ASTR 598, Autumn 2021, University of Washington:

Introduction to Astrostatistics and Data-Intensive Astronomy

Mario Jurić, @mjuric on GitHub

This repository contains ASTR 598 class materials. To get the latest versions into the class JupyterHub, make sure you're logged in and then click:

https://dirac.us/refresh598

Location

• When: MW, 2:30pm-3:50pm
• Where: B305 (http://dirac.us/598 if you can't attend in person)

Class Materials

• Syllabus and course description
• Lectures: Notebooks, Videos
• Canvas (used for quizzes only).
• Homework assignments
• JupyterHub: JupyterHub

Reference textbook

Ivezić, Connolly, VanderPlas & Gray: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data; first or second edition.

Class Description

This course will introduce you to concepts, tools, and and techniques from statistics and computer science that are essential for accurate and reproducible analysis of datasets, large and small. Through a series of lectures and hands-on problems, we will learn about elementary statistics, maximum likelihood methods, Bayesian probability and inference, MCMC methods, databases, and time series analysis. Practical data analysis will be done using Python, including astroML, astropy, astroquery and others.

The goal of this course is to give you the basic skills necessary to understand and correctly analyze rich datasets, from Kepler to LSST. It will also give you the theoretical prerequisites needed to successfully proceed to ASTR 597 Machine Learning in Astronomy (to be offered in the Winter quarter).