Dive into the modules!
So you have some knowledge of Python and programming in notebooks and in environments like Jupyter Hub, Posit, and Google Colab, and you’re ready to start implementing data science in your classroom. On this page, I’ve highlighted three modules that would be great places to start for students without much previous exposure to data science.
Before we proceed...
if you are a faculty member looking for solutions to these modules, they are available. You must have a GitHub account to access them. You can make a free account here . Once you have this account, you can either email DSECOP and ask for access (make sure you give them your GitHub username so they can add you), or you can join the slack community and request access there. On the slack, you can connect with the fellows who wrote the modules and to others who may be implementing the modules in their classes as well.
if you are a faculty member looking for solutions to these modules, they are available. You must have a GitHub account to access them. You can make a free account here . Once you have this account, you can either email DSECOP and ask for access (make sure you give them your GitHub username so they can add you), or you can join the slack community and request access there. On the slack, you can connect with the fellows who wrote the modules and to others who may be implementing the modules in their classes as well.
Intro to Data Processing with Histograms
This would be a great module to slot into an intro physics lab, and it requires just the basics of the Python programming skills laid out in the last section.
Here are the learning goals and pre-requisites for this module (please note it can be modified as needed).
Here are the learning goals and pre-requisites for this module (please note it can be modified as needed).
Exploratory Data Analysis
This would be a great module for a lab course, a course on computational or data science techniques in physics, or even for an introductory physics lab where the students have already worked with some of the ideas of data science. It also requires just the basics of the Python programming skills laid out in the last section.
Here are the learning goals and pre-requisites for this module (please note it can be modified as needed).
Here are the learning goals and pre-requisites for this module (please note it can be modified as needed).
Intro to Data Science Libraries
For classes where you want to use more of the data science tools -- or work with especially large data sets -- this module will equip your students with the tools they need. It requires just the basics of the Python programming skills laid out in the last section, and then builds on those to add familiarity with commonly-used libraries for data science in Python.
Here are the learning goals and pre-requisites for this module (please note it can be modified as needed).
Here are the learning goals and pre-requisites for this module (please note it can be modified as needed).
