Clean Data
Video Walkthrough
YouTube Video
Resources
Teacher Notes
When doing this as part of an outreach activity, often this section is just a discussion and the full clean_data.py script is provided to students so they can quickly clean their raw data and move ahead to the next part. However, this can easily be expanded to a larger activity to actually build and test the clean_data.py script or perform additional data cleaning processes in a spreadsheet program.
Also, it is worth noting that the clean_data.py script as provided has a commented out line that does not process any missing data (denoted by an "M" in the Kansas Mesonet data) - that will be handled later in the lesson when it becomes an issue.
Teacher Guide
A STEM outreach activity using data science and machine learning techniques to analyze weather data.
- Time: 60 - 120 minutes+
- Age: Middle School and Up
Teacher Introduction Video
YouTube Video
Starter Code
The starter code can be found on GitHub. This repository is a template that can be easily forked by students or teachers to their own accounts.
The files can also be downloaded as ZIP file from the 1.0 Release and then redistributed through a course LMS or other process.
Teacher Setup
For students to follow along with this activity, they need access to system with Python and Jupyter Notebooks installed and available. There are many possible configurations that work, which are discussed below. If you have questions or need assistance setting things up, reach out to altcs AT ksu DOT edu and we’ll do our best to help!
Simple - Local Installation
The simplest setup is to have Python and Visual Studio Code installed on the system locally. Then, within Visual Studio Code, install the Python and Jupyter extensions. If the folder containing the starter files are opened in Visual Studio Code, you should be prompted to install those extensions if they aren’t already installed.
Devcontainers and GitHub Codespaces
The starter files also contain a .devcontainer folder that can be used to create a local [Development Container] using Docker, or a cloud development environment using GitHub Codespaces. That environment will contain the Python installation and handle installing the required extensions and libraries. Students can create their own copies of the starter code using the template repository on GitHub.
Codio
The Codio platform supports Jupyter Notebooks and could be used to deliver this lesson. Teachers will need to configure the lesson in Codio for students to use.
Google Colab
Google Colab is another possible option for students to engage with this lesson online. Students can start by clicking on the link below to open the starter file in Google Colab (the same link can be found on GitHub).
It requires a bit of additional configuration:
- Run the code block at the top of the file to clone the repository and install the required libraries.
- Remember that all Python commands must be run within code blocks and not the terminal. See the example included in the starter notebook.
Model Solution
The starter repository contains a solution folder that contains the intended model solution at the end of the activity. Since this activity is meant to be exploratory in nature, it can be helpful to give students an option to either write the code themselves or follow along with a model solution and make changes to the existing code. Teachers may choose to remove that folder if they redistribute these materials.
