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PHYS 434 A: Advanced Laboratory: Computational Data Analysis

Meeting Time: 
MW 11:30am - 12:20pm
PAA A110
Miguel Morales

Syllabus Description:

Welcome to PHYS 434. While for historical reasons this course's official name is "Application Of Computers To Physical Measurement,"  a more descriptive name would be "Advanced Data Analysis Techniques for Large Datasets." 

I am excited to be back in person, but also nervous, and we are all going to have to remain flexible as we work our way through the quarter and any more curveballs COVID-19 throws us. Naturally the syllabus is subject to change. 

In both lecture and lab we will be strictly following the UW Community Standards and Student Conduct to keep everyone safer.


Important links:

Online JupyterHub

Lecture recordings and slides

Lab Sections

Tuesday 1:30-4:20

Thursday 1:30-4:20

Friday 1:30-4:20 (Alex Schuy)

Useful Probability links

Expectations & Grading

  • This is a portfolio based class. Labs and homework will all be submitted via GitHub pull requests (PRs). 
  • There is no textbook. Lecture attendance is mandatory as that is the only place the material will be presented.
  • You must submit all 8 labs to pass the course, and laboratory attendance is required. At the discretion of your TA, you may miss one lab (the lab must still be turned in, though possibly with a later due date.)
  • You will work closely with your lab partner, but you will turn in separate labs via PR. The labs are expected to take 4-6 hours each, so significant work outside the scheduled laboratory time will be necessary.
  • Writeup instructions



We endeavor to make the course welcoming and accessible to all students. Standard accessibility requests will be handled through DRS.

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy ( Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form ( 



Miguel F. Morales (;)

Alex Schuy  (

Lingnan Shen (

Haoran Zhao (


Catalog Description: 
Data analysis using computational tools: curve fitting, statistical and error analysis, handling large data sets. Prerequisite: PHYS 334; and a minimum grade of 2.0 in either AMATH 301 or ASTR 300. Offered: A.
GE Requirements: 
Natural World (NW)
Section Type: 
Last updated: 
April 19, 2021 - 10:11pm