Course materials for Big Data in Astrophysics

• The topics of each week's lectures are described in the syllabus PDF

• Lectures are in directories named XY/ where XY/ is the number of the week

• Homework assignments are in the UMN github (https://github.umn.edu/umn-csci-8581-S24/assignments) - there are instructions in there for submitting your work

• Solutions to homework assignments will be posted over the weekend after the Saturday they were due

• Various help cheat sheets are included in help/.

• You should also review in-class notebooks and homework solutions to make sure you understand what is happening

• The lecture notebooks have in-class exercises for you to work on

• Federica Bianco’s Python Bootcamp: https://github.com/fedhere/PyBOOT
• UIUC Fundamentals of Data science: https://github.com/gnarayan/ast596_2023_Spring
• Vanderbilt Astrostatistics: https://github.com/VanderbiltAstronomy/astr_8070_s22
• Drexel Big Data Physics: Methods of Machine Learning: https://github.com/gtrichards/PHYS_440_540
• ZTF summer school: https://github.com/mcoughlin/ztf_summer_school_2023
• AURA winter school: http://www.aura-o.aura-astronomy.org/winter_school/ - go to Past Years.
• YouTube Neural Networks: https://www.youtube.com/watch?v=aircAruvnKk