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11 - 20 of 142 results for: all courses

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: not given this year, last offered Winter 2013 | Units: 5 | UG Reqs: GER:DB-SocSci, WAY-AQR | Grading: Letter (ABCD/NP)

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

This multidisciplinary course combines different approaches to how GIS and spatial tools can be applied in social science research. We take a collaborative, project oriented approach to bring together technical expertise and substantive applications from several social science disciplines. The course aims to integrate tools, methods, and current debates in social science research and will enable students to engage in critical spatial research and a multidisciplinary dialogue around geographic space.
Terms: Win | Units: 5 | UG Reqs: WAY-AQR, WAY-SI | Grading: Letter or Credit/No Credit

APPPHYS 61: Science as a Creative Process (BIO 61)

What is the process of science, and why does creativity matter? We'll delve deeply into the applicability of science in addressing a vast range of real-world problems. This course is designed to teach the scientific method as it's actually practiced by working scientists. It will cover how to ask a well-posed question, how to design a good experiment, how to collect and interpret quantitative data, how to recover from error, and how to communicate findings. Facts matter! Course topics will include experimental design, statistics and statistical significance, formulating appropriate controls, modeling, peer review, and more. The course will incorporate a significant hands-on component featuring device fabrication, testing, and measurement. Among other "Dorm Science" activities, we'll be distributing Arduino microcontroller kits and electronic sensors, then use these items, along with other materials, to complete a variety of group and individual projects outside the classroom. The final course assignment will be to develop and write a scientific grant proposal to test a student-selected myth or scientific controversy. Although helpful, no prior experience with electronics or computer programming is required. Recommended for freshmen.
Terms: Aut | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)

BIO 61: Science as a Creative Process (APPPHYS 61)

What is the process of science, and why does creativity matter? We'll delve deeply into the applicability of science in addressing a vast range of real-world problems. This course is designed to teach the scientific method as it's actually practiced by working scientists. It will cover how to ask a well-posed question, how to design a good experiment, how to collect and interpret quantitative data, how to recover from error, and how to communicate findings. Facts matter! Course topics will include experimental design, statistics and statistical significance, formulating appropriate controls, modeling, peer review, and more. The course will incorporate a significant hands-on component featuring device fabrication, testing, and measurement. Among other "Dorm Science" activities, we'll be distributing Arduino microcontroller kits and electronic sensors, then use these items, along with other materials, to complete a variety of group and individual projects outside the classroom. The final course assignment will be to develop and write a scientific grant proposal to test a student-selected myth or scientific controversy. Although helpful, no prior experience with electronics or computer programming is required. Recommended for freshmen.
Terms: Aut | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)

BIO 108: Essential Statistics for Human Biology (HUMBIO 85A)

Introduction to statistical concepts and methods that are essential to the study of questions in biology, environment, health and related areas. The course will teach and use the computer language R and Python (you learn both, choose one). Topics include distributions, probabilities, likelihood, linear models; illustrations will be based on recent research.
Terms: not given this year, last offered Spring 2016 | Units: 4 | UG Reqs: WAY-AQR | Grading: Letter (ABCD/NP)

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: 3-5 | UG Reqs: GER:DB-Math, WAY-AQR | Grading: Letter or Credit/No Credit

BIO 183: Theoretical Population Genetics (BIO 283)

Models in population genetics and evolution. Selection, random drift, gene linkage, migration, and inbreeding, and their influence on the evolution of gene frequencies and chromosome structure. Models are related to DNA sequence evolution. Prerequisites: calculus and linear algebra, or consent of instructor.
Terms: Win, alternate years, not given next year | Units: 3 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter or Credit/No Credit
Instructors: Feldman, M. (PI)

BIOE 42: Physical Biology

BIOE 42 is designed to introduce students to general engineering principles that have emerged from theory and experiments in biology. Topics covered will cover the scales from molecules to cells to organisms, including fundamental principles of entropy, diffusion, and continuum mechanics. These topics will link to several biological questions, including DNA organization, ligand binding, cytoskeletal mechanics, and the electromagnetic origin of nerve impulses. In all cases, students will learn to develop toy models that can explain quantitative measurements of the function of biological systems. Prerequisites: MATH 19, 20, 21 CHEM 31A, B (or 31X), PHYSICS 41; strongly recommended: CS 106A, CME 100 or MATH 51, and CME 106; or instructor approval.
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)

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

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, BIO 84.
Terms: Spr | Units: 4 | UG Reqs: WAY-AQR, WAY-SMA | Grading: Letter (ABCD/NP)
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