This repository contains data science training content for DataWhys in the form of JupyterLab notebooks (.ipynb files).
These notebooks are completely portable to all JupyterLab environments but require a Blockly extension for the full user experience (see prerequisites below).
For a complete list of topics covered, see the course outline.
Each topic has an introduction/worked example notebook and an independent problem solving notebook (-PS).
All materials are in Python. See here for the same materials in R.
Click on any notebook in the repository, and GitHub will render it in your browser as a non-interactive document.
Launch a demo session by clicking on the Binder badge below.
If you've never used Jupyter or want to try the Blockly extension, check out the tutorial video below.
Instructors can use these notebooks as-is: they provide solutions to each problem.
Student versions (without answers) can be created by running the create-exercises-from-solutions.py script.
This script creates a subfolder containing all notebooks with answers removed.
Typically we place these student notebooks in their own repository and then distribute exercises using nbgitpller with TLJH. However you could also put the student notebooks in an LMS for students to download.
- JupyterLab
- JupyterLab Blockly extension (optional but strongly recommended)
The above is a minimal environment.
See the binder subfolder for the recommended conda env and JupyterLab extension installation.
Any other content-related materials, e.g. spreadsheets, should be placed in the OneDrive folder. If you create an issue that references a document in that folder, please try to link to said document.
If you want to change/correct content, either create an issue describing your change or use a git workflow to make the change.
The DataWhys Project was supported by the National Science Foundation through Grant 1918751 to the University of Memphis