On the 14th of March, the Data Conference 2019 was held in Athens with the participation of local and international lectors who introduced the topics of Machine & Deep Learning and Data Science in Business Environment. Sergi Sergiev, the founder of Data Science Society was also invited to present the topic about open-innovation and open-source […]
From 1st to 4th of March Data Science Society was a co-host of a cool event in Sofia Lab. The event was Hack & Design Challenge vol 1: Make Games and AI (organized by Hardcore Game Jam) and it was all about meeting the AI community with the game designing community. It was not exactly […]
This article will not be useful only for data scientists, programmers, mathematicians, statisticians, and other scientists, but also for everybody. This is part of the movement for Open data and Open information. We all want to learn topics based on our interests, that comes often through books. Unfortunately, most books are paid and are often […]
What is Data Science? In this Data science online training you will understand all basics to advanced statistics and learn how to program in R & Python and how to use R & Python for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming […]
Sofia Air Pollution Case Team BG-USA: Kristiyan Vachev – Bulgaria () Sergey Vichev – Bulgaria () Stefan Panev – Bulgaria Georgi Kirilov – Bulgaria Mike Lane – USA () Data Preparation Geocoding the construction data: The original source file can be found here. Basically, this is very very similar to geocoding as proposed in the original documentation […]
Sofia is a city with significant concentrations of particulate matter less than 10 micrometers in diameter (PM10.) A high concentration of PM10 is disruptive to life and the climate. The purpose of this project is to predict the concentration of PM10 at a particular day given the climatic conditions. This is important in allowing the making of policies to reduce the pollution in the city. Our contribution consists of a random forest regressor that acheives the purpose with 70 to 80% accuracy.