Course detail
Computer Science
FSI-1INAcad. year: 2025/2026
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
Number of ECTS credits
Mode of study
Guarantor
Entry knowledge
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.
Study aids
Prerequisites and corequisites
Basic literature
Recommended reading
Scientific Python Lectures [on-line 01.07.2025: https://lectures.scientific-python.org/] (EN)
Sedgewick, R., Wayne, K.: Algorithms, Addison-Wesley, 4. vydání, 2016. (EN)
Wengrow, J.: A Common-sense Guide to Data Structures and Algorithms, Pragmatic Bookshelf, 2. vydání, 2020. (EN)
Wirth, N.: Algorithms and Data Structures, Prentice Hall, 1985. (EN)
Elearning
Classification of course in study plans
- Programme B-ENE-P Bachelor's 1 year of study, winter semester, compulsory
- Programme B-FIN-P Bachelor's 1 year of study, winter semester, compulsory
- Programme B-KSI-P Bachelor's 1 year of study, winter semester, compulsory
- Programme B-PRP-P Bachelor's 1 year of study, winter semester, elective
- Programme B-VTE-P Bachelor's 1 year of study, winter semester, compulsory
- Programme B-ZSI-P Bachelor's
specialization STI , 1 year of study, winter semester, compulsory
specialization MTI , 1 year of study, winter semester, compulsory - Programme B-STR-P Bachelor's
specialization AIŘ , 1 year of study, winter semester, compulsory
specialization KSB , 1 year of study, winter semester, compulsory
specialization SSZ , 1 year of study, winter semester, compulsory
specialization STG , 1 year of study, winter semester, compulsory - Programme C-AKR-P Lifelong learning
specialization CZS , 1 year of study, winter semester, elective
Type of course unit
Lecture
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
Teacher / Lecturer
Ing. Jan Bajer
Ing. Vojtěch Slabý
Ing. Radek Poliščuk, Ph.D.
Mgr. Jan Faltýnek, Ph.D.
Ing. Jindřich Šafran
Ing. Jan Turčínek, Ph.D.
Ing. Petr Šoustek, Ph.D.
Ing. Petr Lošák, Ph.D.
Ing. Tomáš Holoubek
Ing. David Ibehej
Ing. Bc. Kamil Staněk
Ing. Ondřej Liška
Ing. Antonín Černý
Ing. Tereza Kůdelová, 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.
Elearning