Data Science

For students interested in algorithmic problem solving in a variety of settings. No experience is required. Students will learn to solve problems using a variety of coding styles while becoming familiar with the Python Standard Library and its essential third-party packages. The focus is on building skills that students can transfer to other settings, including data science and analytics. 

A survey of major data science tools and concepts. Students will learn the process of acquiring, cleaning, managing, and analyzing data sets to produce insights and make data-driven decisions. They will also gain experience with narrative building and visual storytelling using data.

A deep dive into specific tools and methods used in Data Science and Analytics. Topics will vary to match student needs and faculty expertise, and may include Statistical Regression, GIS systems, Linear and Nonlinear Optimization, Convolutional Neural Networks, Econometrics, and Business Analytics.

Seminar-style research project applying Data Science and Analytics to a problem in another discipline. Students will work under the supervision of a Data Science faculty member, with support from a discipline-specific advisor, to produce a deliverable product such as a research manuscript, case study, web page, etc.