Applied mathematics

Faculty of Computer Science and Cybernetics

Name
Applied mathematics
Program code
Qualification awarded
Master of Applied Mathematics
Length of programme
2 years
Number of credits
120
level of qualification according to the National Qualification Framework and the European Qualifications Framework
7
Qualification level
Second (Master)
Discipline
Mathematics and statistics
Speciality
KnowledgeField EN
Specific admission requirements
Bachelor's degree
Specific arrangements for recognition of prior learning
On the basis of entrance examinations conducted in the form of:
• a single entrance exam in a foreign language in the form of a test; • professional entrance examination conducted by the University.
Qualification requirements and regulations, including graduation requirements
After defending the master's thesis and a comprehensive exam in applied mathematics, the qualification is awarded: "Master of Applied Mathematics". Additional qualification "Junior Researcher (Computer Systems)", "Applied Programmer" (grades: 1. DVVS> = 75 b; 2. Internships> = 75 b; 3. Defense of master's qualification work> = 75 b.) .
Programme learning outcomes
PLO1. Be able to use of in-depth professional knowledge and practical skills to optimize the design of models of any complexity, to solve specific problems of designing intelligent information systems of different physical nature. PLO2. Understanding of the principles and methods of analysis and evaluation of the range of tasks that contribute to the further development of effective use of information resources. PLO3. Gaining knowledge for the ability to evaluate existing technologies and on the basis of analysis to form requirements for the development of advanced information technologies. PLO4. Be able to determine the type of data integration required for a task. PLO5. Be able to carry out effective communicative activities of the project development team. PLO6. Be able to design and use existing data integration tools, process data stored in different systems. PLO7. Be able to organize, configure and develop Web-systems using the principles of distributed systems, hypertext systems, appropriate hardware and software. PLO8. Communicate effectively on information, ideas, problems and solutions with professionals and society at large. PLO9. Collect and interpret relevant data and analyze complexities within their specialization to make judgments that reflect relevant social and ethical issues. PLO10. Be able to build models of physical and production processes, design of storage and data space, knowledge base, using charting techniques and standards for information systems development. http://csc.knu.ua/uk/filer/canonical/1642859065/1717/
Form of study
Full-time form
Examination regulations and grading scale
Meet the requirements of the "Regulations on the organization of the educational process at the Taras Shevchenko National University of Kyiv." http://nmc.univ.kiev.ua/docs/poloz_org_osv_proc-2018.pdf
Оbligatory or optional mobility windows (if applicable)
Carried out in accordance with agreements on international cooperation and coordination in the field of education and science. In particular, according to the Agreement on Double Diplomacy between Kyiv National University. Taras Shevchenko and Universita Degli Studi de L'Aquila (Aquila, Italy).
Work placement
Industrial practice can be conducted both on the basis of the graduating department and on the basis of enterprises, organizations, research institutes, banks, insurance companies and other institutions engaged in the design, development, implementation and operation of automated information systems. The choice of practice bases is carried out in coordination with the graduating department, taking into account the tasks of practice and the possibility of their implementation. In particular, the following organizations can act as bases of production practice: Samsung, GlobalLogic, Avora, EPAM, SoftServ, InfoPulse, DataArt and others.
Work-based learning
Director of the course
Ihor V. Samoilenko
Operations Research
Faculty of Computer Science and Cybernetics
Occupational profiles of graduates
Professional activity as a specialist in the development of mathematical methods, models, algorithms and software designed for research, analysis, design of processes and systems in various specific subject areas.
Access to further studies
Obtaining education under the educational program of the third (educational-scientific) level of higher education and obtaining additional qualifications in the adult education system.

Subjects

As part of the curriculum, students study the following disciplines

Numerical modeling technologies
Code: ННД.07,
Computer-analytical modeling
Code: ДВВ.05,
Project management
Code: ННД.06,
Professional and Corporate Ethics
Code: ,
Modern problems of applied mathematics
Code: ,
Mathematical models of cybernetics
Code: ,
Preparation of the master's thesis
Code: ,
INTERNSHIP "Methods of modeling and optimization of complex systems"
Code: ,
Information processing and analysis technologies
Code: ,
Nonclassical problems of mathematical physics
Code: ,
Complementary chapters of functional analysis/ Module 1. Applied functional analysis.
Code: ,
Pattern Recognition.
Code: ,
Modern problems of computational mathematics
Code: ,
Actual problems of applied mathematics
Code: ,
Theory of optimization in functional spaces
Code: ,
Numerical modeling of system dynamics.
Code: ,
Advanced functional analysis. Module 1. Applied functional analysis. Module 2. Convex and nonlinear
Code: ,
Methods of analysis of operator systems
Code: ,
Nonclassical optimal control problems
Code: ,
Operator algebras and quantum information theory
Code: ,
Code: ,
Methods of artificial intelligence
Code: ДВС.3.02.01,
Fundamentals of artificial intelligence
Code: ННД.04,
Set-valued analysis
Code: ДВВ.01.01,
Dynamic system modeling
Code: ННД.05,
Modeling of information systems
Code: ДВС.2.01,
Problems of non-classic optimization
Code: ННД.13,
Information processing technologies
Code: ДВС.2.02,
Title Basics of nonlinear dynamics
Code: ДВВ.02,
Adaptive information processing and recognition
Code: ДВС.2.03,
Methods of non-smooth optimization
Code: ДВВ.06,