5 courses with the subject MTHS, 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.
MTHS 1000Mathematical Foundations for Data Analytics
This course provides an introduction to key topics that form the foundation for further study in mathematics, data analytics, and statistics. Topics covered include finite math, logic, algebra (including basics of matrix algebra) functions, probability, and a conceptual introduction to calculus. Through this course, students will develop both an understanding of the concept the ability to apply the concepts and techniques to analysis and problem-solving. Course format includes readings, lectures and demonstrations, and extensive hands-on practice with instructor and peer feedback. 1 Course Unit
Understanding concepts from Linear Algebra is essential to serious study of many disciplines, ranging from physics and chemistry to economics and computer and data science, not to mention further study of higher mathematics. In this course, we'll be looking at both computational and theoretical aspects of linear algebra, as well as at a number of applications. The "basic stuff" of linear algebra comprises vector spaces and the linear mappings between them. These mappings are represented by matrices, and a lot of linear algebra is concerned with reducing the enormous amount of data contained in a matrix to a few salient numbers and properties. There aren't many prerequisites for the course other than basic high-school algebra and a willingness to stretch your mind around some awesome abstract concepts -- higher (than 3)-dimensional spaces, deducing things abstractly from basic principles, and learning how to interpret and exploit the deductions. It will be an exciting and fast-paced journey through topics such as Gaussian elimination, linear systems, linear transformations and their matrix representations, eigenvalues and eigenvectors, the singular-value decomposition and principal component analysis.
