The study course consists from the following topics:
-introduction to computer program process parallelization and cloud technology usage.
-CPU several core usage in one calculation process.
-cloud technology usage opportunities.
-practical classes connected to CPU several core usage into one calculation process.
-practical classes connected to cloud technology usage in computer program.

Data management and mining supply methods and technologies can be used to transform available data into the useful information and knowledge. Data management solves the data retrieval, transformation and structuring tasks. As a result the data is prepared for the analysis. Data mining solves the data analysis tasks, giving ability to find yet unknown relationships in data. Data mining results let enterprises take correct and wise decisions.

The course on the fundamentals of ontology in data mining is intended to provide information technology students with an insight into knowledge structures and their possible applications. The aim of the course is to inform about the approaches and tools that allow the acquisition, description, structuring and use of formally described and structured knowledge. It offers the opportunity to acquire the skills necessary for the creation and application of a knowledge base.

The course is devoted to the construction of artificial neural networks and includes the following sections: Biological neuron; Artificial neuron; Single layer perceptrons; Straightforward chain networks; Architecture; Learning procedures in single-layer and multi-layer networks; Backward chain networks; Adaptation procedures; Associative memory; Concurrent learning; Software; Neural network applications.

This module is devoted to the construction of artificial neural networks and includes the following sections: Biological neuron; Artificial neuron; Single-layer perceptrons; Straightforward chain networks; Architecture; Learning procedures in single-layer and multi-layer networks; Backward chain networks. Adaptation procedures; Associative memory; Concurrent learning; Software; Neural network applications for information processing.