This lesson is being piloted (Beta version)

Foundations of Astronomical Data Science

The Foundations of Astronomical Data Science curriculum covers a range of core concepts necessary to efficiently study the ever-growing datasets developed in modern astronomy. In particular, this curriculum teaches learners to perform database operations (SQL queries, joins, filtering) and to create publication-quality data visualisations. Learners will use software packages common to the general and astronomy-specific data science communities (Pandas, Astropy, Astroquery combined with two astronomical datasets: the large, all-sky, multi-dimensional dataset from the Gaia satellite, which measures the positions, motions, and distances of approximately a billion stars in our Milky Way galaxy with unprecedented accuracy and precision; and the Pan-STARRS photometric survey, which precisely measures light output and distribution from many stars. Together, the software and datasets are used to reproduce part of the analysis from the article “Off the beaten path: Gaia reveals GD-1 stars outside of the main stream” by Drs. Adrian M. Price-Whelan and Ana Bonaca. This lesson shows how to identify and visualize the GD-1 stellar stream, which is a globular cluster that has been tidally stretched by the Milky Way.

This lesson can be taught in approximately 10 hours and covers the following topics:

Prerequisites

This lesson assumes you have a working knowledge of Python and some previous exposure to the Bash shell. These requirements can be fulfilled by:
a) completing a Software Carpentry Python workshop or
b) completing a Data Carpentry Ecology workshop (with Python) and a Data Carpentry Genomics workshop or
c) independent exposure to both Python and the Bash shell.

If you’re unsure whether you have enough experience to participate in this workshop, please read over this detailed list, which gives all of the functions, operators, and other concepts you will need to be familiar with.

In addition, this lesson assumes that learners have some familiarity with astronomical concepts, including reference frames, proper motion, color-magnitude diagrams, globular clusters, and isochrones. Participants should bring their own laptops and plan to participate actively.

Schedule

Setup Download files required for the lesson
00:00 1. Basic queries How can we select and download the data we want from the Gaia server?
01:30 2. Coordinate Transformations How do we transform celestial coordinates from one frame to another and save results in files?
03:03 3. Plotting and Tabular Data How do we manipulate Astropy Tables? How do we make scatter plots in Matplotlib? How do we store data in a Pandas DataFrame? How do we save a workflow into a reusable function?
04:28 4. Plotting and Pandas How do efficiently explore our data and identify appropriate filters to produce a clean sample (in this case of GD-1 stars)?
05:53 5. Transform and Select How do we move the computation to the data?
07:08 6. Join How do we use JOIN to combine information from multiple tables?
08:33 7. Photometry How do we use Matplotlib to define a polygon and select points that fall inside it?
09:28 8. Visualization How do we make a compelling visualization that tells a story?
10:53 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.