Information systems modeling
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
            Title
        
        
            Information systems modeling
        
    
            Code
        
        
            ВК.2.01
        
    
            Module type 
        
        
            Вибіркова дисципліна для ОП
        
    
            Educational cycle
        
        
            Second
        
    
            Year of study when the component is delivered
        
        
            2023/2024
        
    
            Semester/trimester when the component is delivered
        
        
            3 Semester
        
    
            Number of ECTS credits allocated
        
        
            9
        
    
            Learning outcomes
        
        
            PLO11.2. 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. Know materials of standard courses in mathematical analysis, basic concepts from courses in ordinary differential equations, equations of mathematical physics, difference equations and algebra, probability theory and mathematical statistics, and decision-making theories.
2. Be able to build and investigate mathematical models described by dynamic systems of various types. Conduct qualitative research on differential equations on the plane and in three-dimensional space, investigate difference equations, investigate functions and functionals at the extremum, calculate eigenvectors and eigenvalues, find inverse matrices, and solve systems of linear inhomogeneous equations.
3. Have skills in elementary programming, use of mathematical application software packages, numerical and analytical solutions of applied problems, and elementary skills in building models and making decisions under risk and uncertainty.
        
    
            Course content
        
        
            Students will acquire the ability to create mathematical models of dynamic processes, create abstract models of real systems and processes taking into account stochastic disturbances, develop theoretical and practical skills in this area, develop future specialists' competence in the practical application of mathematical models for predicting the behavior of objects of arbitrary nature. The course includes two content modules and two tests. The discipline ends with a test.
        
    
            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 p.
2. Obod I.I., Svyd I.V., Ruban I.V., Zavolodko H.E. Matematychne modeliuvannia informatsiinykh system. Kharkiv, Drukarnia Madryd, 2019. 270 p.
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 p.
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
        
        
            The maximum number of points that can be obtained by a student is 100 points.
1. Test paper No. 1: 35/21 points.
2. Test paper No. 2: 35/21 points.
3. Current assessment: 30/18 points.
Student points are determined as the sum of the grade points for all successfully assessed learning outcomes provided for by this program. The minimum threshold level for the total assessment for all components is 60% of the possible number of points. A student receives an overall positive grade for the discipline if their 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