Writing a Tutorial

A tutorial in this repository is referred to a Jupyter notebook which is written for a dataset to either do a deep analysis of it, or apply some ML technique to it. Some possibilities for tutorials are as follows:

• Walk through of a dataset, go over its main features, use data visualization to tell a story about the dataset
• Correlation/causation analysis
• Time series analysis
• Supervised Learning, such as Classification, Regression, Forecasting, etc
• Unsupervised Learning, such as clustering

Choose a Dataset

Make sure the dataset you choose is tabular and onboarded by our team. There should be a directory available for that dataset here.

Write your Tutorial

Your tutorial can be anything you want, as long as it shows something interesting about the dataset.

Colab Development

You may start by downloading a copy of the template and upload it to Colab.

Development Tips

• While Colab offers many cool macros and shortcuts, we ask you not to use them, since these tutorials should also be runnable in Workbench, and locally.
• To help the reader understand your tutorial easier, make sure to add enough description in markdown cells before your code cells.
• When you are done with your code, try and download your notebook to your local machine and run it locally to make sure it still runs without any issues.