Monday¶
Objectives¶
- Learn how to connect a GitHub project to TravisCI to enable testing and coverage reporting on push.
- Learn about the Binary Search Tree and how it is structured.
Readings¶
Please read and follow the installation instructions for conda at the top of this page
Questions about Pandas? Read Nick’s quick Pandas tutorial.
(And you can download the Titanic data as a CSV file to work with it)
These readings will support your work on the Data Model for our Django Imager website.
- Django Models API
- Django Model Managers
- Django’s Signal Framework, Built-In Signals and Signal Handling (You’ll also want to read up on using the new App Config system to register signals and signal handlers)
- Django Testing (familiarize yourself both with writing and running tests and with the tools Django provides for testing.
- Using Dynamic Test Fixtures with Factory Boy (in particular pay attention to using Factory Boy with Django)
Review¶
Concepts¶
- Overview
- Statistics 1: Descriptive Statistics
- The Problem
- The Solution (to start)
- Averages/Means - Getting a Feeling for the Data
- Averages/Means - Do It With Code!
- Just stick with Numpy for your means...
- Always visualize your data
- Averages/Means - Limitations
- np.histogram - a valuable tool
- Averages/Means - Limitations
- Median - The Literal Middle
- Standard Deviation - The Spread of the Data
- Standard Deviation - The 68-95-99 rule
- Mode - The most frequent thing(s)
- Quantiles - Values some fraction of the way into something
- Automate Tests with Travis CI
- Binary Search Tree
K-Nearest-Neighbor lecture notes. (Click on the “binder” link to open an Jupyter Notebook in the cloud, or download the repository and run it locally in your Conda env)
Demo¶
- Connecting a GitHub Project to TravisCI