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.

GD-1 is a stellar stream around the Milky Way. This means it is a collection of stars that we believe was once part of a bound clump, but the gravitational influence of the Milky Way has torn it apart and spread it over an arc that traces out its orbit on the sky. This is interesting, because if the original bound clump was a dwarf galaxy, understanding its orbit with sufficient precision allows us to measure the mass of the Milky Way, which is very important for understanding the future and past of the Milky Way as a whole. But that is much easier to do if we have a coordinate system aligned with the stream because that makes fitting the location of the stars much easier mathematically - it becomes more linear instead of some complicated curve. Additionally, this stream is especially interesting because it has “gaps”, which have a natural interpretation as being caused by the influence of small clumps of dark matter passing near the stream. Knowing the typical rate of these gaps tells you about the typical size and density of these clumps, which turns out to be one of the best probes we have of the fine structure of dark matter.

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

Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach this lesson. After watching this video, please contact so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at Centrally-Organised Data Carpentry Foundations of Astronomical Data Science workshops.


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.


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 a subset of the results in files?
03:05 3. Plotting and Tabular Data How do we make scatter plots in Matplotlib?
How do we store data in a Pandas DataFrame?
04:00 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)?
05:05 5. Transform and Select When should we use the database server for computation?
When should we download the data from the database server and compute locally?
06:15 6. Join How do we use JOIN to combine information from multiple tables?
How can we make a selection within a joined table?
How should we save the result?
07:45 7. Photometry How do we use Matplotlib to define a polygon and select points that fall inside it?
08:25 8. Visualization What elements make a compelling visualization that authentically reports scientific results ready for scientific presentation and publication?
What tools and techinques are available to save time on creating presentation and publication-ready figures?
10:35 Finish

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