Graduate Minor in Big Data in Astrophysics

The minor in Data Science in Astrophysics is designed to be interdisciplinary and integrates data science (statistics, data processing, artificial intelligence) with the field of astrophysics. Students pursuing the minor will receive the training needed to advance the field of astrophysics, while simultaneously preparing to be successful professionals and leaders in the modern data-driven workforce.

The curriculum covers the fundamental concepts in statistics, data processing and data management, as well as the modern machine learning and deep learning techniques needed for analyzing the ever-increasing astrophysics data-sets. Students will have opportunities to conduct frontier research projects using modern astrophysics data-sets, and will work in interdisciplinary teams mentored by interdisciplinary faculty. They will also have opportunities to develop their professional skills, such as communications and leadership.

Course Requirements

The Minor in Data Science in Astrophysics requires two courses:

  1. AST 5731 Bayesian Astrophysics

    This course will introduce Bayesian methods for interpreting and analyzing large data sets from astrophysical experiments. These methods will be demonstrated using astrophysics real-world data sets, and a focus on modern statistical softwares such as R and python. The course will assume familiarity with basic concepts in astrophysics, but it will include brief reviews as needed to demonstrate the use of modern data analysis techniques.

  2. AST 8581 Big Data in Astrophysics

    This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. The course will assume familiarity with basic concepts in astrophysics, but it will include brief reviews as needed to demonstrate the use of modern data analysis techniques.