed for measurements, including input/output impedance concepts, filters, amplifiers, sensors, and fundamentals of noise in physical systems. Lab exercises include DC circuits, RC and diode circuits, applications of operational amplifiers, optoelectronics, synchronous detection, and noise in measurements. The second portion of the class is an instrumentation design project, where essential instrumentation for a practical lab measurement is designed, constructed, and applied for an experiment. Example measurements can include temperature measurement in a cryostat, resistivity measurement of a superconducting material, measurement of the 2-D position of an optical beam, development of a high impedance ion probe and clamp for neuroscience, or other projects of personal interest. The course focuses on practical techniques and insight from the lab exercises, with the goal of preparing undergraduates for laboratory research. No formal electronics experience is required beyond exposure to concepts from introductory Physics or Engineering courses (Ohm's law, charge conservation, physics of capacitors and inductors, etc.). Students who have previously taken Physics 105 should not enroll in this course due to significant overlap. Recommended prerequisite: (Physics 43 and 44) OR (Physics 81 (formerly Physics 63) and 89L (formerly Physics 67), OR (Engineering 40A or 40M).
Terms: Aut | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA
Instructors: ; Fox, J. (PI)

PHYSICS 105: Intermediate Physics Laboratory I: Analog Electronics

Introductory laboratory electronics, designed for Physics and Engineering Physics majors but open to all students with science or engineering interests in analog circuits, instrumentation and signal processing. The course is focused on laboratory exercises that build skills needed for measurements, including sensors, amplification and filtering, and fundamentals of noise in physical systems. The hands-on lab exercises include DC circuits, RC and diode circuits, applications of operational amplifiers, non-linear circuits and optoelectronics. The class exercises build towards a lock-in amplifier contest where each lab section designs and builds a synchronous detection system to measure a weak optical signal, with opportunities to understand the limits of the design, build improvements and compare results with the other lab sections. The course focuses on practical techniques and insight from the lab exercises, with a goal to prepare undergraduates for laboratory research. No formal electronics experience is required beyond exposure to concepts from introductory Physics or Engineering courses (Ohm's law, charge conservation, physics of capacitors and inductors, etc.). Now offered as PHYSICS 104. Recommended prerequisite: Physics 43 or 63, or Engineering 40A or 40M.
Last offered: Autumn 2019 | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-SMA

PHYSICS 106: Experimental Methods in Quantum Physics

Experimental physics lab course aimed at providing an understanding of and appreciation for experimental methods in physics, including the capabilities and limitations, both fundamental and technical. Students perform experiments that use optics, lasers, and electronics to measure fundamental constants of nature, perform measurements at the atomic level, and analyze results. Goals include developing an understanding of measurement precision and accuracy through concepts of spectral-analysis of coherent signals combined with noise. We explore the fundamental limits to measurement set by thermal noise at finite temperature, as well as optical shot-noise in photo-detection that sets the standard quantum limit in detecting light. Spectroscopy of light emitted from atoms reveals the quantum nature of atomic energy levels, and when combined with theoretical models provides information on atomic structure and fundamental constants of nature (e.g. the fine structure constant that characterizes the strength of all electro-magnetic interactions, and the ratio of the electron mass to the proton mass, me/mp. Experiments may include laser spectroscopy to determine the interatomic potential, effective spring constant, and binding energy of a diatomic molecule, or measure the speed of light. This course will provide hands-on experience with semiconductor diode lasers, basic optics, propagation and detection of optical beams, and related electronics and laboratory instrumentation. For lab notebooks the class uses an integrated online environment for data analysis, curve fitting, (system is based on Jupyter notebooks, Python, and document preparation). Prerequisites: PHYSICS 40 series and PHYSICS 70, or 60 series, PHYSICS 120, PHYSICS 130; some familiarity with basic electronics is helpful but not required. Very basic programming in Python is needed, but background with Matlab, Origin, or similar software should be sufficient to come up to speed for the data analysis.
Terms: Win | Units: 4 | UG Reqs: WAY-AQR
Instructors: ; Hollberg, L. (PI)

PHYSICS 107: Intermediate Physics Laboratory II: Experimental Techniques and Data Analysis

Experiments on lasers, Gaussian optics, and atom-light interaction, with emphasis on data and error analysis techniques. Students describe a subset of experiments in scientific paper format. Prerequisites: completion of PHYSICS 40 or PHYSICS 60 series, and PHYSICS 70 and PHYSICS 105. Recommended pre- or corequisites: PHYSICS 120 and 130. WIM
Last offered: Winter 2020 | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA

PHYSICS 108: Advanced Physics Laboratory: Project

Have you ever wanted to dream up a research question, then design, execute, and analyze an experiment to address it, together with a small group of your fellow students? This is an accelerated, guided experimental research experience, resembling real frontier research. Phenomena that have been studied include the magnetization of ferromagnets, the quantum hall effect in graphene, interference in superconducting circuits, loss in nanomechanical resonators, and superfluidity in helium. But most projects pursued (drawn from condensed matter and recently also particle physics) have never been done in the class before. Our equipment and apparatus for Physics 108 are very flexible, and not standardized like in most other lab classes. We provide substantial resources to help your team. Often, with instructors' help, students obtain unique samples from Stanford research groups. Prerequisite: PHYSICS 104, or other experience in electronics. Suggested but less critical: Physics 130 (many phenomena you might study build on quantum mechanics) and Physics 106 (experience with data analysis and useful measurement tools: lock-in amplifier, spectrum analyzer.) We recommend taking this class in junior year if possible, as it can inform post-graduation decisions and can empower the professor to write a powerful letter of recommendation.
Terms: Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-SMA

PHYSICS 113: Computational Physics

Numerical methods for solving problems in mechanics, astrophysics, electromagnetism, quantum mechanics, and statistical mechanics. Methods include numerical integration; solutions of ordinary and partial differential equations; solutions of the diffusion equation, Laplace's equation, and Poisson's equation with various methods; statistical methods including Monte Carlo techniques; matrix methods and eigenvalue problems. A short introduction to Python, which is used for class examples and active learning notebooks. Independent class projects allow deep explorations of course topics and make up a significant component of the course grade. No prerequisites but some previous programming experience is advisable.
Terms: Spr | Units: 4 | UG Reqs: GER: DB-NatSci, WAY-AQR, WAY-FR

POLISCI 141: Political Economy of Development (FEMGEN 141P)

The last few decades have seen remarkable progress in lifting millions of people out of poverty worldwide. Yet, millions still are unable to meet even their most basic sustenance needs. This course examines foundational reasons for why some countries remain poor and why inequality persists today. In addition to answering the why question, we will also examine how practitioners, policy-makers, and academics have tackled global development challenges, where they have met success, and where failure has provided key lessons for the future. The course will examine how social, political, and economic institutions affect prospects for development, including by covering issues of colonialism and contemporary foreign aid. Students will learn about and explore patterns of development across the world, critically evaluate foundational theories of development, and understand the practical challenges and possible solutions to reducing poverty, creating equality, and ensuring good governance. Course assignments will aim to have students practice linking data and evidence with policy innovation, using global datasets to perform statistical analyses. Students will leave this class with an understanding of how development works (and does not work) in practice.
Last offered: Spring 2023 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

POLISCI 141A: Immigration and Multiculturalism (CSRE 141S)

What are the economic effects of immigration? Do immigrants assimilate into local culture? What drives native attitudes towards immigrants? Is diversity bad for local economies and societies and which policies work for managing diversity and multiculturalism? We will address these and similar questions by synthesizing the conclusions of a number of empirical studies on immigration and multiculturalism. The emphasis of the course is on the use of research design and statistical techniques that allow us to move beyond correlations and towards causal assessments of the effects of immigration and immigration policy.
Last offered: Winter 2022 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

POLISCI 150A: Data Science for Politics (POLISCI 355A)

Data science is quickly changing the way we understand and and engage in the political process. In this course we will develop fundamental techniques of data science and apply them to large political datasets on elections, campaign finance, lobbying, and more. The objective is to give students the skills to carry out cutting edge quantitative political studies in both academia and the private sector. Students with technical backgrounds looking to study politics quantitatively are encouraged to enroll.
Terms: Aut | Units: 3-5 | UG Reqs: WAY-AQR

POLISCI 150B: Machine Learning for Social Scientists (POLISCI 355B)

Machine learning - the use of algorithms to classify, predict, sort, learn and discover from data - has exploded in use across academic fields, industry, government, and the non-profit sector. This course provides an introduction to machine learning for social scientists. We will introduce state of the art machine learning tools, show how to use those tools in the programming language R, and demonstrate why a social science focus is essential to effectively apply machine learning techniques in social, political, and policy contexts. Applications of the methods will include forecasting social phenomena, evaluating the use of algorithms in public policy, and the analysis of social media and text data. Prerequisite: POLISCI 150A/355A.
Terms: Win | Units: 3-5 | UG Reqs: WAY-AQR

POLISCI 150C: Causal Inference for Social Science (POLISCI 355C)

Causal inference methods have revolutionized the way we use data, statistics, and research design to move from correlation to causation and rigorously learn about the impact of some potential cause (e.g., a new policy or intervention) on some outcome (e.g., election results, levels of violence, poverty). This course provides an introduction that teaches students the toolkit of modern causal inference methods as they are now widely used across academic fields, government, industry, and non-profits. Topics include experiments, matching, regression, sensitivity analysis, difference-in-differences, panel methods, instrumental variable estimation, and regression discontinuity designs. We will illustrate and apply the methods with examples drawn from various fields including policy evaluation, political science, public health, economics, business, and sociology. Prerequisite: POLISCI 150A.
Terms: Spr | Units: 3-5 | UG Reqs: WAY-AQR

POLISCI 151: Tackling Big Questions Using Social Data Science (ECON 151)

Big data can help us provide answers to fundamental social questions, from poverty and social mobility, to climate change, migration, and the spread of disease. But making sense of data requires more than just statistical techniques: it calls for models of how humans behave and interact with each other. Social data science combines the analysis of big data with social science theory. We will take a project-oriented, many models-many methods approach. This course will introduce students to a variety of models and methods used across the social sciences, including tools such as game theoretical models, network models, models of diffusion and contagion, agent based models, model simulations, machine learning and causal inference. Students will apply these tools to tackle important topics in guided projects. Prerequisite is Econ 102A, Polisci 150A or equivalent.
Last offered: Autumn 2022 | Units: 5 | UG Reqs: WAY-AQR

POLISCI 154: Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, MS&E 134, 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 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-AQR, WAY-SI

POLISCI 241S: Spatial Approaches to Social Science (ANTHRO 130D, ANTHRO 230D, 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.
Last offered: Winter 2020 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI

POLISCI 247A: Games Developing Nations Play (ECON 162, POLISCI 347A)

If, as economists argue, development can make everyone in a society better off, why do leaders fail to pursue policies that promote development? The course uses game theoretic approaches from both economics and political science to address this question. Incentive problems are at the heart of explanations for development failure. Specifically, the course focuses on a series of questions central to the development problem: Why do developing countries have weak and often counterproductive political institutions? Why is violence (civil wars, ethnic conflict, military coups) so prevalent in the developing world, and how does it interact with development? Why do developing economies fail to generate high levels of income and wealth? We study how various kinds of development traps arise, preventing development for most countries. We also explain how some countries have overcome such traps. This approach emphasizes the importance of simultaneous economic and political development as two different facets of the same developmental process. No background in game theory is required.
Last offered: Winter 2021 | Units: 3-5 | UG Reqs: WAY-AQR, WAY-SI

PSYCH 10: Introduction to Statistical Methods: Precalculus (STATS 60, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

PUBLPOL 104: Economic Policy Analysis (ECON 150, PUBLPOL 204)

The relationship between microeconomic analysis and public policy making. How economic policy analysis is done and why political leaders regard it as useful but not definitive in making policy decisions. Economic rationales for policy interventions, methods of policy evaluation and the role of benefit-cost analysis, economic models of politics and their application to policy making, and the relationship of income distribution to policy choice. Theoretical foundations of policy making and analysis, and applications to program adoption and implementation. Prerequisites: PUBLPOL 50 or ECON 50. Students are also strongly encouraged to either complete ECON 102B prior to taking this course or take ECON 102B concurrently with this course. Undergraduate Public Policy students are required to take this class for a letter grade and enroll in this class for five units.
Terms: Win | Units: 4-5 | UG Reqs: WAY-AQR
Instructors: ; Rosston, G. (PI)

PUBLPOL 105: Empirical Methods in Public Policy (PUBLPOL 205)

Methods of empirical analysis and applications in public policy. Emphasis on causal inference and program evaluation. Public policy applications include health, labor and saving. Assignments include hands-on data analysis, evaluation of existing literature, and a final research project. Objective is to obtain tools to 1) critically evaluate evidence used to make policy decisions and 2) perform empirical analysis to answer questions in public policy. Prerequisite: ECON 102B. Public Policy students must take the course for a letter grade. Priority for enrollment will be given to Public Policy students. Non-Public Policy majors must receive instructor permission to enroll.
Terms: Win, Spr | Units: 4-5 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; Chee, C. (PI)

PUBLPOL 155: Solving Social Problems with Data (COMM 140X, DATASCI 154, EARTHSYS 153, ECON 163, MS&E 134, POLISCI 154, 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 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-AQR, WAY-SI

SINY 65: Climate Justice in the Megacity

Climate Justice in the New York megacity is scaling rapidly. The five boroughs are a living experiment in the global fight against extinction in the Anthropocene. With hardhat site visits, this course will visit projects under construction that address the long-term causation of climate volatility and those that are also adapting to the immediate symptoms.
Terms: Aut | Units: 4 | UG Reqs: WAY-AQR

SINY 150: Biology, Technology, and Society: The City as a Human Life Support System

While environmental issues related to cities are often considered in the context of climate change, this course will use New York City as a lab to explore how dense global cities deal with their intense biological needs clean drinking water, sanitation and sewage, public health, food supply the ongoing management and maintenance of which occupy a surprising portion of the infrastructure, management, and tax expenditure of most city governments.
Last offered: Spring 2018 | Units: 4 | UG Reqs: WAY-AQR

SINY 162: Sustainable and Resilient Urban Systems in NYC

The objective of the course is to develop a qualitative and quantitative understanding of sustainability and resilience for major urban areas. The first part of the quarter will focus on sustainability and the second on resilience. n nThe course will commence with an overview of the 17 Sustainable Development Goals (SDG) as defined by the United Nations and how New York City is working towards these goals. The components and pillars that lead to a sustainable urban environment will be defined and corresponding metrics will be used to quantify sustainability utilizing simple data analytics tools. Challenges to meeting these goals will be an important part of reading and project assignments. Examples of sustainability efforts within New York City (NYC) include the development of new parks or renewal of industrial areas for recreational use, design of green buildings, rooftop farms and gardens, and the renovation of transportation facilities as focal points. Field trips will include visits to the Highline, the Hudson Yard and the Bank of America building. n nResilience of urban systems will be the focus of the second half of the class. Issues of resilience will be discussed in relation to major disasters including floods, extreme storms and climate change. The 7 global resilience targets will be identified according to the Sendai Framework for Disaster Risk Reduction. The major components of resilience will be reviewed in class followed by a visit to or by a representative from the 100 Resilient City Initiative supported by the Rockefeller Foundation. The Superstorm Sandy in 2012 will be used as a case study to identify the disaster impact to the urban environment in NYC. We will discuss the emergency response of the City after Sandy and assess the performance of the NYC resilience plan. We will explore how New York City is preparing to respond and recover from future major disasters as part of the 100 Resilient Cities Initiative supported by the Rockefeller Foundation. n nNovel technologies will be discussed in terms of their implications for disaster resilience and sustainability. We will delve into a case study showing how modern and decentralized power systems with rooftop solar panels and microgrids can make NYC more resilient and sustainable.
Last offered: Spring 2021 | Units: 3-4 | UG Reqs: WAY-AQR

SOC 11N: The Data Scientist as Detective

This seminar is about how data are used to figure things out. We will consider cases in which a standing mystery existed, a question without an answer that was subsequently solved with a crisp, clever, or comprehensive analysis of data. We will pay close attention to the reasoning used involved in getting answers from data, and together we will consider how to assess how confident to be in those answers. All of which is directed to providing a better understanding of the logic of making inferences from data, evaluating those inferences, and actually working with data. Over the quarter, students will also be asked to pose and advance a project of their own that involves answering a question with data.
Terms: Spr | Units: 3 | UG Reqs: WAY-AQR, WAY-SI
Instructors: ; Freese, J. (PI)

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

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-AQR, WAY-SI

SOC 128D: Analytics for a Changing Climate: Introduction to Social Data Science

Data science has rapidly gained recognition within the social sciences because it offers powerful new ways to ask questions about social systems and problems. This course will examine how tools from data science can be used to analyze pressing issues relating to disaster, inequality, and scarcity in the Anthropocene (the current period in which humans are the primary driver of planetary changes). We will explore how a range of computational methods can be used to garner new meanings from sources such as weather monitors, press releases, websites, government programs, and more. This is a hands-on, interactive course culminating in a social data science project designed by the student or a team of up to four students. Most class sessions will be taught interactively using Jupyter Notebooks. Students will follow along with workshop-style lectures by using and modifying the provided R/Python code in real time to analyze data and visualize results. The course will cover such topics as the South African water crisis, Hurricane Katrina, the California Wildfires, and water rights along the Colorado River. Students will learn to explore text data with tools such as word embeddings, topic models, and sentiment analysis. Students will gain experience with Python and R and will learn about a range of packages for cleaning data, linking and matching records, and mapping their results.
Last offered: Summer 2023 | Units: 3 | UG Reqs: WAY-AQR, WAY-SI

SOC 180B: Introduction to Data Analysis (SOC 280B)

Preference to sociology majors, minors, and co-terms. Methods for analyzing and evaluating quantitative data in sociological research. Students will be taught how to run and interpret multivariate regressions, how to test hypotheses, and how to read and critique published data analyses.
Terms: Spr | Units: 4 | UG Reqs: GER:DB-SocSci, WAY-AQR
Instructors: ; Peterson, C. (PI)

STATS 48N: Riding the Data Wave (BIODS 48N)

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Last offered: Autumn 2020 | Units: 3 | UG Reqs: WAY-AQR, WAY-FR

STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

STATS 100: Mathematics of Sports

This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants. Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis. The course is intended to focus on data-based applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e.g, Stats 60,101 or 116, are recommended. A prior knowledge of Linear Algebra (e.g., Math 51) and basic probability is strongly recommended.
Terms: Win | Units: 3 | UG Reqs: GER:DB-Math, WAY-AQR

STATS 101: Data Science 101

This course will provide a hands-on introduction to statistics and data science. Students will engage with fundamental ideas in inferential and computational thinking. Each week consists of three lectures and two labs, in which students will manipulate real-world data and learn about statistical and computational tools. Topics covered include introductions to data visualization techniques, summary statistics, regression, prediction, sampling variability, statistical testing, inference, and replicability. The objectives of this course are to have students (1) be able to connect data to underlying phenomena and think critically about conclusions drawn from data analysis, and (2) be knowledgeable about how to carry out their own data analysis later. Some statistical background or programming experience is helpful, but not required. The class will start with a brief introduction to R but will move at a relatively fast pace. Freshmen and sophomores interested in data science, computing, and statistics are encouraged to attend. Also open to graduate students.
Last offered: Summer 2023 | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR

STATS 110: Statistical Methods in Engineering and the Physical Sciences

Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

STATS 116: Theory of Probability

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent. Undergraduate students enroll for 5 units, graduate students enroll for 4 units. Undergraduate students must enroll in one section in addition to the main lecture. Sections are optional for graduate students. Note: Autumn 2023-24 is the last time this course will be offered. It will be replaced by STATS 117 and STATS 118 in 2024-25.
Terms: Aut | Units: 4-5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

STATS 141: Biostatistics (BIO 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: Win | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR

STATS 191: Introduction to Applied Statistics

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisite: introductory statistical methods course. Recommended: 60, 110, or 141.
Terms: Spr, Sum | Units: 3 | UG Reqs: GER:DB-Math, WAY-AQR

SUSTAIN 101C: Climate 101

This course provides an introduction to Earth's climate system, including how climate has changed in the past, how it is changing now, and how it could change in the future. Topics include quantifying signal-to-noise ratio for detecting long-term change in climate variables; calculating Earth's energy balance; calculating sources and sinks of carbon; understanding the history of climate variations and changes over Earth's history; quantifying the contribution of different greenhouse gases and human activities to historical and future climate change; understanding extreme weather events in the past, present and future, and quantifying the time to different global warming thresholds given different socio-economic scenarios. Students will be asked to engage in analysis of climate datasets to understand climate processes and climate change. No prerequisites.
Terms: Aut | Units: 3 | UG Reqs: WAY-AQR

URBANST 124: Spatial Approaches to Social Science (ANTHRO 130D, ANTHRO 230D, POLISCI 241S)

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.
Last offered: Winter 2020 | Units: 5 | UG Reqs: WAY-AQR, WAY-SI
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