AY 128: Astronomy Data Science Lab (Spring 2025)

Synopsis

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).

This course satisfies the Data Science major requirement for “Computational & Inferential Depth”. Read more about the upper division classes for the Data Science BA. This course also satisfies the Laboratory requirement for the Astronomy Major.

Instructors

• Dan Weisz (Professor, dan.weisz@berkeley.edu; 311 Campbell Hall)
• Saahit Srihari Mogan (GSI, smogan@berkeley.edu)

Course Aims

• Introduce and motivate a range of analysis techniques and data pipelining
• Gain practical, in-depth experience doing inference on real, open-ended modern astronomical challenges
• Build reproducible, well-tested, well-documented software & infrastructure
• Learn to work with open data and code, and in an open science environment
• Hone presentation (speaking & visualization) skills
• Develop skills for future in academia, industry, …

Course Format

• 4 credits
• 2 weekly 1.5 hour meetings
• “Show & tell” progress reports + instructor lecture
• 3.5 labs
• Will require a fair amount of dedicated coding time

Lab Schedule:

• Lab 0 (Clusters) — Assigned: Fri 1/17, Due: Thurs 2/6
• Lab 1 (Variable Stars & Dust) — Assigned: Thus 2/6, Due: Thus 3/6

Lectures

• Tuesday and Thursday 2:00-3:30pm in 131 Campbell

Office Hours

• Dan: 3:30-4:30pm Tuesday (Campbell 355)
• Saahit: 1-3pm Monday (355) and 12:30-2pm Thursday (355)

Grading

• 10%: Class Participation – Active engagement in class discussion and lecture, participation during “show and tell”, attending office hours, Q&A engagement on (Ed Discussion ).

• 60%: Lab Reports/Notebooks – due before specified class, you can collaborate with people in the class, but all work, writeups, notebooks, coding, plots, etc. MUST be your own. See syllabus for grading and late policy.

• 30%: Lab Checkpoints – weekly progress goals for each lab; graded mostly for completeness and presenting and leading discussion on at least one checkpoint topic. We’ll drop your 2 lowest checkpoint scores, no extensions on checkpoints.

Prereqs

• There are no formal prereqs for this class but there are a number of informal requirements, outlined in this document, which you must satisfy.

Use of Generative AI models & LLMs

Please see the Academic Use Policy of Artificial Intelligence (AI) for the policy in this course.