MAT 345: Data Science

This course presents a variety of computational tools for modeling and understanding complex data and explores the data science lifecycle. Topics include manipulating data, exploratory data analysis, statistical inference, spam filters and naïve Bayes, neural networks, and machine learning algorithms such as linear regression, k-nearest neighbors, and k-means. The course will focus on both understanding the mathematics underlying the computational methods and gaining hands-on experience in the application of these techniques to real datasets.