What: QuarkNet Coding Camp
When: 21-25 June or 28 June - 2 July
Where: remote over Zoom
Cost: none! Actually, we pay you.
Application Deadline: Sunday, June 6 @ 5:00 PM EDT (local time here)
QuarkNet Teachers,
We’re now taking applications for the 2021 QuarkNet Coding Camp. It’s a one-week remote workshop for teachers where you'll learn to code in Python, analyze particle physics data, and practice integrating that into your own curriculum with reformed pedagogy. Along the way, you'll pick up strategies for promoting diversity, equity, and inclusion through your curriculum. We'll meet via Zoom Mon-Fri for a week (approximately 3hrs AM, 2hrs break, 3hrs PM). Whether you’re a beginner or well-practiced at coding or particle physics, there’s a place for you here. We’ll offer two one-week sessions Monday, June 21 - Friday, June 25 and Monday, June 28 - Friday, July 2.
What’s in it for you?
- We will be providing the usual $500 stipend for the week.
- We also provide a small equipment budget up to $250 per teacher, for technical supplies (e.g., to buy or upgrade your laptop, modem, headphones). You’ll need to submit receipts.
- You can earn graduate credit for this workshop.
- Five days of amazing speakers, professional development aligned with reformed pedagogy, and paid time to work with wonderful teachers from around the country.
What do you need?
- A reliable internet connection that can handle video calling.
- A Google account that is unconnected to your work email.
- Free time during the day: we built in some flexibility, but plan to be free during the synchronous work time.
- A desktop or laptop computer that can handle video calls and several browser tabs open.
- A willingness to work remotely in a team on a challenging task
What will you do?
- Become familiar with Jupyter notebooks, and practice writing code to perform simple tasks and analyses. Try out the example here … it’ll help you decide if you’ll find it interesting and doable.
- Develop your coding skills, and apply them by analyzing real data from the CMS experiment.
- Extend these skills and tools to new datasets and areas of physics.
- Design investigations and activities to be used with your students in the classroom, and develop a plan for implementation.
I’m interested! What should I do to apply?
- Submit this form by Sunday, June 6 @ 5:00 PM EDT (local time here)
- Have questions? Email Adam at adamlamee@gmail.com
Sincerely,
The QuarkNet Coding Fellows and Teaching & Learning Fellows
(Peter Apps, Tiffany Coke, Chris Dimenna, Gerry Gagnon, Jodi Hansen, Adam LaMee, & Jeremy Smith)
Agenda (1st session)
Mon June 21
Tues June 22
Wed June 23
Thurs June 24
Fri June 25
Post-Camp To Do List
-
Adam with email about: Money, grad credit, fame, fortune: TBA
Resources
Learning to code
-
CODE.org has TONS of great stuff for teachers and students
-
W3Schools.org has great, free tutorials on Python, HTML, Java and more
-
Python for Everybody (pdf) book
-
Python Programming: An Introduction to Computer Science by John M. Zelle
-
EDX.org online courses
-
Adam’s CODINGinK12.org science coding activities
-
Chris Orban’s Let’s Code physics simulation activities and HS coding page on Compadre
-
PICUP collaboration of activities submitted by other teachers
Data Science
-
Chris Albon’s Pandas tutorials (see the Data Wrangling section)
-
Function to run on a Pandas DataFrame (like getting columns names or seeing unique values) and some Pandas statistical functions
-
Some Numpy functions
-
Some Pyplot functions
-
Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Perez
Physics
-
Disordered Cosmos: Dark Matter, Spacetime, & Dreams Deferred by Dr. Chanda Prescod-Weinstein
-
Quantum Diaries blog
-
PhyPhox mobile app to collect, plot, and export raw data from Apple and Android mobile devices. And it’s free.
-
Particle Physics Data Group (PDG): for example, the page on the J/ψ.
Colab Techniques
-
How to make a Colab link from GitHub
-
url of file on GitHub:
https://github.com/QuarkNet-HEP/coding-camp/blob/main/probability.ipynb
-
add at the beginning the Colab bit:
https://colab.research.google.com/
-
Then you have a shareable link:
https://colab.research.google.com/github/QuarkNet-HEP/coding-camp/blob/master/intro.ipynb
-
-
Reading a dataset from Google Drive
-
How to instructions (thx Jim Deane)
-
Example notebook from Peter Apps for Rolling With Rutherford
-
use the pandas read_csv() function
-
pd.read_csv(“https://drive.google.com/uc?export=download&id=DRIVE_FILE_ID”) where “file ID” is the code at the end of the sharing link
pd.read_csv("https://drive.google.com/uc?export=download&id=1bfAmN-qQ6ptoKQCmDH3u-w0mdfQEsdfq")
-
-
or use read_excel() and append /export to the end of your Google Sheet ‘view only’ link, like this:
pd.read_excel("https://docs.google.com/spreadsheets/d/1EJhtT4Ssbem1JkzcJplULYW-ojKwmg4DqQRNeElAmyw/export")
-
Agenda (2nd session)
Mon June 28
Tues June 29
Wed June 30
Thurs July 1
Fri July 2
Post-Camp To Do List
-
Adam with email about: Money, grad credit, fame, fortune: TBA
Resources
Learning to code
-
CODE.org has TONS of great stuff for teachers and students
-
W3Schools.org has great, free tutorials on Python, HTML, Java and more
-
Python for Everybody (pdf) book
-
Python Programming: An Introduction to Computer Science by John M. Zelle
-
EDX.org online courses
-
Chris Orban’s Let’s Code physics simulation activities and HS coding page on Compadre
-
PICUP collaboration of activities submitted by other teachers
Data Science
-
Chris Albon’s Pandas tutorials (see the Data Wrangling section)
-
Jake Vanderplas’ Data Science Handbook
-
PBS documentary Coded Bias about algorithmic biases and their societal effects
-
Adam’s CODINGinK12.org science coding activities
-
Function to run on a Pandas DataFrame (like getting columns names or seeing unique values) and some Pandas statistical functions
-
Some Numpy functions
-
Some Pyplot functions
-
Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Perez
Physics
-
Disordered Cosmos: Dark Matter, Spacetime, & Dreams Deferred by Dr. Chanda Prescod-Weinstein
-
Quantum Diaries blog
-
PhyPhox mobile app to collect, plot, and export raw data from Apple and Android mobile devices. And it’s free.
-
Particle Physics Data Group (PDG): for example, the page on the J/ψ.
-
Teaching Relativity in Week 1 by E. R. Huggins
Colab Techniques
-
How to make a Colab link from GitHub
-
url of file on GitHub:
https://github.com/QuarkNet-HEP/coding-camp/blob/main/probability.ipynb
-
add at the beginning the Colab bit:
https://colab.research.google.com/
-
Then you have a shareable link:
https://colab.research.google.com/github/QuarkNet-HEP/coding-camp/blob/master/intro.ipynb
-
-
Reading a dataset from Google Drive
-
How to instructions (thx Jim Deane)
-
Example notebook from Peter Apps for Rolling With Rutherford
-
use the pandas read_csv() function
-
pd.read_csv(“https://drive.google.com/uc?export=download&id=DRIVE_FILE_ID”) where “file ID” is the code at the end of the sharing link
pd.read_csv("https://drive.google.com/uc?export=download&id=1bfAmN-qQ6ptoKQCmDH3u-w0mdfQEsdfq")
-
-
or use read_excel() and append /export to the end of your Google Sheet ‘view only’ link, like this:
pd.read_excel("https://docs.google.com/spreadsheets/d/1EJhtT4Ssbem1JkzcJplULYW-ojKwmg4DqQRNeElAmyw/export")
-
Workshop Goals
-
Review and reteach core concepts of particle physics, such as the framework of the Standard Model, the anatomy of a particle accelerator and detector, and the methods for calculating invariant mass from 4-vector data.
-
Review and apply basic aspects of computer programming in Python, such as conditionals, math functions and plotting, and file manipulation.
-
Use simple programming tools to analyze large datasets generated from the CMS experiment in the 2010 and 2011 runs, and run analyses of these data. Generate conclusions about these analyses that include both calculations and plots (e.g. of invariant or transverse mass).
-
Search for new scientific datasets available online and write code to perform analyses of these new data.
-
Design a series of code-centered activities that either add onto existing units in a high school physics course, or replace an already existing activity; create a plan for implementation of these activities.
QuarkNet Enduring Understandings
-
Claims are made based on data that constitute the evidence for the claim.
-
Particle physicists use conservation of energy and momentum to discover the mass of fundamental particles.
-
Indirect evidence provides data to study phenomena that cannot be directly observed.
-
Scientists continuously check the performance of their instruments by performing calibration runs, using particles with well-known characteristics.
-
Data can be analyzed more effectively when properly organized; charts and histograms provide methods of finding patterns in large data sets.
-
Data can be used to develop models based on patterns in the data.
-
Physicists use models to make predictions about and explain natural phenomena.
-
Particle decays are probabilistic for any one particle.
-
Physicists must identify and subtract “noisy” background events in order to identify the “signal.”
-
Well-understood particle properties such as charge, mass, and spin provide data to calibrate detectors.
-
The Standard Model provides a framework for our understanding of matter.
-
Research questions, experiments and models are formed and refined by observed patterns in large data sets.