Instructor: Karianne Bergen, Assistant Professor of Data Science and EEPS and Assistant Professor of Computer Science
Faculty Office hours: Mondays 3:30-4:30pm (see Canvas for details)
Teaching Assistant: Matt Jones (PhD candidate in EEPS)
Class meetings: Tuesday & Thursday 2:30-3:50pm First class: January 27, 2022
Course Description [flier]
This course introduces science students to modern data science tools for exploratory data analysis, predictive modeling with machine learning, and scalable algorithms for big data. Familiarize students with a cross-section of common machine learning models and algorithms emphasizing developing practical skills for working with data. Topics covered may include dimensionality reduction, clustering, time series modeling, linear regression, regularization, linear classifiers, ensemble methods, neural networks, model selection and evaluation, scalable algorithms for big data, and data ethics. The course will present case studies of these tools applied to problems in the Earth sciences. Intended audience is advanced undergraduate and graduate students in Earth, Environmental and Planetary Sciences or other physical science disciplines. Students will practice and develop their skills in data science through a hands-on project on a topic of their choice. This course is taught using the Python programming language.
Prerequisites
Required: Basic programming experience in Matlab, Python, R, or any high-level programming language (e.g. EEPS 0250, 1690, or 1430; APMA 0160; CSCI 0111, 0150, 0170, or 1090).
Recommended: Linear algebra (e.g. EEPS 1690; Math 0520, 0540; CSCI 0530) and statistics (e.g EEPS 1690; APMA 0650 or 1650; CSCI 0220) or permission of the instructor.
Students should be interested in Earth, environmental and/or planetary sciences, but prior coursework in these subjects is not required for the course.
see Resources page for review material on these topics