Lubov Grigoryevna Chikina
+7(863) 2184000
ext.
11054
Professor
Institute of Mathematics, Mechanics, and Computer Science named after of I.I. Vorovich
Research interests:
information and computer technology in basic and applied physical and mathematical studies and their realization on supercomputers
Teaching:

Modern numerical methods for solving convectiondiffusion problems
The objectives of this course are:
• the development of postgraduate students of modern numerical analysis methods, providing the technological foundations of modern innovative fields of activity;
• training of graduate students to the principles of construction of numerical algorithms for the solution of contemporary problems of differential and integral equations, various problems of mathematical physics;
• training of graduate students study the properties of convectiondiffusion problems, depending on the different forms of recording tasks;
• introduce graduate students to the basic principles of the use of numerical methods for solving boundary value problems of mathematical physics;
• formation of approaches to the implementation of postgraduate studies related to the work on the dissertation

Modern methods of solving difference equations
The objectives of this course are:
• introduce graduate students to the classical difference schemes approximating the boundary value problems of mathematical physics;
• teach graduate qualitative analysis of difference schemes properties: determination of the order of approximation and study their stability;
• teach graduate students methods of solving systems of differential equations that arise in sampling the boundary value problems of mathematical physics
• introduce graduate students to the basic principles of the use of numerical methods for solving boundary value problems of mathematical physics and, in particular, boundary value problems of convectiondiffusion;
• formation of approaches to the implementation of postgraduate studies related to the work on the dissertation

R and Python languages for analyzing and forecasting financial market indicators
In this course you will learn the fundamental mathematical concepts necessary for the analysis of the data, and get the basic skills of programming in R and Python. The course is divided into two parts. The first part of the course is devoted to methods of optimization. The emphasis is on explanation of mathematical concepts and their application in practice, rather than the output of complex formulas and theorem proving. The second part of the course  practical, it is devoted to R and Python programming languages. You will get acquainted with the libraries, which are often used in practice for analysis and forecasting of financial market indicators.

Using packet R and Python environments for statistical analysis and data visualization
Python and R  popular programming languages for use with the statistics. The course consists of three parts. The first part of the course is devoted to data processing procedures and build statistical models. The second part of the course is devoted to the basic graphical capabilities of R and Python environments. The third part  practical, it is illustrated with a few dozen examples. They include a brief description of the analysis of algorithms, the main results and their possible interpretation.