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Course Criteria
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2.00 Credits
This course explores the skills necessary to manage and produce formal dance concerts (e.g., costumes, scenery, lighting, sound, and stage management).
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2.00 Credits
This two-semester course gives students a forum to demonstrate their mastery of choreographic ideas, audition and rehearsal processes, and performance design-culminating in a high quality production.
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2.00 Credits
This two-semester course gives students a forum to demonstrate their mastery of choreographic ideas, audition and rehearsal processes, and performance design-culminating in a high quality production.
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4.00 Credits
Data Science is on the forefront of the Big Data Revolution. Governments, companies, nonprofits, and health care providers are collecting, storing, and analyzing vast amounts of data to extract information about us and make predictions about our lives. The mathematical and technological aspects of data science have been central to its success, yet they cannot exist in isolation. The context in which data is collected and used, and potentially misused, shape the impact on individuals and society as a whole. Therefore, the study of issues involving data collection, analysis, and its communication from multiple contexts involving different disciplines-including but not limited to economics, psychology, sociology, biology, medicine and chemistry-will be a central theme of this class. (WCore: WCSAM, QE)
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4.00 Credits
Quantitative literacy is increasingly important in our world of information. The primary goal of this course is to learn about data and how it's used. Along the way, we will learn how to develop basic tools to analyze and visualize data, read and evaluate research claims, and report research findings in honest and ethical ways. (This course may not be taken for credit if a student already has credit for DATA 220.) This course fulfills a Quantitative Emphasis requirement.
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4.00 Credits
Statistics is the study of data. This course will develop tools for analyzing data from a variety of fields. We follow the process from data gathering (sampling methods and experimental design) to exploratory data analysis (graphs, tables, charts, and summary statistics) to inferential statistics (hypothesis tests and confidence intervals) using simulation and sampling distributions. A key component of the course is the introduction of the statistical language R for analysis and R Markdown for the presentation of statistical analysis. (WCore: QE)
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2.00 Credits
Excellent graphics infuse data with meaning making complex ideas visible. This course focuses on the graphic capabilities of the R programming language for the effective display of quantitative information. Students will both curate and create data visualization products.
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2.00 Credits
Computational neuroscience is an interdisciplinary field that examines brain function in terms of the information processing properties of the structures that make up the nervous system, including neurons, circuits, and large scale networks. In this course we will investigate how cells and networks encode information via their biophysical and electrical properties, and how sensory and other information can be reconstructed (decoded) from studying the electrical state(s) of the brain. It is strongly recommended that students have taken either MATH 210 (Discrete Mathematics) or WCSAM 203 (Linear Algebra).
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2.00 Credits
Structural Equation Modeling (SEM) refers to a family of statistical methods for modeling the relationships between variables. As a research tool, SEM integrates and extends features of analysis of variance (ANOVA) procedures, linear multiple regression, and factor analysis by allowing the testing of predictive and causal relationships among continuous and categorical variables, both observed and unobserved (latent). This course will provide a conceptual as well as an applied, hands-on understanding of SEM assumptions, analyses, and the interpretation of data, and is recommended for students, faculty, and staff that are interested in learning about how SEM can be used to model various forms of quantitative data, using null-hypothesis testing and Bayesian approaches. Statistical programs, such as R, will be highlighted as a way to learn about and apply SEM to a wide-variety of research questions and hypotheses in the sciences, business, and beyond.
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4.00 Credits
The general linear model is a powerful framework for modeling relationships in data analysis. This course establishes the theory and application of regression models from simple and multiple regression through ANOVA and logit/probit models. In addition to building models, we will also learn to diagnose model fit and handle a wide range of possible complications. We will use the statistical language R for analysis and R Markdown for the presentation of statistical analysis.
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