ASTR 457

Fall 2026 All Classes

All Classes

Foundations of Data Science in Astronomy

Credit: 3 OR 4 hours.

Introduction to statistical and data science methods used in modern astronomy. Topics include robust statistics, regression, model building and hypotheses testing, MCMC methods, parameter estimation, time series analysis, clustering and dimensionality reduction, and hierarchical modeling. Applications to astronomical data are emphasized, as well as best practices for writing code and version control.

3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: ASTR 210, ASTR 310. Prior coursework in linear algebra, e.g. MATH 227 or MATH 257, recommended.

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ASTR 457 class schedule data for fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			81348	Lecture	1	12:30PM -1:50PM	TR	134 Astronomy Building	Narayan, G	Availability: Open Part of Term: 1 Date Range: 08/24/26-12/09/26 Credit: 3 hours Section Info: This course will cover a number of statistical techniques that are relevant to astrophysical studies. These include robust statistics, regression, model building and hypotheses testing, MCMC methods, parameter estimation, time series analysis, clustering and dimensionality reduction, and hierarchical modeling. We will also cover best practices for writing code and version control. These techniques are ubiquitous in science and industry. The goal is to provide a survey of these techniques, together with realistic problems, so that you see how they work and what their implicit assumptions are. Prerequisites: ASTR 210, ASTR 310; Recommended: Prior coursework in undergraduate statistics and undergraduate linear algebra (e.g., MATH 227 or 257). You will also need a computer with a working conda and git installation for much of the coursework.