Faculty 

Specialty 

• • Interview to determine the level of proficiency in the language of instruction
  
• • Additional interview in academic disciplines corresponding to the profile of the chosen specialty

Curriculum

(with an indication of the main disciplines studied)

• • Mathematical modelling and optimization of complex systems

Study of the theoretical foundations of mathematical modeling and optimization, principles and practical methods for constructing and analyzing models of complex processes, phenomena and systems.

Solving optimization problems in various fields of science, technology and economics.

Formation of practical skills in applying mathematical modeling and optimization methods to solve problems of modeling and optimizing complex systems using modern software

• • Multivariate statistical analysis

Study of mathematical models and methods of statistical analysis of multidimensional structure data.

Formation of practical skills for solving applied problems of multidimensional data analysis using modern software in the field of statistical analysis

• • Mathematical and computer prediction

Obtaining theoretical knowledge and practical skills on methodological foundations and methods of making forecasts based on mathematical models.

Building predictive models.

Mastering the methods of constructing econometric models based on spatial data and time series, building econometric models based on economic and statistical data.

Mastering computer software packages for economic and statistical data analysis and forecasting

• • Special data structures

Creating a database for using modern libraries in various programming languages.

The ability to adapt existing algorithms and data structures for specific application tasks.

Development of the ability to evaluate the complexity of operations with data structures, perform worst-case estimation and amortized estimation of operations, use probabilistic estimation of the complexity of operations.

Learning about special data structures.

Study of methods of organizing efficient search, optimization of this method, using various data structures.

• • Technologies and computer data processing systems

Study of modern computer systems for statistical data processing.
The most commonly used statistical methods of data processing and the principles of forming conclusions based on their application, the features of implementing algorithms for statistical methods of data processing in various modules of computer data processing systems.
Using computer systems in solving applied problems related to data processing

• • Fundamentals of programming on Python

Mastering the syntax of the Python programming language and acquiring practical programming skills in Python

• • Mathematics and Python for data analysis

Acquisition of practical skills in using mathematics to solve data analysis problems using the Python programming language

• • Introduction to computer-assisted teaching on Python

Study of various models of neural networks, methods and algorithms of their training for solving practical problems with the use of ready-made libraries of the Python programming language

• • Applied problems of data analysis on Python

Study of methods for solving applied problems of data analysis using  Pandas module of the Python programming language

• • Multivariate statistical analysis on panel data

Construction of econometric models based on panel data, methods of conducting econometric analysis, consisting in the diagnosis of models.

Methods for developing forecasts based on econometric models.

• • Numerical methods of computer modeling and analysis

Mastering and obtaining practical skills in algoritmization and programming methods of computational mathematics and numerical optimization methods.

Using modern computer modeling software in solving problems from various fields of human activity

• • Data analysis in logistics

Development, solution and analysis of logistics problems based on economic and mathematical methods and models and computer technologies: construction of economic and mathematical models during logistics research.

Identification of the most significant quantitative relationships of the modeled logistics objects.

Mastering the techniques of mathematical formulation of individual connections and phenomena of logistics systems.

Acquisition of theoretical knowledge of basic economic and mathematical models and methods of transport logistics and inventory management

• • Features of computer data analysis by the types of economic activity

Construction and application of optimization models and methods of applied statistics for the analysis of statistical data in various spheres of socio-economic development of the region on the basis of modern computer technologies.

Using modern information technologies to solve the problems of state regulation, forecasting and planning of state revenues and expenditures.

Evaluation of results, including financial and economic analysis of economic processes and production activities.

Using scientific achievements and development of proposals for improving professional activities in the field of finance, money circulation and credit.

Development of practical recommendations for the use of scientific research in the field of finance, money circulation and credit, planning and conducting experimental research

• • Mining technology

Introduction to the basic concepts and types of patterns identified by data mining, study of IAD methods, training in the use of software tools based on data mining technology for solving practical problems

• • Methods and means of data visualization

Study of the main approaches and methods of graphical representation and data analysis.

Formation of practical skills and skills of working with tools for data visualization, including business intelligence systems.

• • Discrete optimization

Mastering the statements and features of discrete programming problems.

Study modern methods for solving discrete programming problems.

• • Mathematical methods of management in incomplete information conditions

Mastering methods, techniques, procedures, models and software tools for analyzing decisions in conditions of multivariance, multicriteria, uncertainty and risk, acquiring modeling skills for decision-making tasks in conditions of incomplete information

• • Fundamentals of computer data analysis using R language

Systematic study of the R programming language.

Study of methods for solving the main applied problems of statistical data analysis by R language (data types in R, methods for creating loops, checking conditions, rules for writing functions, handling exceptional situations, writing and reading data from files and databases, working with graphs, exporting reports to pdf-file, etc., finding descriptive statistics).

• • Analysis and forecasting in economics and finance using time series in Mathematica

Study of time series analysis methods based on higher-order statistics with implementation in the Mathematica package

The main competencies that

the graduate will acquire

• • apply effective mathematical methods and computer technologies, as well as specialized software to solve scientific and practical problems;
  • develop and apply effective methods, algorithms and software tools for computer data analysis;
  • create and explore models of complex systems and processes of nature;
  • develop intelligent decision support systems;
  • solve problems of computer modeling and optimization of systems and processes of nature, management of data mining processes, simulation modeling, statistical modeling and forecasting.

Options to continue education

(postgraduate specialties)

Faculty 

Specialty 

• • Interview to determine the level of proficiency in the language of instruction
  
• • Additional interview in academic disciplines corresponding to the profile of the chosen specialty

Curriculum

(with an indication of the main disciplines studied)

• • Control Methods in Complex Systems

• • Methods of Computer-assisted Teaching

• • Neural Network Technology

• • Technologies of Digital Business Transformation

Digital transformation is the introduction of modern digital technologies into the business processes of an enterprise. It involves not only the use of modern hardware or software, but also fundamental changes in management approaches, corporate culture, and external communications. The discipline examines the methods of digital transformation caused by the introduction of Industry 4.0 technologies, in particular technologies based on the Internet of Things platform. The acquired practical skills will allow you to apply technologies, models, tools and digital business transformation; provide basic knowledge on the use of services to ensure cybersecurity in the context of digital business transformation

• • Project Management in Automatization Sector

Project management is a recognized methodology for effective management, which is especially relevant in the context of the Industry 4.0 economy. While studying the discipline, students will get acquainted with the principles, tools and technologies that allow them to competently initiate, plan, execute, monitor and control the implementation of the plan, budget, requirements for the content and quality of the project, successfully complete the project, and effectively manage the project team. The acquired practical skills will allow you to design IT products; choose, justify and try on a project management methodology; use modern project management software systems.

• • Knowledge Management Technologies and Intelligent Information Systems

• • Contemporary Threats and Data Security Technologies

The purpose of the discipline is to gain knowledge on the issues of ensuring the protection of information in cyber-physical systems built on the Internet of Things platform. We consider systems that are currently being rapidly implemented in critical areas – medicine, transport, manufacturing, but, at the same time, carry a number of threats and vulnerabilities associated with the use of modern IT. The knowledge and skills gained in the course of studying the discipline will be useful in the development and use of smart systems and the analysis of the problems of their complex information protection.

• • Fundamentals of Data Science

Within the framework of the discipline, methods of building and applying complex data processing systems are considered. Modern methods of data processing (receiving, storing, processing) and analysis using Python libraries for data representation and analysis, machine learning, and visualization are studied and systematized. The acquired knowledge and practical skills will allow you to use batch and distributed data technologies; mathematical methods and tools for statistical processing and data mining.

• • Methods and Algorithms of Unstructured Data Distributed Processingta

The discipline is aimed at developing students ' professional competencies in the development and use of big data processing and analysis systems (Big Data). Methods of using Big Data distributed processing systems based on Map Reduce, Hadoop, Apache Spark technologies, methods of analyzing graph structures of social networks are considered. The acquired practical skills will allow you to develop decision-making automation systems; design and use distributed data processing systems and best practices in the development of competitive software products

The main competencies that

the graduate will acquire

• • Will be able to make decisions based on the results of the application of scientific methods, assess the completeness of information in the course of professional activity, if necessary, fill in and synthesize missing information, work in conditions of uncertainty.
  • Will be able to carry out a critical analysis of problem situations based on a systematic approach, develop an action strategy.
  • Will be able to identify and implement the priorities of their own activities and ways to improve them based on self-esteem.
  • 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 apply methods of analysis and synthesis of optimal and adaptive control systems.
  • Will be able to apply modern technologies and tools in information systems.
  • Will be able to apply systems analysis methodology when designing complex systems.

Faculty 

Specialty 

• • Interview to determine the level of proficiency in the language of instruction
  
• • Additional interview in academic disciplines corresponding to the profile of the chosen specialty

Curriculum

(with an indication of the main disciplines studied)

• • Parallel and Reconfigurable Computing Systems

• • System Engineering

• • Systems of Enterprises' Informatization

• • 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;

• • 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.

• • Development and Internet-services Profiling

• • 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.

• • 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.

• • 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.

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.