Join this interactive live coding session with Damiaan Zwietering from IBM, as we look at modeling and making sense of COVID data.
During the early stages of the COVID pandemic there was a lot of discussion about flattening the curve in order to prevent overload on our health care system. So what exactly is that curve, and how do you fit it to the raw case report data coming in?
In this session, we will discuss several approaches to understanding this data, and look at when and how they work. Once you understand the nature of outbreaks, you can start comparing them using the available context.
As an example, we will try to make sense of the influence of measures such as lockdown and economic intervention using state of the art data modeling and explanation tools such as XGBoost and SHAP. In this process, we will encounter many of the usual pitfalls of data modeling and discuss the typical solutions for these.
This session will provide a great an example of refining and modeling data to generate useful results and will be suitable for both interested (beginner) and experienced participants.
The format will be an interactive live coding session, during which Damiaan will show his research from the last couple of months in a live environment and will interact with participants of the session through the chat function.
After the session, we will share all code and analysis with you as Python notebooks from our git repository so you can pick it up for further exploration.
Sign up and join us on 21st July!
About the speaker
Damiaan Zwietering is an IBM specialist who spent his career on achieving real world results innovating with information. He was a developer, consultant, architect and sales engineer in the area of data warehousing, business intelligence and advanced analytics before his current position as a developer advocate for data science, specializing in the practical application of machine learning and artificial intelligence.