Welcome! This website contains links to materials used or mentioned in the talk Computational thinking in undergraduate discrete mathematics using Python and Jupyter notebooks by Robert Talbert of Grand Valley State University, given at the AMS/MAA Joint Meetings in the MAA Contibuted Paper Session on Discrete Mathematics in the Undergraduate Curriculum -- Ideas and Innovations.

To contact Robert with more questions, email talbertr@gvsu.edu or go to Robert's website and use one of the social media contacts.

Click here for an HTML version of these slides (where the last few slides are a bit more legible).

Examples of Jupyter/Python work done in class

Links to Jupyter notebooks are actually links to HTML versions of the notebooks hosted on nbviewer. To download the notebooks themselves and edit/interact with them, use the download icon at the upper right of the screen when you click on the link.

• Recurrence relation solver (mentioned in the talk)
• Diameters of complete bipartite graphs (mentioned in the talk)
• Testing binary relations for properties
• Reverse-engineering the networkX clustering coefficient method

Other resources mentioned in/related to the talk:

• Jeanette Wing computational thinking paper (PDF)
• MTH 325 Syllabus (shows how CT is woven into the course)
• MTH 325 Technology Assessment (self-paced Python/Jupyter/Markdown/LaTeX assignment done in first three weeks of semester)
• Instructions on how to download and set up Anaconda
• SageMath Cloud (cloud-based service that provides access to Jupyter notebooks running either Python or SageMath)
• Microsoft Azure notebook server (another cloud-based service providing access to Jupyter notebooks, running Python or R)