Often the most innovative ideas are born out of multidisciplinary collaborations in technology and other fields, increasingly public health. In this session we will discuss what this looks like in practice by leveraging an actual use case, “Exploring Social Media indicators to Predict COVID-19 Trends''. We begin with initial data ingestion then move to data exploration and model experimentation. Finally we will discuss the path to production and publication of the outcomes of the research.

We focus on four roles: Data Engineer, Machine Learning Engineer, Data Scientist and Public Health Domain Expert. In this interactive session you will be able to walk through the end-to-end process of how these roles interact with each other. We will cover how teams can work together and transition from messy data wrangling, to experimentation, to production and, finally, publication. We will demonstrate how you can use various open source tools to build data pipelines, co-analyze datasets and perform predictive analysis using machine learning in the context of the COVID-19 pandemic.

At the end of this session you will leave with a better understanding of public health pandemic analyses and a greater understanding of data flow. When we are done we hope to inspire and empower you to work on your own project bringing meaningful insights to the world with data!

Session Repository: https://github.com/ShawnKyzer/who-ears-social-listening