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. Prerequisite: MAT 4100 Pre-Capstone: Environmental and Social Advocacy through Data and junior standing..
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. Prerequisite: MAT 2108 Introduction to Data Science or MAT 2110 Principles of Computer Science with Python.
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. Prerequisite: MAT 1411 Applied Statistics or BIO 2020 Ecology. May be taken concurrently.
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. Corequisite: MAT 2410 Calculus I.