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Computer engineering


Computer engineering





1-40 80 01 Computer engineering
Profilization Programmable complexes, systems and services
Language of study  English
Mode of study  Full-time
Term of study  1 year 8 months
Entrance exams for foreign citizens 


(with an indication of the main disciplines studied)

Names of master’s degree students’ activities, cycles of  disciplines, disciplines Number of credits



  • • Parallel and Reconfigurable Computing Systems
The discipline provides theoretical and practical training in the field of parallel programming and parallel computing. Mathematical models, methods and technologies of parallel programming for multiprocessor systems, systems with shared and distributed memory are considered. Practical classes are aimed at strengthening the skills of designing parallel algorithms and developing parallel programs using the Message Passing Interface (MPI) and the Open Multi-Processing (OpenMP) standard for parallelization based on multithreading. Students will be able to apply the acquired knowledge to the organization of distributed and high-performance computing, modeling and analysis of the results of fundamental physical experiments, designing parallel algorithms and developing parallel programs with different properties, as well as to optimize application programs.
  • • System Engineering
The discipline considers modern methods, standards and tools for planning and effectively implementing the full life cycle of complex systems of various types and purposes, as well as the basics of modeling using the MATLAB application software package. Students will study software system life cycle models, software engineering methods and tools, get acquainted with the international standard for software engineering SWEBOK (Software Engineering Body of Knowledge): software requirements, software design, software engineering, software testing, software maintenance, configuration management, development models and methods, software quality. As a result of studying the discipline, students will be able to develop models of subject areas, manage the system design process, apply system engineering standards, and perform mathematical modeling using the MATLAB engineering package.
  • • Systems of Enterprises' Informatization
Informatization systems ensure the collection, storage, processing, search, analysis and effective use of information necessary for decision-making in any field. This course is aimed at the development and design of information systems for enterprises of various scales and levels of development. As a result of studying the discipline, students will be able to design an information system of an enterprise (department of an enterprise, firm) using structural and object-oriented approaches, possess skills in the development, operation and maintenance of information systems, skills in using the SADT (Structured Analysis and Design Technique) methodology and the UML (Unified Modeling Language) modeling language.
  • • Network Problem-oriented Systems

The discipline examines the architectures and concepts of functioning of specialized computer systems and networks, as well as the stages of design and operation of problem-oriented systems:

  • types of architecture of computer systems, principles of their construction and functioning;
  • the main and most promising areas of development of computer system architecture;
  • computer networks: network node, resource, client, server, traffic, bandwidth;
  • classification of computer networks, features of local and global networks;
  • modern methods of designing elements and devices of computer technology, design automation tools, design documentation design;
As a result of studying the discipline, students will be able to formalize and model the processes occurring in intelligent computing systems, choose the means for building networks in accordance with the specified operating conditions, synthesize schemes of computer systems and networks, perform diagnostics of computer systems, plan and conduct experimental research, develop scientific and technical documentation.
  • • Information Systems of Decision-making Support

The discipline aims to master the basic concepts and gain practical skills in the design and development of decision support systems (DSS) and covers the following sections:

  • tasks and methods of decision-making, examples of decision-making tasks;
  • generating solutions using expert systems, generating solutions based on heuristic
  • assumptions;
  • formation and analysis of cognitive maps;
  • information technologies for decision support, goals and purpose of DSS;
  • Classification and main components of the DSS;
  • types of patterns identified by Data Mining methods;
  • DSS and Knowledge Discovery in Databases (KDD), KDD based on analysis of class pattern properties.
As a result of studying the discipline, students will know the main approaches to the design and construction of information DSS, possess the skills of analysis and formalization of the subject area, have practical skills in the design and development of DSS.
  • • Development and Internet-services Profiling
The discipline is dedicated to web service development technologies and introduces students to the most common description languages and protocols for their implementation: Web Services Description Language( WSDL), Simple Object Access Protocol (SOAP), Universal Description, Discovery, and Integration (UDDI). The course content includes
• the concept of a web service, the architecture of XML Web services;
• development of the SOAP Web service;
• Web services technology extensions: WS-Security, WS-Federation, WS-Management, and others;
• Java Web services.
As a result of studying the discipline, students will be able to design, implement, deploy and maintain web services.
  • • Automatization of Technological Design

The discipline introduces students to computer-aided design (CAD) systems used to create design and technological documentation, 3D models and drawings. The course deals with:

  • CAD structure and classification;
  • mathematical support for automation of technological design;
  • geometric modeling;
  • algorithmization of design design tasks;
  • AutoCAD and T-FLEX CAD computer-aided design systems.
As a result of studying the discipline, students will be able to use SPAR for creating drawings and three-dimensional models, for automatic and semi-automatic creation and editing of control programs, for engineering calculations and document management, as well as for working with digitized tech
  • • Big Data Basis

The discipline examines the theoretical foundations of big data, the basic elements of big data mining, and the basics of information retrieval and organization. The course content includes:

  • definitions, terms, and tasks of big data analysis (Big Data);
  • the concept of Data Mining, sources of information for Big Data, methods of data collection;
  • Map Reduce and Hadoop;
  • big data processing tools;
  • big data analytics;
  • cognitive data analysis;
  • Data Mining methods;
  • big data storage and processing technologies;
  • software tools for Big Data analysis.
As a result of studying the discipline, students will know the principles of big data analysis and be able to apply methods for analyzing and
  • • Methods of Data Mining Analysis

The discipline introduces students to the basic concepts of data mining (IAD) and the types of patterns identified using IAD. Students will learn IAD methods and software tools for data analysis to solve practical problems. The course content includes:

  • data mining: methods and tasks, formulation and classification of IAD tasks, data;
  • preliminary data analysis: cleaning, normalization, standardization, analysis of emissions and anomalous values;
  • variance, exploration, and cluster data analysis;
  • data mining process: data collection and preparation, construction, validation, evaluation, model selection and correction, data analysis, interpretation and forecasting;
  • The R–language for statistical data processing and the RStudio development environment.
As a result of studying the discipline, students will be able to perform calculations using the IAD apparatus; apply IAD methods in the R computing software environment, possess skills in developing algorithms and software systems for data analysis, as well as skills in working with automation tools for data mining and processing.

The main competencies that

the graduate will acquire

  • • Will be able to apply the methods of scientific knowledge (analysis, comparison, systematization, abstraction, modeling, data validation, decision-making, etc.) in independent research activities, generate and implement innovative ideas.
    • Will be able to identify complex causal relationships for the design of computing systems.
    • Will be able to analyze and solve scientific and technical problems that arise in the process of planning and conducting a scientific experiment.
    • Will possess modern tools for creating a virtual environment in the design of computer systems.
    • Will have the skills to perform parallel computing on multiprocessor systems.
Graduate certificate MASTER’S DEGREE DIPLOMA


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