AST 307 -- Introductory Astronomy

Semesters: Fall 2018, Spring 2019, Fall 2019, Fall 2020, Spring 2022
Course Description: Astronomy 307 is an introductory overview of the science of astronomy. Topics that will be covered will include the Earth, Moon, and sky; radiation and spectra; the formation, structure, and evolution of stars and planets; the solar system; exoplanets; astrobiology; the structure and evolution of galaxies; cosmology; and the fate of the universe. This course will emphasize critical thinking, scientific literacy, and quantitative approaches to problem solving rather than rote memorization of facts. Course lectures will be supplemented with in-class group activities and peer-to-peer discussions to promote active, inquiry-based learning.
This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real- world problems.

AST 381 -- Planetary Astrophysics

Semesters: Spring 2020
Course Description: Over the past quarter century the field of exoplanets has accelerated from the first detection of a planet orbiting another star to become one of the leading areas of active research in Astronomy. This graduate-level course will introduce students to the dynamic field of planetary systems. Topics will include exoplanet detection methods, formation and migration pathways, demographics and orbital architectures, atmospheres and interiors, statistical properties, and habitability. In addition, students will gain practical experience analyzing actual data sets, reading journal articles, and developing research skills. Requires graduate standing or consent of the instructor.

AST 382D -- Astronomical Data Analysis

Semesters: Fall 2021
Course Description: Interpreting astronomical observations begins with analyzing data. From optimally extracting a stellar spectrum to constraining cosmological models, data analysis lies at the heart of all aspects of astronomy. The goal of this course is to provide a practical guide to analyzing astronomical data. Topics will include applied probability theory, parameter estimation, Bayesian statistics, maximum likelihood methods, model fitting, and Markov Chain Monte Carlo sampling. Students will work with a variety of real astronomical datasets to develop experience and skills for research. Requires graduate standing or consent of the instructor.

Group-based active learning in AST 307.