Course detail
Computer Science
FSI-1IN-A Acad. year: 2025/2026 Winter semester
The course deals with the development of computational thinking and selected software tools for modeling and data processing in engineering applications, which are often used in technical practice. Variables, commands, control structures, functions, data import/export, plotting are presented using the Python language, and the principles of program creation are demonstrated. The capabilities of the Python language are illustrated with examples of models of simple engineering applications.
Language of instruction
English
Number of ECTS credits
5
Supervisor
Department
Entry knowledge
The usual secondary school computer literacy is supposed.
Rules for evaluation and completion of the course
Maximum points earned 100b (ECTS), divided into 3 continuous tests (total 50b) and one final test (50b). To successfully complete the course, at least 50b in total and at least 25b from the final test are required. Attendance at lectures is desirable, at seminars is mandatory. Teaching is carried out according to weekly plans. The method of making up for missed seminars is fully within the competence of the teacher.
Aims
The goal is to master the use of computing technology in solving tasks oriented towards modeling problems in engineering applications. Students will gain experience in solving problems using the Python language. Students will learn the basics of imperative programming.
The study programmes with the given course
Programme B-STI-A: Fundamentals of Mechanical Engineering, Bachelor's, compulsory
Programme B-STI-Z: Fundamentals of Mechanical Engineering, Bachelor's, recommended course
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
1. Introduction to computer science and Python.
2. System modeling, problem analysis.
3. Basic data types, operations and functions.
4. Control structures.
5. Variables and composite data types.
6. Algorithmization.
7. Numpy, Scipy, matrix operations.
8. Matplotlib, visualization.
9. Recursion.
10. Working with files.
11. Testing, debugging, exceptions, prompting.
12. Symbolic and numerical calculations (SymPy).
13. Current trends, final summary and discussion.
Computer-assisted exercise
26 hours, compulsory
Teacher / Lecturer
Syllabus
1. Python language, simple expressions.
2. Operators and variables.
3. Functions.
4. Control structures I.
5. Control structures II.
6. Variables and composite data types.
7. Algorithmization.
8. Numpy, Scipy, matrix operations.
9. Matplotlib, visualization.
10. Recursion.
11. Working with files.
12. Final test.
13. Credit.