This lesson is being piloted (Beta version)

Foundations of Astronomical Data Science: Instructor Notes

Instructor notes


This lesson guides learners through analyzing data from a large database. Scientifically, we are identifying stars in GD-1, a stellar stream in the Milky Way (creating Figure 1 in “Off the beaten path: Gaia reveals GD-1 stars outside of the main stream” by Adrian Price-Whelan and Ana Bonaca.). The first part of this lesson (1-5) shows learners how to prototype a query, starting by querying a subset of the sky we ultimately want and then building up stronger and stronger filters locally. With our filters in place, lesson 6 performs the full query remotely, giving us a dataset to visualize in lesson 7. Lesson 7 demonstrates best practices and tips and tricks to efficiently and effectively visualize data.

Because this lesson follows a single dataset throughout, its easy for learners (and instructors) to lose sight of the bigger picture and focus instead on the scientific goals. At the beginning of each lesson it is recommended that the instructor discuss both the scientific goal of the lesson (with frequent references to Figure 1) and highlight the big picture skills that we hope each student takes away from the lesson, beyond the specific science case. At the end of the lesson the instructors should recap the same information, highlighting the best practices covered. TODO: reference slide show.

Decisions Made

Lesson 1: Queries

Lesson 2: Coordinates and units

Lesson 3: Proper motion

Lesson 4: Coordinate transformation and selection

x = ...
y = ...
plt.plot(x, y)

This idiom violates the recommendation not to repeat variables names, but since they are defined and used immediately, it might be ok. If you don’t like it, you can inline the expressions.

pmra_poly, pmdec_poly = np.transpose(pm_vertices)

Lesson 6: Photometry

Lesson 7: Visualization