Course Information
Instructor: Karianne Bergen, Assistant Professor of Data Science and EEPS and Assistant Professor of Computer Science
Faculty Office hours: Mondays 3-4pm or by appointment [virtual -- Zoom link on Canvas]
Teaching Assistant: Ethan Kyzivat
Class meetings: Tuesday & Thursday 10:30-11:50am [virtual -- Zoom link on Canvas] First class: January 21, 2021
Course Description:
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.
see Resources page for review material on these topics