Major: Computer Sciences
Code of subject: 7.122.04.E.73
Department: Artificial Intelligence Systems
Lecturer: Boyko Nataliya
Semester: 2 семестр
Mode of study: денна
Learning outcomes: 1. Select the data visualization mode. 2. Apply a schema (framework) for data visualization. 3. Take into account the peculiarities of human perception of information when rendered. 4. Analyze data. 5. Adapt the visual attributes to abstract data. 6. Select data visualization methods. 7. Take into account the principles of information design when rendering data. 8. Visualize the data using specialized tools.
Required prior and related subjects: prerequisite: Intellectual data analysis Methods and means of data integration The theory of decision making co-requisite: Project management process management Engineering of designing software systems Methods and tools for data and knowledge engineering
Summary of the subject: Educational discipline Visualization of data is an integral part of the cycle of professional training of specialists of the second educational qualification level "Master". The proposed training course will provide students with in-depth theoretical and practical knowledge, skills and insights related to the areas of artificial intelligence systems that will enable them to effectively carry out tasks of innovative character at the appropriate level of professional activity, which is focused on research and solving complex designing problems and the development of information systems to meet the needs of science, business and enterprises in various fields.
Assessment methods and criteria: - current control (40%): written reports on laboratory work, practical tasks, oral examination; - final control (60% of exam), testing (50%), oral component (10%).
Recommended books: 1. Savchenko, D. V. Fundamentals of processing and visualization of physical data in the OriginPro software environment 8. Computer workshop [Electronic resource]: training. manual for bachelor's degree holders in the educational program "Computer modeling of physical processes" in specialty 104 "Physics and astronomy / D. V. Savchenko. – Kyiv: KPI named after Igor Sikorsky, 2021. - 111 p. 2. Infographics: study guide / compiled by O. V. Gudim – Chernivtsi, Chernivtsi National University, 2017. – 107 p. 3. Horvat A.A., Molnar O.O., Minkovich V.V. Processing, visualization and analysis of experimental data using the Origin package: A tutorial. – Uzhgorod: Publishing House of UzhNU “Hoverla”, 2020. – 64 p. 4. Ashanin V.S., Pasko V.V. Processing and visualization of scientific research data. Tutorial. Part 1. Kharkiv: KhDAFK, 2020, 132 p. 5. Chen C. Handbook of Data VisualizaOon / C. Chen, W. Hardle, A. Unwin. - Berlin Heidelberg: Springer-Verlag, 2008. - 936 p. 6. Kvyetnyi R.N. Computer modeling of systems and processes. Calculation methods. Part 1: study guide / R. N. Kvetny, I. V. Bogach, O. R. Boyko, O. Yu. Sofina, O.M. Shushura; in general ed. R.N. Kvyetny – Vinnytsia: VNTU, 2012. – 193 p. 7. R. N. Kvyetny Filtering methods of textured images in recognition and classification tasks / R. N. Kvyetny, O. Yu. Sofina. – Vinnytsia: UNIVERSUM-Vinnytsia, 2011. – 119 p. 8. Lyubchak, V.O. Calculation methods and algorithms [Text]: teaching. manual / V.O. Lyubchak, L.D. Nazarenko. – Sumy: Sumy State University, 2008. – 313 p. 9. Matviychuk Y.M. Computer calculation methods and algorithms: teaching. manual / Y.M. Matviychuk. - Lviv: Liga-Press, 2008. - 84 p. 10. L. P. Feldman Numerical methods in computer science: a textbook / L. P. Feldman, M. Z. Zgurovsky, L. P. Feldman, A. I. Petrenko, O. A. Dmitrieva. - K.: Ed. BHV group, 2006. – 480 p.