蜜桃导航

Introduces the tools of statistical analysis. Combines theory with extensive data collection and computer-assisted laboratory work. Develops an attitude of mind accepting uncertainty and variability as part of problem analysis and decision-making. Topics include: exploratory data analysis and data transformation, hypothesis-testing and the analysis of variance, simple and multiple regression with residual and influence analyses.

This course is intended to introduce non-scientists to key concepts and approaches in the study of the environment. With a focus on the scientific method, we learn about natural systems using case studies of disruptions caused by human activity. Topics include global warming, deforestation, waste production and recycling, water pollution, environmental toxins and sustainable development. The relationships between science and policy, the media, and citizen action are also addressed. *Lab required. Please note that an additional fee will be charged for this course.

Introduces differential and integral calculus. Develops the concepts of calculus as applied to polynomials, logarithmic, and exponential functions. Topics include: limits, derivatives, techniques of differentiation, applications to extrema and graphing; the definite integral; the fundamental theorem of calculus, applications; logarithmic and exponential functions, growth and decay; partial derivatives. Appropriate for students in the biological, management, computer and social sciences.

This project-based course introduces data science by looking at the whole cycle of activities involved in data science projects. Students will learn how to think about problems with rigor and creativity, ethically applying data science skills to address those problems. The course project will address the theoretical, mathematical and computational challenges involved in data science.

This course will focus on anthropogenic environmental emergencies, such as global warming, habitat destruction, and the introduction of invasive species. Students will investigate specific cases discussed in recent peer-reviewed scientific articles, and will evaluate possible solutions to these crises from multiple perspectives.

Treats applications in economics and computer science, limited to Euclidean n-space. Topics include: the linear structure of space, vectors, norms and angles, transformations of space, systems of linear equations and their applications, the Gauss-Jordan method, matrices, determinants, eigenvalues and eigenvectors. Uses Mathematica for graphics and algorithms.

The 21st century has seen a big increase in the amount of data which is made accessible. Social media such as Facebook, online shops such as Amazon and many others, are all gathering raw data. But what can be done about this data? Data Science covers tools and methods around the extraction of knowledge from data. Such tools cover its collection, storing, processing and analysis. In this course we will learn about several of the most important tools in the above flow and will apply them to real-world examples.

We will explore how to understand environmental systems from data science angles. The course encourages students to think critically and reason quantitatively about an environmental problem rather than just focusing on getting a specific answer. This course will have hands-on practical work with real data, R or Python, and statistical or machine learning software packages.

A Senior Project is an independent study representing a Major Capstone Project that needs to be registered using the Senior Project registration form. (Download: https://aupforms.formstack.com/workflows/senior_project)

Internships may be taken for 1 or 4 credits. Students may do more than one internship, but internship credit cannot cumulatively total more than 4 credits.