The goal of this lecture is to transport the best practices of data science from the industry while developing a CORONA / COVID-19 analysis prototype

The student should learn the process of modeling (Python) and a methodology to approach a business problem based on daily updated COVID 19 data sets

The final result will be a dynamic dashboard - which can be updated by one click - of COVID-19 data with filtered and calculated data sets like the current Doubling Rate of confirmed cases

Techniques used are REST Services, Python Pandas, scikit-learn, Facebook Prophet, Plotly, Dash, and SIRvirus spread simulations + bonus section Tableau for visual analytics

For this, we will follow an industry-standard CRISP process by focusing on the iterative nature of agile development

• Business understanding (what is our goal)

• Data Understanding (where do we get data and cleaning of data)

• Data Preparation (data transformation and visualization)

• Modeling (Statistics, Machine Learning, and SIRSimulations on COVID Data)

• Deployment (how to deliver results, dynamic dashboards in python and Tableau)