Dr. Mahdi Salehi, an associate member of SDAT and assistant professor of statistics at the University of Neyshabur, introduced a useful online interactive dashboard that visualize and follows confirmed cases of COVID-19 in real-time. The dashboard was publicly made available on 6 April 2020 to illustrate the counts of confirmed cases, deaths, and recoveries of COVID-19 at the level of country or continent. This dashboard is intended as a user-friendly dashboard for researchers as well as the general public to track the COVID-19 pandemic, and is generated from trusted data sources and built-in open-source R software (Shiny in particular); ensuring a high sense of transparency and reproducibility.

Access the shiny dashboard: https://mahdisalehi.shinyapps.io/Covid19Dashboard/

Symposium of Data Science Developement and its job opportunities hold at the Faculty of Science, Shiraz University in Feb 20 2019. For more information please visit: http://sdat.ir/dss97 

The concept is certainly compelling. Having a machine capable of reacting to real-world visual, auditory or other type of data and then responding, in an intelligent way, has been the stuff of science fiction until very recently. We are now on the verge of this new reality with little general understanding of what it is that artificial intelligence, convolutional neural networks, and deep learning can (and can’t) do, nor what it takes to make them work. At the simplest level, much of the current efforts around deep learning involve very rapid recognition and classification of objects—whether visual, audible, or some other form of digital data. Using cameras, microphones and other types of sensors, data is input into a system that contains a multi-level set of filters that provide increasingly detailed levels of differentiation. Think of it like the animal or plant classification charts from your grammar school days: Kingdom, Phylum, Class, Order, Family, Genus, Species.