Course detail
Computer Science
FSI-1IN Acad. year: 2025/2026 Winter semester
The course deals with selected of software modeling tools, which are often used in engineering practice. The variables, commands, data import/export, drawing, procedures and functions are presented and rules of program developing are demonstrated in Python language. Python capabilities are illustrated with examples of simple models of technical systems and technological processes.
Supervisor
Department
Learning outcomes of the course unit
Prerequisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
The maximum achievable score 100b (ECTS). Partial e-tests (6 tests up to 10 points), final test (max. 40 points). For passing the course it is necessary at least 50 points, including at least 20 points from e-tests and 10 points from the final test. Moreover, none of the sub-examples of the final test will have a score below 2 points. Furthermore, successful completion of Python Onramp.
The attendance at lectures is recommended while at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a tutor.
Language of instruction
Czech
Aims
The aim is to acquire the use of computers to solve problems focused to technical systems and processes modeling.
Students will acquire the basic knowledge of modeling technical systems and technological processes. They will gain experience with solving problems using tools of Python. Students will learn the basics of imperative programming.
Specification of controlled education, way of implementation and compensation for absences
The study programmes with the given course
Programme B-FIN-P: Physical Engineering and Nanotechnology, Bachelor's
branch ---: no specialisation, 5 credits, compulsory
Programme B-KSI-P: Mechanical Engineering Design, Bachelor's
branch ---: no specialisation, 5 credits, compulsory
Programme B-PRP-P: Professional Pilot, Bachelor's
branch ---: no specialisation, 5 credits, elective
Programme B-ENE-P: Energy, Bachelor's
branch ---: no specialisation, 5 credits, compulsory
Programme B-STR-P: Engineering, Bachelor's
branch AIŘ: Applied Computer Science and Control, 5 credits, compulsory
Programme C-AKR-P: , Lifelong learning
branch CZS: , 5 credits, elective
Programme B-STR-P: Engineering, Bachelor's
branch KSB: Quality, Reliability and Safety, 5 credits, compulsory
Programme B-ZSI-P: Fundamentals of Mechanical Engineering, Bachelor's
branch MTI: Materials Engineering, 5 credits, compulsory
Programme B-STR-P: Engineering, Bachelor's
branch SSZ: Machine and Equipment Construction, 5 credits, compulsory
Programme B-STR-P: Engineering, Bachelor's
branch STG: Manufacturing Technology, 5 credits, compulsory
Programme B-ZSI-P: Fundamentals of Mechanical Engineering, Bachelor's
branch STI: Fundamentals of Mechanical Engineering, 5 credits, compulsory
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
Ing. Jan Bajer
Ing. Antonín Černý
Mgr. Jan Faltýnek, Ph.D.
Ing. Tomáš Holoubek
Ing. David Ibehej
Ing. Tereza Kůdelová, Ph.D.
Ing. Ondřej Liška
Ing. Petr Lošák, Ph.D.
Ing. Radek Poliščuk, Ph.D.
Ing. Vladimír Skřivánek
Ing. Vojtěch Slabý
Ing. Bc. Kamil Staněk
Ing. Jindřich Šafran
Ing. Petr Šoustek, Ph.D.
Ing. Jan Turčínek, Ph.D.
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.