Module 5.2: Answering Data Driven Questions Computationally

Introduction

In this module we will show some examples of using open civic data to answer a data driven question. This module will dive into several open civic datasets and use the Python programming language to manipulate and merge these datasets to gain new insights from the information they contain.

This module will also demonstrate how to download and manipulate open civic data using the Python programming language.

Guiding Questions

How can we use data and computation to learn about our communities?
How can we join datasets together to gain new insights?
What tools exist to engage and explore our data computationally?

Vignette

The librarian would like to investigate the usage of Public-wifi at various branches in their library system. Specifically, what neighborhood uses the most wifi? They use the Python programming language and Jupyter Notebooks to perform calculations and generate data visualizations. The librarian visits a local data portal, downloads the datasets of interested, and then write Python code to manipulate the data, join multiple datasets together, and produce a data visualization and answer a data driven question.

Instructional Materials

Computational Notebook

The instructional materials for this module have been written in the form of a Jupyter Notebook. The notebook is accessible in two different ways. There is a read-only mode and there is an interactive mode.