Academic Events - Data Science Guides

Welcome to Academic Events' Data Science documentation. Academic Events includes key dates in tertiary education calendars such as graduation, homecoming and exams for the US. This content is provided as a resource for Data Science teams to help get them up and running quickly with Academic Events.

We provide guides to using our API and data with common Data Science tools and libraries in Python. The articles include a link to Jupyter Notebooks that you can download and run. The guides include code samples and instructions for performing common tasks.

These guides consist of the following three Jupyter notebooks:

• Part 1: Data Engineering shows you how to call the Events API to extract the data to a DataFrame.

• Part 2: Data Exploration is a guide to exploring PredictHQ's Academic Events data for Data Science teams.

• Part 3: Feature Engineering provides examples of how to aggregate and extract features from the data.