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.

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

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.

1. Boyko N.I. Visualization of data: Methodical work is an electronic educational-methodical complex for students of specialty 122 "Computer sciences and information technologies" [Electronic resource]. - Access mode: http://vns.lp.edu.ua
2. Zhelezny D. Speak in the language of diagrams: a manual for visual communications for executives / D. Zelyazny. - M.: Institute for Comprehensive Strategic Research, 2004. - 220 p.
3. Quetn R. N. Computer simulation of systems and processes. Methods of calculation. Part 1: Textbook / R. N. Kvitenyk, I. V. Bogach, O. R. Boyko, O. Yu. Sofina, O. M. Shushura; per community Ed. RN Quiet - Vinnitsa: VNTU, 2012. - 193 p.
4. Quetn R. N. Methods for filtering textured images in recognition and classification problems / R. N. Kvitenyk, O. Yu. Sofina. - Vinnytsya: UNIVERSUM-Vinnytsya, 2011. - 119 p.
5. Kruglov V.V., Dly M.I., Golunov R. Yu. False logic and artificial neural networks. Study allowance M .: Publishing House of Physical and Mathematical Literature, 2001. - 224 p.

- current control (40%): written reports on laboratory work, practical tasks, oral examination;
- final control (60% of exam), testing (50%), oral component (10%).