4.1 Course(s) corresponding to requirement in Algebra and geometry (including course code; name; local credits; local grade; URL)
CS.18057; Advanced Calculus; 10; 90; -

4.1 b) Course descriptions (including course code; name; description)
The course "Advanced Calculus" consists of 2 main parts:
"Linear Algebra and Computational geometry" part includes:
- matrices and actions on them;
- systems of algebraic equations;
- vectors on the plane and in space;
- elements of linear space theory;
- second-order curves, second-order surfaces;
"Calculus" part described in pt. (4.2 b)

4.2 Course(s) corresponding to requirement in Calculus in one and several variables (including course code; name; local credits; local grade; URL)
CS.18057; Advanced Calculus; 10; 90; -

4.2 b) Course descriptions (including course code; name; description)
The course "Advanced Calculus" consists of 2 main parts:
"Linear Algebra and Computational geometry" part described in pt. (4.1 b)
"Calculus" part includes:
- general concept of functions;
- limit of numerical sequence;
- function boundary and continuity;
- derivative and differential functions;
- indefinite, definite integrals;
- differential and integral calculus of functions of one and many variables;
- theory of differential equations;
- theory of numerical and functional series;

4.3 Course(s) corresponding to requirement in Probability theory and statistics (including course code; name; local credits; local grade; URL)
CS.20348; Probability Theory, Mathematical Statistics and Empirical Methods of Software Engineering; 5; 98 ; -

4.3 b) Course descriptions (including course code; name; description)
The course "Probability Theory, Mathematical Statistics and Empirical Methods of Software Engineering" includes the following topics:
- fundamentals of combinatorics and probability theory;
- theory of random variables;
- statistical estimates of distribution parameters;
- statistical criteria for hypothesis testing;
- analysis and processing of experimental data;
- parametric and non-parametric statistical criteria;
- correlation and regression analysis;
- ANOVA;
- experiment planning;
- reliability analysis;

4.4 Other courses in Mathematics (including course code; name; local credits; local grade; URL)
CS.18052; Discrete Mathematics and Discrete Structures; 5; 94 ; -

4.4 b) Course descriptions (including course code; name; description)
The course "Computer Discrete Mathematics" includes:
- Boolean algebra (Boolean functions, logical algebra, normal forms);
- Concepts of Graph theory;
- Trees, Transport networks;
- Combinatorial analysis;
- Recurrence analysis;

4.5 Course(s) corresponding to requirement in Programming (including course code; name; local credits; local grade; URL)
CS.9628; Basics of Programming; 5; 98; -
CS.9608; Object-Oriented Programming; 4; 100; -
CS.20400; The C and C++ System Programming Languages; 5; 96; -
CS.18096; Basics of Programming in Java; 5; 96; -

4.5 b) Course descriptions (including course code; name; description)
The course "Basics of Programming" was dedicated to teaching concepts of programming based on JavaScript language.
The contents included common topics like variables, functions, arrays, objects, number systems, paradigms of programming.
And it also included some JavaScript specific topics like interacting with DOM, visualization, current JS standards, etc.


The course "Object-Oriented Programming" included topics dedicated to OOP like classes and objects, encapsulation, inheritance, polymorphism, as well as more in-depth study of C# programming language - virtual methods, collections, events, data binding.


The course "The C and C++ System Programming Languages" was dedicated to low-level programming using C and C++ programming languages. The range of topics included the C/C++ programming languages specifications, STL, GUI-programming with C++, some algorithms and data structures sections.


The course "Basics of Programming in Java" offered both the basics and deep knowledge of Java programming language. Apart from teaching the concepts of Java itself, it also concentrated on topics such as I/O streams, Multi-Threaded programming and build tools such as Maven.

4.6 Course(s) corresponding to requirement in Algorithms and Data Structures (including course code; name; local credits; local grade; URL)
CS.18055; Algorithms and Data Structures; 5; 98; -

4.6 b) Course descriptions (including course code; name; description)
The course " Algorithms and Data Structures" had 2 main parts:
The "Algorithms" part included the following topics:
- sorting algorithms;
- dynamic programming;
- greedy algorithms;
- graph algorithms, including: strongly connected components, spanning trees, traverse, topological sorting, shortest path;
- recursive algorithms;
- the complexity of the algorithm, asymptotic notation;
- analysis of recursive and non-recursive algorithm;
The "Data Structures" part included the following topics:
- Basic data structures, including vector, stack, queue, linked lists;
- Balanced search trees;
- Hash tables;
- Priority queues;
- Graphs;

4.7 Other courses in Computer Science (including course code; name; local credits; local grade; URL)
CS.21314; Data Analyses on the Base of Artificial Intelligence; 5; 100; -
CS.4589; Databases; 5; 100; -
CS.21205; Data Mining; 5; 100; -

4.7 b) Course descriptions (including course code; name; description)
The course "Data Analyses on the Base of Artificial Intelligence" included topics such as:
- areas of data analysis based on artificial intelligence, prospects for their development;
- modern methods of data analysis based on artificial intelligence;
- the basic stages of data analysis based on artificial intelligence;
- principles of work of modern software applications for forming experimental material (research objects), conducting research;
- the basic steps of creating a computer application in Windows OS for research opportunities;
- the basic stages of developing an algorithm for data analysis;


The course "Databases" was dedicated to teaching the concepts of relational databases and how to work with them using SQL.
It included topics like:
- principles of information models and systems design;
- methods of data modeling (conceptual, physical);
- concepts, methods of designing and working with relational databases;
- database query languages;
- basics of physical organization of databases and access technology;
- data architecture and features of the management systems databases;
- transaction processing methods;
- technologies of organization of distributed databases;

The course "Data Mining" included topics:
- cluster analysis;
- correlation analysis;
- associative analysis;
- genetic algorithms for optimization problems.

4.8 Optional other courses, and courses taken outside home university (including course name; description; URL)
Machine Learning, This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition; https://www.coursera.org/learn/machine-learning