Applied Mathematics – Data Analysis

Data analysis is the study of the extraction of knowledge from data. This course is an introduction to the mathematical methods behind the scientific techniques developed for extracting information from large data sets. We will explore several fundamental topics in computational data analysis, including basic concepts in probability, Bayes’ rule, central limit theory, linear regression, dimension reduction, gradient descent, principal component analysis, clustering and classification. We will use Python to demonstrate and explore basic concepts, but programming will not be the main focus of the course.

Making good decisions requires understanding the consequences of those decisions. In this course we will explore how to use various mathematical models to  predict outcomes of decisions in fields as diverse as environmental conservation, business management, and engineering. Students will also gain experience  using software to both analyze their models and communicate their results and recommendations. Emphasis will be on providing quantitative evidence for a  decision using mathematical tools including basic probability, calculus, graph theory, and game theory.

An introduction to numerical methods for solving problems from calculus and linear algebra, including the solution of a single nonlinear equation, the solution of linear systems, interpolation and approximation, differentiation and integration, and the solution of eigenvalue problems.