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)