Dhavide Aruliah

The goal of this tutorial is to give you hands-on experience accessing & using NASA Earthdata Cloud — i.e., freely available satellite data — through Pythonic APIs.

This tutorial walks participants — Earth scientists with some prior Python experience — through analyses of two particular climate risk scenarios: floods & wildfires. The goal is to obtain hands-on experience with common reproducible Jupyter/Python workflows based on data products from the NASA Earthdata Cloud.

Teaching & learning with Jupyter notebooks ought to be straightforward, right? Unfortunately, no, not exactly; my experience—both in developing content & in live instruction—suggests otherwise.

In this tutorial, you’ll be introduced to Sentiment Analysis (SA), the extraction of subjective, affective information from text through Natural Language Processing (NLP) to enable data-driven decisions.