To help everyone understand the value of these tools, we’ll spend two class days working on “mini-projects”. Students will form groups of two or three to:

  • obtain a (clean) dataset
  • apply a tool in our curriculum:
    • Perceptron

    • Line of Best Fit

    • Dynamical System

    • Hypothesis Testing

    • Regression (Statistical Significance)

    • Bayesian Network

  • write a brief discussion which provides
    • an examination of whether a method’s assumptions are satisfied in this application

    • discusses whether analysis outputs are trustworthy
      • (its very possible to do correct math and produce nonsense results: maybe the dataset is biased?)

    • offers a quick summary of results which is easily understood by non-technical readers


You needn’t have any prior Python programming experience to succesfully complete these tasks.

The mini-projects will be graded on the mathematical understanding of the method used, not their implementation. Additionally, students will have access to:

  • TA support in class, and all their Python expertise

  • example projects of each type to work from

It is expected that each student (of each group) gets a working Python and Jupyter Notebook installation up and running:

We hope that this experience helps students to draw the connection between the abstract “whiteboard math” and how these tools are really used.