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Yanka Kupala State University of Grodno will accept documents from applicants from July, 20 to August, 9 The terms of the entrance campaign are determined in Belarus

The Ministry of Education of the Republic of Belarus has set the deadline for the entrance campaign to higher education institutions in 2021. From July, 20 to 26, documents for higher education will be accepted at the expense of budget funds and on a paid basis. Applicants entering full-time (full-time) education in higher education institutions of agricultural profile at the expense of budget funds and on a paid basis, must submit documents in the period from July, 20 to 28. For graduates of specialized classes of pedagogical and agricultural orientation, entering pedagogical and agricultural specialties, the deadline for submitting documents is from July, 20 to 22. Acceptance of documents from applicants entering the correspondence form of education in institutions of higher education of agricultural profile at the expense of budget funds and on a paid basis will be carried out from November, 15 to December, 5.

For applicants who enter for a fee and do not pass the entrance tests, the following deadlines for submitting documents are established: from July, 20 to August, 5-at the Academy of the Ministry of Internal Affairs, the Mogilev Institute of the Ministry of Internal Affairs, the University of Civil Protection of the Ministry of Emergency Situations; from July, 20 to August, 10 – in full – time (full-time) education in agricultural specialties (from November, 15 to December, 5 in correspondence education in this direction); from July, 20 to August 9-at other institutions of higher education.

Terms of entrance tests at the University of Civil Protection of the Ministry of Emergency Situations, the Military Academy of the Republic of Belarus, at military faculties in institutions of higher education - from July, 27 to 29; in other institutions of higher education – from July, 27 to August, 4; in full – time (full – time) form of education in agricultural specialties-from July, 29 to August, 4; in correspondence form of education in institutions of higher education of agricultural profile-from December, 6 to 15.

The terms of enrollment of applicants for state-funded places are also defined: at the Military Academy of the Republic of Belarus, the Institute of the Border Service of the Republic of Belarus, the University of Civil Protection of the Ministry of Emergency Situations, at military faculties in higher education institutions-until July, 31; at the Belarusian State Academy of Arts, the Belarusian State Academy of Music, the Belarusian State University of Culture and Arts-until August, 6; in other state institutions of higher education (except for correspondence education in institutions of higher education of agricultural profile) - until August, 5; in correspondence education in institutions of higher education of agricultural profile – until December, 20.

For places for obtaining higher education on a paid basis in higher education institutions (except for correspondence education in agricultural higher education institutions), the terms for enrollment are set until August, 11, and for correspondence education in agricultural higher education institutions-until December, 20.

The decree of the Ministry of Education of the Republic of Belarus, published on the National Legal Portal, also specifies the terms of additional recruitment to state institutions of higher education and the procedure for conducting an entrance campaign for foreigners and stateless persons.

Representatives of Krasnoyarsk State Pedagogical University named after V. P. Astafyev visited Yanka Kupala State University of Grodno as a part of the "Visiting Professor" program

The visit to Yanka Kupala State University of Grodno took place from April 12 to 17.
As a part of the visit to Yanka Kupala State University of Grodno, Professor of Krasnoyarsk State Pedagogical University named after V. P. Astafiev Lyudmila Klimatskaya read lectures for students of the Faculty of Physical Education on the disciplines "Fundamentals of Sports Nutrition" and "Theory and Methodology of Health-improving Physical culture". Associate Professor Yulia Bocharova read lectures for students of the Faculty of Physical Education on the discipline "Pedagogy", and for students of the Faculty of Pedagogy – on the basics of didactic heuristics, on special methods of school education and the basics of practical work of a speech therapist.
Colleagues from Grodno and Krasnoyarsk also discussed the results of the first year of implementation of the joint Belarusian-Russian scientific project "Phenotypes of psychovegetative response to the factors of gerontological ageism and its counteraction in social service organizations in Krasnoyarsk and Grodno". Professor Lyudmila Klimatskaya and Associate Professor Yulia Bocharova shared their experience of scientific and practical activities carried out between the universities under the cooperation agreement. During the visit, by the way, a scientific publication was published in the journal Family Medicine & Primary Care Review (Web of Science), reflecting the first results of the joint scientific project.

А consultative seminar on the joint content generation for the visualization of the ENI-LLB-2-359 project

On 19 November, 2020, the consultative seminar on the joint content generation for the visualization of the project “Preservation of the ethnocultural heritage and the development of tourism in the historical region of traditional beekeeping” ENI-LLB-2-359 was held on the basis of the Faculty of Biology and Ecology at Yanka Kupala State University of Grodno.

A training seminar for students on the project "Preservation of the ethnocultural heritage and the development of tourism in the historical region of traditional beekeeping" ENI-LLB-2-359

On 13 April, 2021, the training seminar for students on the project " Preservation of the ethnocultural heritage and the development of tourism in the historical region of traditional beekeeping" ENI -LLB-2-359 was held on the basis of the Faculty of Biology and Ecology at the Yanka Kupala State University of Grodno. The seminar was attended by employees and students of the Faculty of Biology and Ecology, the Faculty of History, Communication and Tourism, the Faculty of Law and project partners – employees of the State environmental institution "Republican landscape reserve "Ozery".

The results of the IV Regional Olympiad in Russian as a Foreign Language were summed up at Yanka Kupala State University

Participants of the Olympiad were 15 students and students of preparatory courses of universities and colleges of the Grodno region.
Foreign citizens from Brazil, Congo, Ethiopia, Lebanon, Mali, Togo, Cameroon, and Sri Lanka, who are only studying Russian in their first year of study, took part in the intellectual competition.
The Olympiad consisted of three stages. The participants demonstrated to the jury their understanding of authentic oral speech, performed a lexical and grammatical test, and made an oral statement on one of the proposed topics.
The representative of Kupala University, Santos Marques da Silva Guilherme, won the IV Regional Olympiad in Russian as a Foreign Language and will take part in the Republican Olympiad in Russian, which will be held in May in an online form. The second place is also taken by the representative of our University, Zongwe Matilda Kianda. The third place was shared by representatives of Yanka Kupala State University of Grodno Kanonge Priska Kabambi and Grodno State Medical University Mohamed Aslam Fatima Hasnaa.

IV Regional Olympiad in Russian as a Foreign Language is held at Yanka Kupala State University

15 students and students of preparatory courses of universities and colleges of the Grodno region take part in the Olympiad.
These are citizens of eight countries - Brazil, Congo, Ethiopia, Lebanon, Mali, Togo, Cameroon, Sri Lanka - who are only studying Russian in their first year of school. Participation in the Olympiad is a great opportunity to check what real success they managed to achieve during the first year of study.
– The Olympiad in Russian as a foreign language is held by the Department of Language Training of Belarusian and Foreign Citizens of Kupala University for the fourth time. This is a regional Olympiad, which is attended by students and students of all educational institutions of the Grodno region. And the jury that will determine the winners and prize – winners of the Olympiad will include representatives of all three Grodno universities, – said Inna Samoilova, Head of the Department of Language Training of Belarusian and Foreign Citizens of the Faculty of Pre-University Training of Yanka Kupala State University.
She also noted that such intellectual competitions help motivate foreign students to further study the Russian language and culture. This is important because knowing the language afterwards helps them to adapt more quickly to the student environment.
– Foreign students and students really strive to learn the Russian language, even if they study at the university in English, - said Inna Samoilova. – After all, they live in our country, are mainly in the Russian-speaking environment. It is important for them to interact with other students, build relationships, and understand each other.
Participants of the Olympiad will have to pass three stages of tests. They will have to demonstrate an understanding of authentic oral speech, perform a lexico-grammatical test and make an oral statement on one of the proposed topics before the jury. For example, they will be asked to talk about choosing their own path and the importance of knowledge, about love and the value of health, about mistakes, life's victories and defeats. It is important to note that the participants of the Olympiad will learn their topic for oral statements immediately before the speech.
According to the results of the Olympiad, the winners and prize-winners will be determined. They will be awarded certificates and gifts-souvenirs and books that will help them learn more about Belarus, its traditions, and also contribute to further improving their knowledge of the Russian language.

Alexey Dudorov, a student of Yanka Kupala State University, won gold at the European Trampoline Championship The European Trampoline Championships were held from April 29 to May 2 in Sochi

The large-scale competitions in Sochi brought together 300 athletes from 23 countries. Over the course of four days, athletes competed for medals in individual and team disciplines: individual jumps, acrobatic track and synchronized trampoline jumping.
The Belarusian national trampoline team successfully competed at the European Championships in Sochi. The team consisted of a 4th-year student of Kupala University Alexey Dudorov. Together with his teammates Ivan Litvinovich, Oleg Ryabtsev, Vladislav Goncharov, Alexey rose to the highest step of the podium, winning the "gold" of the championship in the team competition.

 

Applied Mathematics and Informatics

 

Faculty 

FEU

Specialty 

1-31 80 09 Applied Mathematics and Informatics
Profilisation Computer Data Analysis
Language of study  English / Russian
Mode of study  Full-time
Term of study  1 year 8 months
Entrance exams for foreign citizens 

Curriculum

(with an indication of the main disciplines studied)

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

 

 

  • • 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

3
 
  • • 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

3
 
  • • 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

6
 
  • • 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

3
 
  • • Fundamentals of programming on Python

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

3
 
  • • Mathematics and Python for data analysis

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

3
 
  • • 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

3
 
  • • 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.

3
 
  • • 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

3
 
  • • 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

3
 
  • • 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

 3
 
  • • 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.

6
 
  • • Discrete optimization

Mastering the statements and features of discrete programming problems.

Study modern methods for solving discrete programming problems.

Developing skills in using software products for implementing discrete programming algorithm.
3
 
  • • 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).

3
 
  • • 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)

• 08.00.13 – Mathematical and Instrumental Methods of Economics
Graduate certificate MASTER’S DEGREE DIPLOMA

 

 

Industrial Automation

 

Faculty 

FMI

Specialty 

1-53 80 01 Industrial Automation
Profilization Analysis and management in digital economy systems
Language of study  English
Mode of study  Full-time
Term of study  1 year
Entrance exams for foreign citizens 

Curriculum

(with an indication of the main disciplines studied)

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

 

 

  • • Control Methods in Complex Systems
Within the framework of the discipline, methods for solving the main problems of control theory are studied. The issues related to the main methods of mathematical description and research of continuous and discrete automatic control systems are considered. The study of the discipline will allow you to develop the skills of studying specific systems, determining their main quality indicators, solving optimization problems, including using computer mathematics packages
3
 
  • • Methods of Computer-assisted Teaching
Machine learning methods are the basic technology of artificial intelligence. Within the framework of this discipline, theoretical and practical aspects of machine learning technologies, modern methods of restoring dependencies based on empirical data are studied. The issues of building and training models and evaluating their quality are considered. Skills for practical solutions to big data mining problems are also being developed in many financial, medical, commercial, and scientific applications.
3
 
  • • Neural Network Technology
Within the framework of the discipline, methods of mathematical modeling of artificial intelligence systems based on neural networks are studied. Students will get acquainted with the methods of design, training, analysis and practical application of neural networks in solving complex formalized engineering problems. The acquired practical skills will allow you to use modern software tools for building and training neural networks.
3
 
  • • 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

3
 
  • • 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.

3
 
  • • Knowledge Management Technologies and Intelligent Information Systems
Within the framework of this discipline, you will gain a systematic knowledge of the main models, methods, tools and languages used in the development of artificial intelligence systems. Get acquainted with the main methods of finding solutions used in artificial intelligence systems. You will develop analytical and practical skills that will allow you to navigate in the choice and use of methods, tools and languages in solving modern problems
3
 
  • • 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.

3
 
  • • 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.

3
 
  • • 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

3

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.
Graduate certificate MASTER’S DEGREE DIPLOMA

 

 

Computer engineering

 

Faculty 

FMI

Specialty 

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 

Curriculum

(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.
4
 
  • • 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.
6
 
  • • 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.
3
 
  • • 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.
3
 
  • • 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.
6
 
  • • 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
3
 
  • • 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
3
 
  • • 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.
3

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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