These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work.

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Data is everywhere,… but who is able to handle the right data?


Decomposition of growth into S-shaped logistic components also known as Loglet-analysis is a better predictor for the covid-19 spread, as it takes into account the evolution of multiple waves.

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In the last article we showed how to make a forecast for the next 30 days using covid data from the Johns Hopkins Institute with KNIME, Jupyter and Tableau. The projections were optimized for a logistic growth model. We will show that the decomposition of growth into S-shaped logistic components also known as Loglet analysis, is more accurate as it takes into account the evolution of multiple covid waves.

The term “Loglet”, coined at The Rockefeller University in 1994 joins “logistic” and “wavelet”. The Loglet analysis is interesting because it can handle multi-peak profiles which is a common challenge for curve fitting techniques. …


Make projections for covid 19 for the next 30 days by combining KNIME for data integration, jupyter to fit models and Tableau to create visualizations.

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While the USA is already in the midst of the third covid19 wave, Austria has just declared the second lockdown.

About

Dennis Ganzaroli

Data Scientist and Head of Report & Data-Management in a big Telco in Switzerland

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