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PHYS 434 A: Advanced Laboratory: Computational Data Analysis

Meeting Time: 
MW 11:30am - 12:20pm
PAA A110
Shih-Chieh Hsu

Syllabus Description:

Welcome to PHYS 434 "Advanced Data Analysis Techniques for Large Datasets."  We will provide a practical introduction to statistical analysis of experimental data in physics and astronomy. We will build computational skills for exploration, understanding, and interpretation of data. The course will conclude with projects developing statistics models to analyze data from LHC and HESS experiments.

This is an in-person course (with a few remote lectures), 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.

Lectures (A110)

Monday 11:30-12:20

Wednesday 11:30-12:20

Lab Sections (B143)

Tuesday (AA) 1:30-4:20 (Ali Garabaglu, Haoran Zhao)

Wednesday (AB) 1:30-4:20 (Alex Schuy)

Thursday  (AC) 1:30-4:20 (Ali Garabaglu, Haoran Zhao)


  • Gerhard Bohm, Günter Zech, "Introduction to Statistics and Data Analysis for Physicists" [pdf][URL]

Important links:


  • This is an active learning course. Pre-lecture assignment (videos, readings, quizzes) are required to be completed prior to lecture time. Post-lecture quiz will be used to evaluate your understanding.
  • This is a portfolio based class. Labs and homework will all be developed using jupyternotebook. The notebook will be submitted via GitHub pull requests (PRs), and the html version will be submitted and graded on Canvas. 
  • Late submission is 20% penalty. 
  • You must submit all 8 labs to pass the course, and laboratory attendance is required. To checkout your lab, TA will post github comment before you leave the lab session. 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.
  • Homework and Lab report:


Grading is divided between the three activities of the course:

(i) Lecture (pre-lecture quiz 10% , post-lecture quiz 10%)
The pre-lecture quiz is graded for completion and is due 10 pm of the previous day before each lecture. It is based on assigned reading and tests your preparation to join the in-class activities.

The post-lecture quiz is graded based on correctness and is due 11:59 pm  of the next day after each lecture. Before Lec 11, the post-lecture quiz is mostly questions to test the concept discussed in the lecture. Since Lec 11, the post-lecture quiz is a set of in-class coding activities. Your lowest quiz score will be dropped. 

(ii) Homework
(30%) due 11:59pm Friday
Each homework is worth 10 pts and is evaluated on 1) Code completeness/meeting the assignment goal (6pts), 2) Code organization (2pts), and 3) Code documentation (2pts). Your lowest homework score will be dropped.

(iii) Lab
(50%) due 11:59pm Monday
Each lab report is worth 10 pts and is evaluated on 1) Lab checkout (1pts), 2) Code completeness/meeting the assignment goal (5pts), 3) Code organization (2pts), and 4) Code documentation (2pts)Your lowest lab report score will be dropped.



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 ( 


People & Office Hours

The TAs will be holding their office hours immediately before their lab, but of course feel free to attend the office hours of any TA. For pandemic considerations, please email me or the TA beforehand if you intend to attend office hours (we may not be there if no one emails).


Shih-Chieh Hsu (; Thursday 10:30-11:20 B213 or by appointment)

Teaching Assistants

Haoran Zhao (; Friday 12:30-1:30 B205)

Alex Schuy  (; Wednesday 12:30-1:30 B205)

Ali Garabaglu (; Thursday 12:30-1:30 B205)


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 Sciences (NSc)
Section Type: 
Last updated: 
May 16, 2022 - 11:55pm