Modeling of information systems
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Modeling of information systems
        
    
            Code
        
        
            ДВС.2.01
        
    
            Module type 
        
        
            Вибіркова дисципліна для ОП
        
    
            Educational cycle
        
        
            Second
        
    
            Year of study when the component is delivered
        
        
            2021/2022
        
    
            Semester/trimester when the component is delivered
        
        
            3 Semester
        
    
            Number of ECTS credits allocated
        
        
            8
        
    
            Learning outcomes
        
        
            PLO 11.2. Understand the main areas of applied mathematics and computer science, to the extent necessary for the development of general professional mathematical disciplines, applied disciplines and the use of their methods in the chosen profession. PLO13.2. Be able to independently analyze the subject area and develop mathematical and structural-algorithmic models.
        
    
            Form of study
        
        
            Full-time form
        
    
            Prerequisites and co-requisites
        
        
            1. To know materials of standard courses of mathematical analysis, basic concepts from courses of ordinary differential equations, equations of mathematical physics, difference equations and algebra, probability theory, mathematical statistics, and theory of decision-making. 2. Be able to build and research mathematical models described by dynamic systems of various types. Conduct a qualitative study of differential equations on the plane and in three-dimensional space, investigate differential equations, investigate functions and functionals at the extremum, calculate eigenvectors and eigenvalues, find inverse matrices, and solve systems of linear inhomogeneous equations. 3. Possess the skills of elementary programming, use of mathematical packages of application programs, numerical and analytical solving of applied problems, elementary skills of building models, and making decisions in conditions of risk and uncertainty.
        
    
            Course content
        
        
            Students master the ability to make mathematical models of dynamic processes, create abstract models of real systems and processes taking into account stochastic disturbances, develop theoretical and practical abilities in this direction; form future specialists' competence in the practical application of mathematical models for forecasting the behavior of objects of arbitrary nature.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1. Matsenko V.H. Matematychne modeliuvannia dynamiky vikovoi struktury. – Chernivtsi, Chernivetskyi nats. un.-t im. Yu. Fedkovycha, 2018. – 191 s.
2. Obod I.I., Svyd I.V., Ruban I.V., Zavolodko H.E. Matematychne modeliuvannia informatsiinykh system. – Kharkiv, Drukarnia Madryd, 2019. – 270 s.
3. Tomasz R Beiletcki, Marek Rutkowski. Credit risk: modeling, valuation and Hedging.
4. Khusainov D.Ya., Kharchenko I.I., Shatyrko A.V. Modeliuvannia dynamichnykh system. – Navchalnyi posibnyk VPTs Kyivskoho universytetu, 2011. – 135 s.
5. Dahlman O., Israelson H. Monitoring Underground Nuclear Explosions. – Amsterdam-Oxford-New York, 1977. 440 p.
6. Melton B.S., Kirkpatrick B.M. The symmetrical triaxial seismometer-Its design for application to long period seismometry. / Bull. Seism. Soc. Amer., 60, 1970. Р. 717-740.
7. Marshall P.D., Burch R.F., Douglas A. How and why to record broad band seismic signals. / Nature, 239, 1972. Р.154-155.
        
    
            Planned learning activities and teaching methods
        
        
            Lectures, independent work
        
    
            Assessment methods and criteria
        
        
            Semester assessment:
The maximum number of points that can be obtained by a student is 100 points.
1. Control work No. 1: RN 1.1, RN 2.1 – 35/21 points.
2. Control work No. 2: RN 1.2, RN 2.2 – 35/21 points.
3. Current evaluation: RN 1.1, RN 1.2, RN 2.1, RN 2.2, RN 3.1, RN 4.1, RN 4.2 – 30/18 points.
Final assessment in the form of credit:
Passing points are defined as the sum of evaluation points for all successfully assessed learning outcomes provided in this program. The minimum threshold level for the total score for all components is 60% of the possible number of points. The student receives an overall positive grade in the discipline if his grade for the semester is at least 60 points.
        
    
            Language of instruction
        
        
            Ukrainian
        
    Lecturers
This discipline is taught by the following teachers
                    Denys
                    Yakhievych
                    Khusainov
                
                
                    Complex systems modelling 
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
                    Vasyl
                    Vasylovych
                    Begun
                
                
                    Complex systems modelling 
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
                    Vasyl
                    Serhiiovych
                    Mostovyi
                
                
                    Complex systems modelling 
Faculty of Computer Science and Cybernetics
            Faculty of Computer Science and Cybernetics
Departments
The following departments are involved in teaching the above discipline
                        Complex systems modelling
                    
                    
                        Faculty of Computer Science and Cybernetics
                    
                
                        Complex systems modelling
                    
                    
                        Faculty of Computer Science and Cybernetics
                    
                
                        Complex systems modelling
                    
                    
                        Faculty of Computer Science and Cybernetics