Course detail

Computer Science

FSI-1IN-KAcad. 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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Jakub Kůdela, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Harms, D., Macdonald, K.: Začínáme programovat v jazyce Python, Computer Press, 2012. (CS)

Recommended reading

Matthes, E.: Python Crash Course, No Starch Press, 3. vydání, 2022. (EN)
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)

Classification of course in study plans

  • Programme B-STR-K Bachelor's

    specialization STG , 1 year of study, winter semester, compulsory
    specialization AIŘ , 1 year of study, winter semester, compulsory
    specialization SSZ , 1 year of study, winter semester, compulsory

Type of course unit

 

Guided consultation in combined form of studies

17 hod., compulsory

Teacher / Lecturer

doc. Ing. Jakub Kůdela, Ph.D.

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.

Guided consultation

35 hod., optionally

Teacher / Lecturer

doc. Ing. Jakub Kůdela, 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.