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.
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/
Scientific Data Analysis Team (SDAT) intends to organize the first event on the value of data to provide data holders and data analyzers with an opportunity to extract maximum value from their data. This event is organized by International Statistical Institute (ISI) and SDAT hosted at the Bu-Ali Sina University, Hamedan, Iran.
Organizers and the data providers will provide more information about the goals of the initial ideas, team arrangement, competition processes, and the benefits of attending this event on a webinar hosted at the ISI Gotowebianr system. Everyone invites to participate in this webinar for free, but it is needed to register at the webinar system by 30 December 2020.
Event Time: 31 December 2020 - 13:30-16:30 Central European Time (CET)
Register for the webinar: https://register.gotowebinar.com/register/8913834636664974352
More details about this event: http://sdat.ir/en/playdata
Aims and outputs:
• Playing with real data by explorative and predictive data analysis techniques
• A platform between a limited number of data providers and hundreds to thousands of data scientist Teams
• Improving creativity and scientific reasoning of data scientist and statisticians
• Finding the possible “bugs” with the current data analysis methods and new developments
• Learn different views about a dataset.
AWARD-WINNING:
The best-report awards consist of a cash prize:
$400 for first place,
$200 for second place, and
$100 for third place.
Important Dates:
Event Webinar: 31 December 2020 - 13:30-16:30 Central European Time (CET).
Team Arrangement: 01 Jan. 2021 - 07 Jan. 2021
Competition: 10 Jan. 2021 - 15 Jan. 2021
First Assessment Result: 25 Jan. 2021
Selected Teams Webinar: 30 Jan. 2021
Award Ceremony: 31 Jan. 2021
Please share this event with your colleagues, students, and data analyzers.
The Developement of Structural and Functional Neuroimaging Symposium hold at the School of Sciences, Shiraz University in April 17 2019. The Advanced fMRI Data Analysis Workshop also held in April 18-19 2019. For more information please visit: http://sdat.ir/dns98
The Rfssa package is available at CRAN. Dr. Hossein Haghbin and Dr. Seyed Morteza Najibi (SDAT Members) have published this package to provide the collections of necessary functions to implement Functional Singular Spectrum Analysis (FSSA) for analysing Functional Time Series (FTS). FSSA is a novel non-parametric method to perform decomposition and reconstruction of FTS. For more information please visit github homepage of package.
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
SDAT is an abbreviation for Scientific Data Analysis Team. It consists of groups who are specialists in various fields of data sciences including Statistical Analytics, Business Analytics, Big Data Analytics and Health Analytics.
Address: No.15 13th West Street, North Sarrafan, Apt. No. 1 Saadat Abad- Tehran
Phone: +98-910-199-2800
Email: info@sdat.ir