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:
- Incremental creation of complex ADQL and SQL queries.
- Using Astroquery to query a remote server in Python.
- Transforming coordinates between common coordinate systems using Astropy units and coordinates.
- Working with common astronomical file formats, including FITS, HDF5, and CSV.
- Managing your data with Pandas DataFrames and Astropy Tables.
- Writing functions to make your work less error-prone and more reproducible.
- Creating a reproducible workflow that brings the computation to the data.
- Customising all elements of a plot and creating complex, multi-panel, publication-quality graphics.
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.