This course is intended to teach students data analysis techniques used in conducting research in physics. We will be analyzing a mix of simulated and real data from particle physics and astrophysics experiments to get hands-on experience with looking at data.
The course will be taught using jupyter notebooks with python, although coding experience is not required. Topics will include visualizing data, understanding and estimating errors, basic fitting concepts, and evaluating hypotheses about data in a statistical manner.
Course website:
The course will meet for 80 minutes every week. There are two sections - students should attend the same section every week. Each class will have a brief introduction of important concepts, and the majority of the class time will be devoted to working through problems in small groups of 2-3 people.
Because this course extensively uses python-based notebooks, students need to have access to a laptop. Laptops are available for check out through Stanford Learning Technologies and Spaces.
Students should be familiar with calculus concepts such as differentiation, integration, and Taylor series.
Students are encouraged to attend the instructors’ Open Door Hours to ask questions about the lab report or concepts covered in lab. We also like talking about physics and data!
This is a 1-unit course. It is graded Satisfactory/No Credit. The class should take about 3 hours per week of your time. This includes:
To receive credit, attendance and participation in each week’s lab is expected. This includes arriving on time to class to participate in group work.
For each lab, there will be a report assigned that you will work through in class and finish after class. Each student must submit a report. Each report will be graded out of 3. The rubric is:
| Grade | Criteria |
|---|---|
| 0 | Assignment was not turned in or is largely incomplete |
| 1 | Unclear responses or responses that do not address the question concept. Explanations demonstrate a lack of understanding of the concepts covered |
| 2 | Detailed responses to questions, student demonstrates that they understand most (>70%) of the concepts covered |
| 3 | Detailed responses to questions, student demonstrates clear understanding of all of the topics covered |
An average grade of 2 or higher is required for a Satisfactory grade, and you must turn in a report for every lab. Reports which receive a grade of 1 can be resubmitted for a regrade up to a week from when they were first graded. Please also email us to tell us that you have submitted it so it doesn’t escape our notice. Each report is due one week after the lab and should be submitted on the Canvas website.
Week 9 of the course is set aside to work on a final project using the concepts you have learned. Each student must complete a final project to receive a Satisfactory grade. Week 10 ends on a Wednesday, and so we will not have a regular class meeting for either section. Instead, the usual Wednesday section will serve as extra Open-Door Hours for you to get advice or help on your final project.
There is no required textbook for this course.
A one-week extension for one report will be accepted without any questions asked (just note it as such on the report). Please also email us to tell us that you have submitted it so it doesn’t escape our notice. No other late reports will be accepted unless granted by an instructor or TA.
If you anticipate an absence:
If you can’t attend the other section, you are allowed one absence. You can complete the lab independently and submit by the due date. You will be deducted one point, so the maximum grade you can receive for that lab is a 2. Any further absences will result in a maximum grade of 1 for that week and are not eligible for regrades. You still must complete each lab to receive credit for this class.
| Week No. | Week | Topic |
|---|---|---|
| 1 | 04/01–04/05 | Introduction, histograms, descriptive statistics |
| 2 | 04/08–04/12 | Weighted averages, variance |
| 3 | 04/15–04/19 | Error propagation |
| 4 | 04/22–04/26 | Gaussian distributions, statistical significance |
| 5 | 04/29–05/03 | Covariance matrix, simple fitting |
| 6 | 05/06–05/10 | Fitting a model |
| 7 | 05/13–05/17 | Distinguishing signal from background |
| 8 | 05/20–05/24 | Frequency analysis |
| 9 | 05/27–06/31 | Final project work |
| 10 | 06/03–06/05 | Final project work |
The Stanford Honor code is found here. We expect that students abide by the honor code.
Much like conducting scientific research, participating in the data labs in this course is very collaborative. Students will work in groups during class, and you are encouraged to talk to other students outside of class about lab concepts. We expect that the reports are in your own words and you credit others that you worked with, as well as any external resources. You can write this on the top of your reports in a line listing collaborators and resources used.
From the OAE website: Students who may need an academic accommodation based on the impact of a disability must register with the Office of Accessible Education (OAE) and initiate their requests. Because accommodations are not retroactive, students should contact the OAE as soon as possible in order to ensure timely notice and coordination. Similarly, it is the student’s responsibility to notify the OAE as early as possible in the event of any problems or unexpected barriers experienced in the obtaining of academic accommodations and services.
If you have any concerns or questions, please don’t hesitate to contact the instructor.
There will be a lot of discussion in this class. Students are expected to engage with others in a respectful manner. This includes thoughtfully sharing their ideas, but also listening to others’ contributions. It is our highest priority that the classroom is a welcoming place to engage in academic discussions. Please read through this list of guidelines.
All materials Copyright 2021 Eric Charles, Ryan Linehan and Benjamin Navid Safvati and distributed for copying and extension under the GPLv3 License, unless otherwise noted.
Mod. Physics 89L Spring 2023 Ann Wang
Mod. Physics 89L Spring 2024 Charles Blakemore