Experimental Data Analysis Lab

PHYS 391 - Fall 2020

http://pages.uoregon.edu/torrence/391/
Updated Monday November 30, 2020

Fourier Transform Notes
Hwk 5 is posted (due Dec. 4)
Lab 5 is posted (due Dec. 11)

Instructor Prof. Eric Torrence Willamette 418, 346-4618
torrence (at) uoregon
Office Hours: Mon. 2-3PM on Zoom
also available by appointment
Lab
Assistant		 Suzanna Officer sdo (at) uoregon
Office Hours: Mon 10-11AM on Zoom
also available by appointment
Lecture TR 12:15-13:45 Remote
Zoom connection details posted to Canvas
Labs Friday 12:15-1:45, 2:15-3:45 PM, Willamette 17
Zoom connection details posted to Canvas
Textbook Introduction to Error Analysis, 2ed, Taylor

Overview

This course will introduce the basic concepts of data analysis and practical techniques for implementing them. Half of the course will emphasize the theoretical foundation of data analysis with lectures and homework assignments, while the other half will emphasize the practical application of data analysis in lab assignments. Development of programming techniques for performing data analysis and data visualization will also be covered using python. The following topics will be covered:

Grading

Course grades will be based on five bi-weekly homework assignments from Taylor (45%), and five bi-weekly lab assignments (45%). There will also be a participation component for the synchronous lectures and labs (10%). There will be no examinations (midterm or final) for this course, so it is important for students to turn in all assigned work when it is due. Late assignments will either be significantly penalized or not accepted at the instructors discretion. The final lab assignment will be due during finals week in place of a final examination.

In order to pass the course, you must complete all of the labs!

Grades will be awarded based on the departmental grading policy, and students can assume that 90% will earn an A, 80% will earn a B, 70% will earn a C, 60% will earn a D, and below this will result in a failing grade. Modifiers (+/-) will be applied for scores within a few percent of these boundaries. I may adjust the grade boundaries depending upon the final distribution so that students with similar scores will receive similar grades.

Remote Instruction

The lecture for this course will be held remotely. I will pre-record a bit less than one hour of lecture material for each day's lecture, and the expectation is that each student will watch these videos on their own time in advance. These videos will be posted to Canvas via Panopto. These videos should also inspire you to read the relevant section of our book for more information. Many of these videos will pose questions or provide topics for discussion during the synchronous lectures. During our assigned lecture time, we will meet synchronously on Zoom and use this class time to cover more complex topics, work through problems in small groups, and answer questions about the material posed each week. I will strive to keep these synchronous zoom meetings to less than an hour, so please show up on time as we will start promptly at 12:15 PM each day.

The participation grade will be based on attendance and reasonable engagement with the synchronous course activities. We will not grade lecture attendance during the first week so you have a chance to get your technology sorted out. For the labs, the required level of participation will vary a bit each week, but please connect at the start of your assigned lab section and check in with the TA for any news or updates. As long as you attend 75% of the synchronous activities, you will receive full credit in your participation grade. The lectures will also be recorded, so if you really can't make it on a given day, you can still review what was discussed.

I would like to try requiring people to connect from your UO Zoom account. Most other universities require this for security reasons, it makes it easier to track who has connected, and it allows us to use pre-assigned and recurring breakout groups. So I would like to try this. If you think this will be a problem for you, please let me know.

We have permission to hold in-person lab sections, although most of the lab work done in this course is analysis activity that can be done remotely anyways. The first several weeks will be entirely remote, and if anyone has concerns about coming into campus for the labs, please contact me and we can work out an alternative arrangement if necessary. I do not want to force anyone to come to campus if you are not comfortable doing so. Depending upon how infection rates trend in Lane county once school is back in session, I may decide to just move all of the labs online as well.

You should plan on attending your lab session on Zoom. There is a dedicated Zoom meeting room for this. Check on Canvas for the most up-to-date information. Our TA will be present during lab times to help you, and we will use breakout rooms so that students can work on the lab assignments collaboratively in small groups.

Syllabus

Week Topic Lab
(due following Tuesday)		 Homework
(due following Tuesday)		 Reading
Week 1
9/28 - 10/2		 Measurement Uncertainties HW1 Taylor Ch. 1-3
Day1 Day2
Week 2
10/5 - 10/9		 Statistical Inference Python Intro Taylor Ch. 4
Day3 Day4
Week 3
10/12 - 10/16		 Normal Distribution HW2 Taylor Ch. 5
Day5 Day6
Week 4
10/19 - 10/23		 Weighted Average Speed of Light Taylor Ch. 6-7
Day7 Day8
Week 5
10/26 - 10/30		 Linear Regression HW3 Taylor Ch. 8
Week 6
11/2 - 11/6		 Binomial Distribution and Random Walks Brownian Motion Taylor Ch. 10
Day11 Day12
Week 7
11/9 - 11/13		 Poisson Distribution and Counting Statistics HW4 Taylor Ch. 11
Poisson Derivation
Week 8
11/16 - 11/20		 Fourier Series and Transforms Photon Counting
due Wednesday 11/25		 Fourier Transform Notes
Day15 Day16
Week 9
11/23 - 11/27		 Sampling Theory
No Labs Friday		 HW5
due Friday 12/4		 Day17
Week 10
11/30 - 12/4		 Discrete Fourier Transforms Fourier Transforms Day18
Finals
12/7 - 12/11		 Final Lab Due Friday 12/11 at 5 PM

This syllabus is tentative, and is subject to change as the quarter progresses.

Python

One of the goals of this course is to give you the skills to properly do non-trivial data analysis of large data samples. There are many different tools available to do this, but we had to pick something, and we are going to use python. Python is relatively easy to learn, very powerful, and widely used in Science disciplines. Python is also a good example of 'procedural programming', and the general techniques learned in this course can easily be transferred to the language or tool of your choice later. Python is widely (and freely) available at the University of Oregon and elsewhere. You will need to use Python extensively in this course, so you should invest some time in the first two weeks to make sure you have a working computing environment which you can use and you are happy with. Rather than using python directly, we will use Jupyter Notebooks, which will provide a more structured environment and give you a convenient way to turn in your lab assignments. If you are new to programming, you may want to look at some of the python tutorials linked from the python resources page.

Homework

Homework will typically be assigned every other week on Tuesday and due on the following Tuesday at the start of class. The homework will mostly be problems from Taylor forcing you to work through a particular concept 'by hand' at least once. Supplemental problems to exercise your python skills may also be assigned. Please turn in your homework as a PDF file through Canvas. There are several free apps available for you phone that make this very convenient.

Labs

Lab assignments will be made every two weeks and will be due on Tuesday during weeks when homework is not due. It is expected that you will work on your labs during the two weeks before they are due. Assigned lab times will be available when TAs will be in room 17 (or online) to provide support and advice, although you are free to work on the labs whenever you have time available. You will be expected to work with a partner, although each member of the lab group is expected to turn in their own material including the data analysis and any associated code. This lab arrangement may change due to Covid restrictions, so please pay attention to announcements and discussion in class for more accurate information.

Formal lab write-ups will not be required, although I do expect you to keep notes that clearly show the work you have done. For each lab assignment, you will be asked to turn in a Jupyter notebook answering the questions posed in the lab writeup, any notes you take about the work you did in the lab, and the solution to any coding or analysis tasks requested for that lab. This Jupyter notebook needs to be turned by emailing it to me along with any auxiliary data files before class on Tuesday.

For an upper-division course, the university expects students to spend one hour in class and two hours out of class for each credit. While each student will vary, you should expect on average to put this much time into this course. In particular, you should not expect to complete all of the lab work during the scheduled lab times each week, although you certainly should be able to collect all of the necessary data during that time. Make sure you do not try to start your lab assignments at the last minute. Most students who struggle in this course simply don't invest enough time in completing the labs.

Academic Misconduct

There is arguably a grey area between working together collaboratively on a homework assignment or lab, and just copying somebody else's work. In general, however, it is usually very apparent to the people involved whether a student is really contributing to a result, or just copying from others. Copying answers and passing them off as your own work, either from another student or from any other source is no different from plagiarism, and will be dealt with according to the UO rules and procedures for academic misconduct.

You are responsible for all the work you turn in for this course. You are encouraged to work with others to help your understanding, but anything you write down on your homework or you lab assignments needs to be your own work. You can certainly collect data with your lab partner and discuss the methods for analyzing the data and even compare your results, but all written and code work must be your own.

Accessible Education

The University of Oregon is working to create inclusive learning environments. Please notify me if there are aspects of the instruction or design of this course that result in disability-related barriers to your participation. You are also encouraged to contact the Accessible Education Center in 164 Oregon Hall at 541-346-1155 or uoaec@uoregon.edu.

Prohibited Discrimination and Harassment

No forms of discriminating, harassing, or hostile behavior in class will be tolerated. Any student who has experienced sexual assault, relationship violence, sex or gender-based bullying, stalking, and/or sexual harassment may seek resources and help at safe.uoregon.edu. To get help by phone, a student can also call either the UO 24-hour hotline at 541-346-7244 [SAFE], or the non-confidential Title IX Coordinator at 541-346-8136. From the SAFE website, students may also connect to Callisto, a confidential, third-party reporting site that is not a part of the university. Students experiencing any other form of prohibited discrimination or harassment can find information at respect.uoregon.edu or aaeo.uoregon.edu or contact the non-confidential AAEO office at 541-346-3123 or the Dean of Students Office at 541-346-3216 for help. As UO policy has different reporting requirements based on the nature of the reported harassment or discrimination, additional information about reporting requirements for discrimination or harassment unrelated to sexual assault, relationship violence, sex or gender based bullying, stalking, and/or sexual harassment is available at Discrimination and Harassment. The instructor of this class, as a Student Directed Employee, will direct students who disclose sexual harassment or sexual violence to resources that can help and will only report the information shared to the university administration when the student requests that the information be reported (unless someone is in imminent risk of serious harm or a minor). The instructor of this class is required to report all other forms of prohibited discrimination or harassment to the university administration. Specific details about confidentiality of information and reporting obligations of employees can be found at titleix.uoregon.edu.

Mandatory Reporting of Child Abuse

UO employees, including faculty, staff, and GEs, are mandatory reporters of child abuse. This statement is to advise you that your disclosure of information about child abuse to a UO employee may trigger the UO employee's duty to report that information to the designated authorities. Please refer to the following links for detailed information about mandatory reporting: Mandatory Reporting of Child Abuse and Neglect.