Infopaket

Computer modeling and data processing in the PYTHON language

ДВС.3.01.03

Вибіркова дисципліна для ОП

First

2023/2024

4 Semester

5

LO 5. Be able to develop and use in practice algorithms related to the approximation of functional dependencies, numerical differentiation and integration, solving systems of algebraic, differential and integral equations, solving boundary value problems, finding optimal solutions. LO 9. Build efficient algorithms regarding the accuracy of calculations, stability, speed and cost of system resources for numerical study of mathematical models and solution of practical problems. LO 11. Be able to apply modern technologies of programming and software development, software implementation of numerical and symbolic algorithms. LO 13. Use specialized software products and software systems of computer mathematics in research. LO 14. Demonstrate the ability to self-study and professional development. LO 18. Communicate effectively on information, ideas, problems and solutions with professionals and society.

Full-time form

To successfully study the discipline "Computer modeling and data processing in the Python language", a student must meet the following requirements: Knowledge: 1. Fundamentals of integral and differential calculus. 2. Methods of solving systems of linear algebraic equations and matrix analysis. 3. Basic linear differential equations of the first and nth orders. 4. Basic principles of procedural and object-oriented programming. Skill: 1. Solve systems of linear algebraic equations. 2. To solve differential equations of the first order and their systems. 3. Investigate functions at the extremum. 4. Use standard Python language libraries. Ownership: 1. Basic programming skills and the use of application software packages for mathematical calculations. 2. Skills in using mathematical apparatus for constructing and analyzing solutions to mathematical problems.

Mastering modern methods of conducting computer experiments and scientific calculations in the Python programming language. Acquaintance of students with modern methods of visualization and data processing using the Python language. The course includes two tests. The discipline ends with an exam.

1. Prohramuvannia chyslovykh metodiv movoiu Python : pidruch. / A. V. Anisimov, A. Yu. Doroshenko, S. D. Pohorilyi, Ya. Yu. Dorohyi; za red. A. V. Anisimova. — K. : Vydavnycho-polihrafichnyi tsentr "Kyivskyi universytet", 2014. — 640 p. 2. Dolya P.G. Vvedenie v nauchnii Python. V 2 chastyakh — Kharkov: Kharkovskii Natsionalnii Universitet, 2016. — 333 p. 3. Unpingco J. Python for Probability, Statistics, and Machine Learning. 2nd Edition. — Springer, 2019. — 384 p. 4. Bashier Eihab B.M. Practical Numerical and Scientific Computing with MATLAB and Python. — CRC Press, 2020. — 345 p. 5. Linge S., Langtangen H.P. Programming for Computations - Python: A Gentle Introduction to Numerical Simulations with Python. — Springer, 2016. — 244 p. 6. Pichkur V.V., Kapustian O.V., Sobchuk V.V. Teoriia dynamichnykh system. — Lutsk: Vezha-Druk, 2020. — 348 p.

Lectures, practical classes, independent work.

Semester evaluation: The maximum number of points that can be obtained by a student is 60 points: 1. Control work No 1: 20/12 points. 2. Control work No 2: 20/12 points. 3. Homework, current assessment: 20/12 points. Final evaluation (in the form of an exam): The maximum number of points that can be obtained by a student is 40 points. Form of conduct: written work. Types of tasks: 5 written tasks (2 theoretical questions and 2 practical tasks). The student receives an overall positive grade in the discipline if his grade for the exam is at least 24 (twenty-four) points. A student is admitted to the exam if he has scored at least 36 points in total during the semester; and completed and passed 2 (two) test papers on time.

Ukrainian