On Friday, July 8, 2022, the three-week International Summer Scientific Professional School of “Data Analysis” for students and pupils of leading technical educational institutions of Ukraine and the world began its work.
The organizers of the school are the Department of Mathematical Modeling and Data Analysis of Educational and Research Institute of Physics and Technology NTUU “Igor Sikorsky Kyiv Polytechnic Institute”, University of Maryland (USA), Anhalt University of Applied Sciences (Germany), in cooperation with representatives of the National University “Kyiv-Mohyla Academy“. The participants of the school are students of these universities, technical university Blekinge Tekniska Högskola (Sweden), as well as students of leading Kyiv lyceums, in particular Lyceum “Leader” and Technical Lyceum of NTUU “KPI”.
The summer school is one of the steps in the creation of the Ukrainian-German center of key competences AIDA&TI, which is being created on the basis of Institute of Physics and Technology NTUU “Igor Sikorsky Kyiv Polytechnic Institute” and Anhalt University of Applied Sciences (Germany).
The summer school program is aimed at developing students’ and schoolchildren’s skills in working primarily with data and their analysis, as well as the ability to independently create educational data sets using the example of geospatial data. In the conditions of war, the subject of practical tasks is the monitoring of changes in the earth’s cover, as well as the use of neural network algorithms to assess the damage of agricultural territories from the war using satellite images.
Students will have the opportunity to communicate with professors of the Department of MMDA NTUU “Igor Sikorsky Kyiv Polytechnic Institute” by Nataliia Kussul and Eduard Siemens of the Anhalt University of Applied Sciences (Germany) about the urgency of solving such problems at the state level and about the possibility of actively participating in real projects on a given topic, provided that the summer school is successfully completed.
In the classes, our students will try to create their own data set to build a land cover classification map using the simplest classification algorithms, as well as learn the initial steps of working with the open cloud platform Google Earth Engine.