42 courses with the subject MAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
This course is a survey of mathematics that may focus on a historical, philosophical, computational, problem solving, and/or aesthetic point of view. The faculty member teaching this course chooses topics. Students study applications of linear and exponential functions. GE: Quantitative Reasoning
This is an introductory course in descriptive and inferential statistics. Students learn how to use statistics in real-world situations to gain insight to often complex data sets and how to present and discuss their results. Students use the R open-source software programming language to learn data visualization and analysis which is an industry-standard tool for today's market. Course Fee. GE: Quantitative Reasoning
This is an introductory two-course sequence in descriptive and inferential statistics. Students learn how to use statistics in real-world situations to gain insight to often complex data sets and how to present and discuss their results. Students use the R open-source software programming language to learn data visualization and analysis, which is an industry-standard tool for today's market. Examples cross disciplines and focus on normal and t-distributions, Chi Square procedures, and Analysis of Variance (ANOVA). Course Fee. GE: Partially satisfies Quantitative Reasoning
This 2-credit course builds on students’ understanding of the basic function concepts and ability with the skills necessary for the successful study of MAT 2410 Calculus I. Students hone their understanding of function concepts and manipulation, become proficient with the calculation and application of average rate of change, and demonstrate expertise with exponential and trigonometric functions. Course Fee.
This course is a continuation of the standard high school algebra sequence. It provides a background in some of the basic functions necessary to study MAT 2410 Calculus I and for applications in the sciences, environmental studies, and finance. Students investigate linear, quadratic, general polynomial, and rational functions. GE: Partially satisfies Quantitative Reasoning
This course is a continuation of MAT 1510 Precalculus I. It provides background in more of the basic functions necessary to study MAT 2410 Calculus I and for applications in the sciences, environmental studies, and finance. Students investigate exponential, logarithmic, and trigonometric functions. Course Fee. GE: Partially satisfies Quantitative Reasoning exam.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 8 credits.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 12 credits.
This course is an introduction to data science. It uses the R programming language to efficiently clean and organize, analyze and explore, and effectively summarize and visualize the data. Appropriate statistical methods are used to make data driven decisions.
This is an introductory course emphasizing the fundamental principles of modern structured and object-oriented programming. Topics include computer and programming basics, data types, control structures, functions/methods, classes and objects, data structures, web and PDF scraping, and an introduction to algorithms. There is significant emphasis on the methodical development of proper (Python) syntax as well as discussions on abstract computer programming concepts. Additionally, students will partner with local nonprofit Inclusive Development International to apply their programming skills and solve problems.
This course is an introduction to the mathematics of rates of change. Students explore limits, investigate the concept of the derivative, master differentiation techniques, apply the first and second derivatives to the graphing of functions, related rates problems, and maxima and minima problems, and glimpse an introduction to integration. Mathematical software may be used extensively to explore and reinforce the material. Course Fee. GE: Quantitative Reasoning
This course builds on the concepts and skills developed in MAT 2410 Calculus I. Students master integration techniques, apply integration to area and volume problems, explore numerical integration, manipulate sequences and series, and employ Taylor's Theorem to approximate transcendental functions. Mathematical software may be used extensively to explore and reinforce the material. Course Fee.
This course is an introduction to the calculus of functions in more than one variable. Students explore topics including vector algebra, lines and planes, partial derivatives, the gradient, graphing in three dimensions, multiple integrals, vector integral calculus, and Stokes' and Divergence Theorems. Mathematical software may be used extensively to explore and reinforce the material. Course Fee.
This course is an introduction to solving linear systems of equations, matrix algebra, and abstract vector spaces. Students explore methods of solving linear systems of equations, including Gaussian elimination, matrix algebra, geometry in three-dimensional Euclidean space, and general vector spaces, and master the concepts of linear independence, eigenvalues, and eigenvectors and their applications. Mathematical software is used extensively to explore and reinforce the material.
This course is an introduction to the theory of differential equations--the methods and theory of solving them. Students will learn to classify differential equations by type, to consider uniqueness and existence properties, and to employ analytic methods for solving first-order and second-order differential equations. Students explore series solutions, matrix methods, Laplace transforms, and numerical methods on computers to solve differential equations and applications. Mathematical software may be used extensively to explore and reinforce the material. Course Fee.
This course is designed for students desiring to pursue mathematical knowledge beyond Calculus. Students learn the language of mathematics through a study of logic and proof techniques. Students learn the culture of mathematics by reading and critiquing published, peer-reviewed, undergraduate research papers. Students learn how to use various mathematical software packages such as Maple, Geogebra, and SAS. They also learn how to create mathematical documents using a mark-up language such as Latex.
This course is designed for students with a desire to pursue mathematical knowledge past Calculus. Students learn the language of mathematics through logic and proof techniques in the context of Calculus, geometry, number theory, and graph theory. Students gain experience necessary for the study of abstract and theoretical mathematics.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 8 credits.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 12 credits.
This is a Study Away course that will focus on a special topic within the discipline, and will include two to three weeks of travel to a domestic or international destination at the culmination of the semester. The topic will be approached through an intercultural lens, and will achieve the following learning outcomes:
Graphs are relational structures in mathematics and data science used to model networks. Graphs consist of vertices and edges between those vertices. In this course, we will introduce the mathematics of graph theory and use technology to aid us in studying graphs as models for data via case studies with publicly available data sets based on student interests. Topics may include connectedness, path distance, cycles, trees, clustering (community detection), centrality, directed graphs, weighted graphs, and spectral theory. Applications may include phylogeny, pathfinding, decision trees, molecular graphs, political science, music theory, and graph neural networks. Python.
This course provides foundations in data management and database systems. It includes the relational database model, manipulating data with an interactive query language (such as SQL) and database programming, database development including internet applications, and database security. Science.
This course is an introduction to abstract mathematical structures, principally groups, and rings. Students investigate axiomatic and abstract structures by exploring elementary group, ring, and field theory. They examine the properties of Symmetry Groups, Permutation Groups, and subgroups of the Real Numbers, homomorphisms, and isomorphisms and refine proof-writing and proof-reading skills.
Students will explore data-driven mathematical models to find solutions to complex problems, using techniques collectively known as Machine Learning. Topics include both supervised learning (parametric and nonparametric algorithms, vector solutions. and neural networks) and unsupervised learning (clustering, dimensionality reduction, and deep learning). Prior programming experience with Python or R is required. Basic understanding of linear algebra is helpful but not required. Python.
This course is an introduction to modern geometries, specifically Euclidean geometry, Riemannian geometry, and hyperbolic geometry. Students investigate the geometric properties of the plane, sphere, cylinder, cone, and hyperbolic plane and write mathematical arguments and proofs based on these investigations. Additionally, students complete a research paper and a written and oral presentation of a proof from Euclid's The Elements. Appropriate technologies may be used to explore and reinforce the material.
This course is designed to focus on the application of mathematical techniques to real world problems. The course content varies depending on instructor and student interest. Students may explore difference equations, Markov Processes and basic probability theory, probability and modeling random phenomena, dynamical systems, fractals, game theory, or mathematical methods in the physical sciences.
This course serves as a follow-up to MAT 1411 Applied Statistics. The content can be broken up into three parts. Students will learn models for fitting data beyond linear regression and probability distributions beyond the normal and t distributions. Students will be introduced to time series data and its correlation and covariance, as well as ways to model time series like ARMA. Students will learn about model selection, using multifactor ANOVA, maximum likelihood, and information criteria as tests for goodness of fit. Throughout, data sets will be chosen from the fields of Biology and Environmental Science, and students will be encouraged to find and use data sets relevant to them.
This course is a seminar designed to survey the central ideas in the history and philosophy of mathematics. Students consider mathematics as a human intellectual endeavor inspired by and impacting our culture, history, and philosophy. They explore the history and philosophy of mathematics by studying original proofs of great mathematical theorems, reading and discussing advanced
This course is an introduction to both the classical and modern questions about numbers. Students explore the integers, examining issues such as primes, divisibility, congruences, primitive roots, quadratic residues, and quadratic reciprocity. They master a variety of number theoretic techniques and computations and apply these in applications such as cryptography and coding theory.
This course is designed for students preparing to embark upon study for a senior thesis in mathematics (see MAT 4890 Senior Thesis). Students read mathematical journal articles and present the material to the class. Students investigate several topics and, in conference with the professor, choose a particular topic for advanced study. Students complete sufficient background study to develop a cohesive plan for future research. A synopsis of this study together with a written research proposal is submitted to the mathematics faculty for approval. Junior standing is recommended.
Topics in Statistics are in-depth thematic courses with a focus in Statistics that may be repeated for credit under a different theme. May be repeated one time for a maximum of 8 credits.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 8 credits.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 12 credits.
This course is a theoretical exploration of the topology and calculus of the real number system. Students examine the real numbers as a linear vector space equipped with a norm; specifically the concepts of open and closed sets, limits, compactness, connectedness, continuity, metric spaces, and continuity of functions on metric spaces.
In the first part of this course, students create professional materials, including their own personal websites, to highlight the skills they have acquired throughout their college experience. Students learn how to turn their data science projects into engaging and informative interactive dashboards, equipping them with valuable skills for their capstone and future career opportunities. The second part of the course focuses on using data science to address challenges in environmental sustainability and social justice. Students choose a topic, together with sufficiently robust datasets, to analyze in depth in their capstone project. Information Systems, and Junior standing.
Under the mentorship of a faculty advisor, students complete a comprehensive capstone project. This course challenges students to integrate data science methodologies to solve a real-world problem within an application domain of their choice.
This course is designed as the culminating academic experience for students completing the major in data science. Students complete the analysis proposed in the MAT 4100 Pre-Capstone: Environmental and Social Advocacy through Data course, submit written reports to data science faculty for approval, and present their work in a public on-campus seminar. This course will also serve as an opportunity to complete the required internship. Data-driven methods can be used to assist a business or community partner. standing.
This course is designed as the culminating course for students completing a major in mathematics. Students complete the research approved in MAT 3880 Pre-Thesis Research Seminar, submit written reports in the form of a thesis to the mathematics faculty for approval, and present their work in a public on-campus seminar.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 8 credits.
Topics courses are 2- or 4-credit, in-depth, thematic courses that may be repeated for credit under a different theme. May be repeated up to a maximum of 12 credits.
Source: Warren Wilson College's catalog, linked per course · table learning_unit · CourseShelf publish 61
