ASTR457: Foundations of Data Science in Astronomy at UIUC, Fall 2026

Gautham Narayan, UIUC

TR, 1230-1350, Astronomy 134

This course has been redesigned for the agentic AI era: AI tools are allowed and documented on all labs, every student gets their own datasets with truth held by the instructor, and grades come from verification, calibration, and your ability to defend your own work. Read the syllabus PDF, especially "Working with AI: The Ground Rules", before doing anything else.

Instructions

Clone this repo

The topics of each week's lectures are described in the syllabus - read
457_FDS_Syllabus.md (screen-reader-friendly) or 457_FDS_Syllabus.pdf,
they have identical content

Lecture slides are in directories named lecture/XY/ where XY is the number of the week

Various help cheat sheets are included in help/. If you are new to
astronomy conventions (magnitudes, filters, light curves), read
help/astro_conventions.md before Lab 01.

Labs are in the labs/ directory - your dataset is labs/XY/data/<your netid>.csv.
Only your own dataset will be graded against your truth values, so make sure
you use yours.

Every lab and take-home submission includes the AI-use appendix described in
the syllabus.

Solutions to labs will be posted over the weekend after the Wednesday they
were due

You should also review slides and lab solutions to make sure you understand
what is happening

Adding to Repo git add git commit -m "Comment" git push

Pulling from <main, origin, upstream> git fetch <main, origin, upstream> git merge upstream/main -m "Comment"

Submitting your work

Fork this repo, create a folder named for your NetID inside `submissions/`,
commit your work there, and
open a pull request before Noon on the due date (Wednesdays for labs).