Astronomy Data Science Lab
Spring 2026
Overview
This course consists of three data-centric laboratory experiments that draw on a variety of tools used by professional astronomers. Students will learn to procure and clean data (drawn from a variety of world-class astronomical facilities), assess the fidelity/quality of data, build and apply models to describe data, learn statistical and computational techniques to analyze data (e.g., Bayesian inference, machine learning, parallel computing), and effectively communicate data and associated scientific results. This class will make use of data from facilities such as Kepler, Gaia, the Sloan Digital Sky Survey, and the Hubble Space Telescope to explore the structure and composition of the Milky Way, stars, and galaxies throughout the local and distant Universe. There is a heavy emphasis software development in the Python language, statistical techniques, and high-quality communication (e.g., written reports, oral presentations, and data visualization).
Staff
Instructors
Teaching Assistants
Schedule
Week 0
- Jan 16
- Lab 0 released Stellar Clusters: Introduction to ADQL & Gaia
Week 1
- Jan 20
- Lecture 1 Course Introduction
- Jan 22
- Lecture 2 Gaia & Stellar Physics
Week 2
- Jan 27
- Lecture 3 Isochrones & Chi-squared
- checkpoint 1 due
- Jan 29
- Lecture 4 Error Bars
- checkpoint 2 due
Week 3
- Feb 3
- Lecture 5 Variable Stars & Lab 1 Introduction
- Feb 5
- Lecture 6 Dust
- lab 0 due
- Lab 1 released Galactic Dust & RR Lyrae Stars
Week 4
- Feb 10
- Lecture 7 Bayesian Statistics & MCMC
- Feb 12
- Lecture 8 Savio & High Performance Computing
- checkpoint 1 due
Week 5
- Feb 17
- Lecture 9 MCMC Convergence & PyMC
- Feb 19
- Lecture 10 Lab Reports
Week 6
- Feb 24
- Lecture 11 Reports & Packaging
- checkpoint 2 due
- Feb 26
- Lecture 12 Lab 1 Presentations
Week 7
- Mar 3
- Lecture 13 Introduction to Lab 2: The Cannon
- checkpoint 3 due
- Mar 5
- Lecture 14 The Cannon - Technical
- lab 1 due
- Lab 2 released Modeling Stellar Spectra
Week 8
- Mar 10
- Lecture 15 Bitmasks & Fitting
- Mar 12
- Lecture 16 Lab 2 Work Session
Week 9
- Mar 17
- Lecture 17 Lab 2 Checkpoint 1 Presentations
- checkpoint 1 due
- Mar 19
- Lecture 18 Bootstrapping & Covariances
Week 10
- Mar 24
- spring break
- Mar 26
- spring break
Week 11
- Mar 31
- Lecture 19 Lab 2 Checkpoint 2 Presentations
- checkpoint 2 due
- Apr 2
- Lecture 20 Lab 2 Work Session
Week 12
- Apr 7
- Lecture 21 Lab 2 Work Session
- Apr 9
- Lecture 22 Lab 2 Checkpoint 3 Presentations
- checkpoint 3 due
Week 13
- Apr 13
- lab 2 due
- Apr 14
- Lecture 23 Introduction to Lab 3: Galaxies & ML
- Lab 3 released Galaxy Image Classification & Merger Rate
- Apr 16
- Lecture 24 Neural Nets & PyTorch
Week 14
- Apr 21
- Lecture 25 Lab 3 Checkpoint 1 Presentations & ResNets
- checkpoint 1 due
- Apr 23
- Lecture 26 Lab 3 Work Session
Week 15
- Apr 28
- Lecture 27 Lab 3 Work Session
- Apr 30
- Lecture 28 Lab 3 Checkpoint 2 Presentations & Final Thoughts
- checkpoint 2 due
Week 16
- May 8
- lab 3 due