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This guide collects useful information for Data Science connector courses, especially on the technical/computing side of things. It contains information about setting up your connector course and the technical infrastructure. There are useful scripts for doing routine things, some example jupyter notebooks, tutorials, and teaching pointers.
To navigate the guide, follow the links to the right.
To go straight to the code, go here (or click on the code tab above).
This guide is for connector instructors and course staff interested in creating course material that uses the same technology infrastructure as the Foundations (Data 8) course.
Specifically, if you want to use Jupyter notebooks and
optionally the datascience package (in python) in your course, this
guide is for you.
This guide may also become the home for other info for connector instructors, such as tips on working with your connector assistants (CAs).
In addition to the resources in this wiki, there is also a codebase that is meant to be useful for connector instructors. This currently includes three things:
- A collection of general "tutorial" notebooks that cover common computational problems and challenges in python
- A python module called
connectortoolsthat has functions meant to be helpful in your classes - A collection of example notebooks that demonstrate the functionality in the
connectortoolsmodule.
You can find a link to this code here (or click the "code" tab to the top left)
If you're interested in running the tutorials or examples for the connectortools module, click the image below to enter an interactive browsing session.
Some of the material is still under development. If you have a question or suggestion for something to add, please open an issue by clicking on the issues tab, then clicking new issue. Or if you're really not GitHub-savvy, feel free to ask your connector assistant to do that for you, or contact us (menu to the right).