.. _project:

Mini-Project
##############

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
    - `Kaggle <kaggle.com>`_
- 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

.. note::
    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:

.. toctree::

    tech_guide

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